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William McKnight
President at McKnight Consulting Group
Plano, United States
William McKnight is an internationally recognized authority in information management. His consulting work has included many of the Global 2000 and numerous midmarket companies. His clients have reaped tremendous ROI and turned data into a real corporate asset. Many have gone public with their success stories.
William is the #1 global influencer in master data management, #1 in data warehousing, #3 in data management, #7 in information management and #14 in information architecture.
He is president of McKnight Consulting Group, which provides clients with action plans, architectures, strategies, complete programs and vendor-neutral tool selection to manage information. MCG is #1001 on the 2018 Inc. 5000 list of the fastest-growing companies in the US and #743 on the 2017 list.
He is the author of the books "Integrating Hadoop", "Information Management: Strategies for Gaining a Competitive Advantage with Data"? and "90 Days to Success in Consulting".
William teaches Data Platforms, Data Maturity, NoSQL, Graph Databases, Business Intelligence, Data Quality, Project Management, Data Governance, Data Architecture, Data Modeling, Data Integration, Data Return on Investment, Agile Methodology, Big Data, Organizational Change Management and Master Data Management for the Data Warehousing Institute (since 1998) and other events globally. He is a frequent international keynote speaker.
William has hundreds of articles and 50+ white papers in publication and is a prolific sponsored blogger.
An Ernst&Young Entrepreneur of the Year Finalist and frequent best practices judge, William is a former Fortune 50 technology executive and database engineer. William has taught at Santa Clara University, UC-Berkeley and UC-Santa Cruz.
He has consulted in 14 countries.
Current Services Offered, Presentation Calendar, White Papers, Articles and Press Quotes can be found at mcknightcg .com.
Procurement Efficiency with the Microsoft Commercial Marketplace
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March 27, 2023
The rapidly growing adoption of public clouds by IT organizations is frequently motivated by a desire to be more adaptable, agile, and
The post Procurement Efficiency with the Microsoft Commercial Marketplace appeared first on Gigaom.
New Microsoft Teams Performance Benchmark
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March 27, 2023
This GigaOm Benchmark report was commissioned by Microsoft. Microsoft Teams (Teams) is a collaboration platform that combines workplace chat, video meetings, file
The post New Microsoft Teams Performance Benchmark appeared first on Gigaom.
Security Information and Event Management: A MITRE ATT&CK Framework Competitive Evaluation
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January 25, 2023
Security information and event management (SIEM) technology supports threat detection, compliance, and security incident management through the collection and analysis (near real-time
The post Security Information and Event Management: A MITRE ATT&CK Framework Competitive Evaluation appeared fi
SQL Transaction Processing and Analytic Performance Price-Performance Testing
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January 25, 2023
The fundamental underpinning of any organization is its transactions. They must be done well, with integrity and performance. Not only has transaction
The post SQL Transaction Processing and Analytic Performance Price-Performance Testing appeared first on Gigaom.
ABAC vs RBAC: The Advantage of Attribute-Based Access Control over Role-Based Access Control
GigaOm
January 01, 2023
Data security has become an undeniable part of the technology stack for modern applications. No longer an afterthought, protecting application assets—namely data—against cybercriminal activities, insider threats, and basic human negligence needs to happen early and often during the application development cycle and beyond. This benchmark report captures the number of policy changes required to manage ever-evolving data security policies seen in a modern data-driven enterprise. The more policy changes required, the more likely a required change will not take place or an error is made when implementing the change. With this study, we show the impacts of data security governance policy management.
CrowdStrike Falcon LogScale Benchmark Report: Log Management and Analytics Platform
GigaOm
January 01, 2023
Real-time observability and enterprise systems monitoring have become critical functions in information technology organizations globally. As organizations continue to digitize and automate key functions, they are introducing more complex systems, hypervisors, virtual machines, Kubernetes, devices, and applications—all of which are generating more log and event data. While the amount of usable log data is growing, there is not an attendant growth in the tools, skilled professionals, and other resources to capture, manage, and analyze this complexity.
Advantages of DataStax Astra Streaming for JMS Applications
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December 22, 2022
Competitive markets demand rapid, well-informed decision-making to succeed. In response, enterprises are building fast and scalable data infrastructures to fuel time-sensitive decisions,
The post Advantages of DataStax Astra Streaming for JMS Applications appeared first on Gigaom.
Confluent Cloud: Fully Managed Kafka Streaming, An Ease-of-Use Comparison
GigaOm
December 13, 2022
This report focuses on real-time data and how autonomous systems can be fed at scale reliably. To shed light on this challenge, we assess the ease of use of a fully managed Kafka platform—Confluent Cloud—and a self-managed open-source Apache Kafka solution.
Cloud Parallel File Systems
GigaOm
December 13, 2022
We benchmarked the usability, effort, and performance of the WEKA Data Platform against Amazon FSx for Lustre on AWS. In this hands-on benchmark, we found that WEKA provided comparable or superior usability and outperformed FSx for Lustre at similar capacities by up to 300% or more. On some of our tests, WekaFS IO latency was less than 30% that of FSx for Lustre. Our usability tests also found WEKA to be a mature and easily deployed and operated solution in AWS specifically.
Dealing with Data System Complexity in Your Applications
GigaOm
December 13, 2022
In conventional information architectures, enterprise needs requires two different database technologies: online transactional processing (OLTP) database management systems (DBMS) to handle transactional workloads and online analytical processing (OLAP) DBMS to perform analytics and reporting. Data types also drive multiple technologies since many databases specialize in types like time series, geospatial, graph, JSON, etc. If there is a single database that can be used to avoid the overhead, it is worthwhile to look into that database for complete application management.
Advantages of DataStax Astra Streaming for JMS Applications
GigaOm
December 13, 2022
Competitive markets demand rapid, well-informed decision-making to succeed. In response, enterprises are building fast and scalable data infrastructures to fuel time-sensitive decisions, provide rich customer experiences enable better business efficiencies, and gain a competitive edge. In our comparative study, we used the Starlight for JMS feature included in DataStax Astra Streaming along with self-managed open-source Apache ActiveMQ Artemis JMS instances. We found several notable differences and benefits for modernizing a JMS-based data streaming stack.
API and Microservices Management Benchmark: Product Evaluation: Kong Enterprise, Apigee X and MuleSoft Anypoint
GigaOm
December 13, 2022
Application programming interfaces, or APIs, are a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations encompass a vast array of applications and systems, many of which have turned to APIs for exchanging data as the glue that holds these heterogeneous artifacts together. APIs have begun to replace older, more cumbersome methods of information sharing with lightweight, loosely-coupled microservices. This change allows organizations to knit together disparate systems and applications without creating technical debt from tight coupling with custom code or proprietary, unwieldy vendor tools.
This report reveals the results of performance testing we completed on these API and microservices management platforms: Kong Enterprise, Google Cloud Apigee X, and MuleSoft Anypoint Flex Gateway.
Transactional and Analytical Workloads: How Transactional and Analytical Performance Impacts the TCO of Cloud Databases
GigaOm
December 13, 2022
Competitive data-driven organizations rely on data-intensive applications to win in the digital service economy. These applications require a robust data tier that can handle the diverse workloads demands of both transactional and analytical processing while serving an interactive, immersive customer experience. The resulting database workloads demand low-latency responses, fast streaming data ingestion, complex analytic queries, high concurrency, and large data volumes.
This report outlines the results from a Field Test derived from three industry standard benchmarks—TPC Benchmark H (TPC-H), TPC Benchmark DS (TPC-DS), and TPC Benchmark C (TPC-C)—to compare SingleStoreDB, Amazon Redshift, and Snowflake.
ABAC vs RBAC: The Advantage of Attribute-Based Access Control over Role-Based Access Control
Import from wordpress feed
December 07, 2022
Data security has become an undeniable part of the technology stack for modern applications. No longer an afterthought, protecting application assets—namely data—against
The post ABAC vs RBAC: The Advantage of Attribute-Based Access Control over Role-Based Access Control appeared first on Gigaom
CrowdStrike Falcon LogScale Benchmark Report
Import from wordpress feed
December 06, 2022
Real-time observability and enterprise systems monitoring have become critical functions in information technology organizations globally. As organizations continue to digitize and automate
The post CrowdStrike Falcon LogScale Benchmark Report appeared first on Gigaom.
Cloud Analytics Platform Total Cost of Ownership v2.0
GigaOm
December 02, 2022
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. We decided to take four leading platforms for machine learning under analysis. We have learned that the cloud analytic framework selected for an enterprise and an enterprise project matters in terms of cost.
Log and Telemetry Analytics Performance Benchmark
GigaOm
December 01, 2022
This report focuses on the performance of cloud-enabled, enterprise-ready, popular log analytical platforms Microsoft Azure Data Explorer (part of Azure Synapse Analytics), Google BigQuery, and Snowflake. Due to cost limitations with Elasticsearch and AWS OpenSearch, we could not run our tests on Elasticsearch. Microsoft invited GigaOm to measure the performance of the Azure Data Explorer engine and compare it with its leading competitors in the log analytics space. The tests we designed intend to simulate a set of basic scenarios to answer fundamental business questions that an organization from nearly any industry might encounter in their log analytics.
In this report, we tested complex workloads with a volume of 100TB of data and concurrency of 1 and 50 concurrent users. The testing was conducted using comparable hardware configurations on Microsoft Azure and Google Cloud.
Managing Microsoft Azure Arc-Enabled Infrastructure from the Azure Portal
GigaOm
December 01, 2022
An Azure Arc-enabled infrastructure is a cloud infrastructure that is managed and monitored by Microsoft Azure. It includes features such as Azure Resource Manager, Azure Monitor, and Azure Security Center. The Azure portal is a web-based management tool that provides a unified experience for managing all Azure resources. The Azure portal allows you to create, manage, and monitor Azure resources in a single, unified console. Many are managing their Microsoft Azure Arc-enabled infrastructure from an Azure portal.
High-Performance Web Application Firewall Testing
GigaOm
December 01, 2022
This report focuses on web application security mechanisms deployed in the cloud and closer to your apps. The cloud enables enterprises to rapidly differentiate and innovate with microservices and allows microservice endpoints to be cloned and scaled in a matter of minutes. It reviews F5 NGINX App Protect WAF vs. AWS WAF, Azure Web Application Firewall, and Cloudflare WAF.
The Data Warehouse in Multi-Cloud and Hybrid Cloud
GigaOm
December 01, 2022
Your Analytical Database Deployment will probably be to Multiple Clouds. Learn about the Role of the Data Warehouse in a World with Data Lakes, Data Science and Decentralization, Options for Provisioning the Data Warehouse and Why Multiple Clouds, Cloudwashing – Cloud-Enabled/Hosted vs Cloud-Native vs Cloud-Owned and Multi-Cloud Flexibility and Deployment Freedom.
Cloud Analytics Platform Total Cost of Ownership
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November 23, 2022
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning.
The post Cloud Analytics Platform Total Cost of Ownership appeared first on Gigaom.
Cloud Parallel File Systems
Import from wordpress feed
November 21, 2022
The latest organizational initiatives involving artificial intelligence (AI), machine learning (ML), high performance computing (HPC), and containerized application spaces necessitate a robust
The post Cloud Parallel File Systems appeared first on Gigaom.
API and Microservices Management Benchmark
GigaOm
July 05, 2022
Application programming interfaces, or APIs, are a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations encompass a vast array of applications and systems, many of which have turned to APIs for exchanging data as the glue that holds these heterogeneous artifacts together. APIs have begun to replace older, more cumbersome methods of information sharing with lightweight, loosely-coupled microservices.
In this report, we reveal the results of performance testing we completed on two API and Microservices Management platforms: Kong Enterprise and Google Cloud Apigee X.
Healthcare Natural Language Processing
GigaOm
June 22, 2022
Our study examined three public cloud service offerings that use natural language processing to meet the challenge—Google Cloud Healthcare API, Amazon Comprehend Medical, and Microsoft Azure Text Analytics for Health. We manually annotated medical notes to identify terms within the documents from a common set of entities and relationships. Next, we built an annotation taxonomy by comparing the taxonomies of the three NLP solutions and created a standard mapping of the entities and relationships shared by all three platforms. We then compared our annotations to the annotations of each solution, using the annotation taxonomy, and noted false negatives (not desired), true positives (desired), and false positives (not desired).
A report by McKnight Consulting Group used industry-standard benchmarks to test three well-known, cloud-optimized analytical platforms – Vertica in Eon Mode, Amazon Redshift, and Snowflake.
SECTOR ROADMAP: MODERN ENTERPRISE GRADE DATA INTEGRATION
GigaOm
June 15, 2022
This Sector Roadmap is focused on data integration (DI) selection for multiple/general purposes across the enterprise.
Vendor solutions are evaluated over six Disruption Vectors: SaaS Applications Connectivity, Use of Artificial Intelligence, Conversion from any format to any format, Intuitive and Programming Time Efficient, Strength in DevOps and Shared Metadata across data platforms.
CASSANDRA TOTAL COST OF OWNERSHIP STUDY
GigaOm
April 06, 2022
This study examines the full cost and true value of Cassandra self-managed on Google Cloud (GCP) and the cost of a fully managed serverless Cassandra service. We included dedicated compute hardware (for self-managed Cassandra), cost per read and write operation (on serverless Cassandra), storage growth (each write operation adds new data) and people cost in our three-year total cost of ownership calculations. People costs take into account that certain capabilities in serverless Cassandra needed for the workload were not available in self-managed Cassandra, requiring workarounds. We used market rates and typical splits of full-time equivalent (FTE) and consulting to determine our people costs.
Enterprise Analytic Solutions 2021
GigaOm
December 03, 2021
In this paper, we focus on the higher-volume, critical-app compute and storage that is the analytic database. We have undertaken the ambitious goal of evaluating the current vendor landscape and assessing the analytic platforms that have made, or are in the process of making, the leap to a new generation of capabilities in order to support the AI-based enterprise.
For this Roadmap Report, we chose technologies powered for an enterprise-class application in a midsize to large enterprise. We considered popularity and interest. The vendors/products chosen were:
Actian Avalanche
Amazon Redshift
Cloudera Data Platform
Google BigQuery
IBM Db2 Warehouse on Cloud and Cloud Pak for Data
Microsoft Azure Synapse Analytics
Oracle Autonomous Data Warehouse
Snowflake
Teradata Vantage
Vertica
Yellowbrick
RADAR for Master Data Management
GigaOm
December 03, 2021
The economic payback of master data management starts with “build once, use often.” Master data must be accessible to each new application that is built, and these applications routinely have up to 50% effort and budget directed toward collecting master data.
This GigaOm Radar report evaluates the capabilities of notable players in the MDM space against the decision-making framework established in the Key Criteria Report for Master Data Management.
SQL TRANSACTION PROCESSING, PRICE-PERFORMANCE TESTING, MICROSOFT SQL SERVER EVALUATION: AZURE VIRTUAL MACHINES VS. AMAZON WEB SERVICES EC2
GigaOm
November 22, 2021
This report outlines the results from a GigaOm Transactional Field Test, derived from the industry-standard TPC Benchmark E (TPC-E), to compare two IaaS cloud database offerings:
Microsoft SQL Server on Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances
Microsoft SQL Server on Microsoft Azure Virtual Machines (VM)
CLOUD DATA SECURITY COMPARISON: IMMUTA AND APACHE RANGER
GigaOm
November 21, 2021
Data security has become an immutable part of the technology stack for modern applications. Protecting application assets and data against cybercriminal activities, insider threats, and basic human negligence is no longer an afterthought. It must be addressed early and often, both in the application development cycle and the data analytics stack.
To measure the policy management burden, we designed a reproducible test that included a standardized, publicly available dataset and a number of access control policy management scenarios based on real world use cases we have observed for cloud data workloads. We tested two options: Apache Ranger with Apache Atlas and Immuta.
Choosing the Right Data Warehouse-as-a-Service for Your Analytical Needs
MCG
November 16, 2021
This paper examines the different flavors of DWaaS to ensure you get into the right one. Then it looks at some of the key criteria that should be considered when reviewing the cloud database.
Behind the covers of the DWaaS term, there are three distinct approaches. While all include most of the benefits for DWaaS, the differences mean that the benefits will accrue quite differently according to the fit of the model to the enterprise. These are vast enough differences to actually be the deciding factor in the DWaaS selection.
Embedded Database Performance Report
MCG
November 10, 2021
This benchmark compared Actian Zen Enterprise Server and MySQL Enterprise, both running on the same Ubuntu Linux in 8- and 16-core VMs as AWS EC2 instances, each using its ODBC driver. The benchmark used is derived from the TPC-C industry standard benchmark.
SQL TRANSACTION PROCESSING, PRICE-PERFORMANCE TESTING, MICROSOFT SQL SERVER EVALUATION: AZURE VIRTUAL MACHINES VS. AMAZON WEB SERVICES EC2
GigaOm
October 20, 2021
This report outlines the results from a GigaOm Transactional Field Test, derived from the industry-standard TPC Benchmark E (TPC-E), to compare two IaaS cloud database offerings:
Microsoft SQL Server on Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances
Microsoft SQL Server on Microsoft Azure Virtual Machines (VM)
ENTERPRISE READINESS OF CLOUD MLOPS
GigaOm
October 14, 2021
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
For the analysis, we used categories of Total Cost of Ownership (TCO) time-to-value and enterprise capabilities. Our assessment resulted in a score of 2.9 (out of 3) for Azure ML using managed endpoints, 1.9 for Google Vertex AI, and TK for AWS SageMaker. The assessment and scoring rubric and methodology are detailed in an annex to this report.
CLOUD DATA SECURITY COMPARISON: IMMUTA AND APACHE RANGER
GigaOm
October 13, 2021
Data security has become an immutable part of the technology stack for modern applications. Protecting application assets and data against cybercriminal activities, insider threats, and basic human negligence is no longer an afterthought. It must be addressed early and often, both in the application development cycle and the data analytics stack.
To measure the policy management burden, we designed a reproducible test that included a standardized, publicly available dataset and a number of access control policy management scenarios based on real world use cases we have observed for cloud data workloads. We tested two options: Apache Ranger with Apache Atlas and Immuta.
Embedded Database Performance Report
MCG
October 13, 2021
This benchmark compared Actian Zen Enterprise Server and MySQL Enterprise, both running on the same Ubuntu Linux in 8- and 16-core VMs as AWS EC2 instances, each using its ODBC driver. The benchmark used is derived from the TPC-C industry standard benchmark.
Choosing the Right Data Warehouse-as-a-Service for Your Analytical Needs
GigaOm
October 06, 2021
This paper examines the different flavors of DWaaS to ensure you get into the right one. Then it looks at some of the key criteria that should be considered when reviewing the cloud database.
Behind the covers of the DWaaS term, there are three distinct approaches. While all include most of the benefits for DWaaS, the differences mean that the benefits will accrue quite differently according to the fit of the model to the enterprise. These are vast enough differences to actually be the deciding factor in the DWaaS selection.
Enterprise Analytic Solutions 2021
GigaOm
October 06, 2021
In this paper, we focus on the higher-volume, critical-app compute and storage that is the analytic database. We have undertaken the ambitious goal of evaluating the current vendor landscape and assessing the analytic platforms that have made, or are in the process of making, the leap to a new generation of capabilities in order to support the AI-based enterprise.
For this Roadmap Report, we chose technologies powered for an enterprise-class application in a midsize to large enterprise. We considered popularity and interest. The vendors/products chosen were:
Actian Avalanche
Amazon Redshift
Cloudera Data Platform
Google BigQuery
IBM Db2 Warehouse on Cloud and Cloud Pak for Data
Microsoft Azure Synapse Analytics
Oracle Autonomous Data Warehouse
Snowflake
Teradata Vantage
Vertica
Yellowbrick
ENTERPRISE READINESS OF CLOUD MLOPS
GigaOm
September 01, 2021
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
For the analysis, we used categories of Total Cost of Ownership (TCO) time-to-value and enterprise capabilities. Our assessment resulted in a score of 2.9 (out of 3) for Azure ML using managed endpoints, 1.9 for Google Vertex AI, and TK for AWS SageMaker. The assessment and scoring rubric and methodology are detailed in an annex to this report.
CLOUD DATA WAREHOUSE PERFORMANCE TESTING: CLOUDERA DATA WAREHOUSE, AMAZON REDSHIFT, MICROSOFT AZURE SYNAPSE, GOOGLE BIGQUERY, AND SNOWFLAKE
GigaOm
August 10, 2021
This report outlines the results from an analytic performance test derived from the industry-standard TPC Benchmark DS (TPC-DS) to compare Cloudera Data Warehouse service (CDW)—part of the broader Cloudera Data Platform (CDP)—with four prominent competitors: Amazon Redshift, Azure Synapse Analytics, Google BigQuery, and Snowflake. Overall, the test results were insightful in revealing query execution performance of these platforms.
BENCHMARK REPORT: TRILLION EDGE KNOWLEDGE GRAPH
MCG
July 06, 2021
Our latest benchmark report, Trillion Edge Knowledge Graph, is the first demonstration of a massive knowledge graph that consists of materialized data and Virtual Graphs spanning hybrid multicloud data sources.
Extra Credit v MongoDB: How the U.K.’s Department of Work and Pensions Scaled Universal Credit to Meet the COVID-19 Crisis
GigaOm
June 10, 2021
The COVID-19 pandemic and subsequent shutdowns posed a direct and unique challenge to the United Kingdom’s Department for Work and Pensions, as the number of active claimants spiked from more than 2 million just before the pandemic to more than 5 million in the span of a couple months. Learn how the DPW leveraged MongoDB to scale its microservices-based infrastructure to protect millions of citizens during a time of crisis.
Cloud Analytics Platform Total Cost of Ownership
GigaOm
June 10, 2021
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a selection that allows a worry-less experience with the architecture and its components.
We decided to take four leading platforms – Azure, AWS, GCP and Snowflake – for machine learning under analysis. We have learned that the cloud analytic framework selected for an enterprise, and for an enterprise project, matters to cost.
A Report on the Cost Savings of Replacing Kafka with Pulsar
GigaOm
June 02, 2021
Picking the wrong event streaming platform for your organization can have massive consequences in terms of fit, function and of course cost. With Apache Pulsar quickly gaining mindshare within enterprises that need a comprehensive, open source event streaming and messaging platform, the expert researchers at GigaOm decided to see how this new, up and coming technology compares to the old industry stalwart: Apache Kafka.
HIGH PERFORMANCE API MANAGEMENT TESTING: PRODUCT EVALUATION: API7 AND KONG ENTERPRISE
GigaOm
May 12, 2021
This report focuses on API management platforms deployed in the cloud. The cloud enables enterprises to differentiate and innovate with microservices at a rapid pace. It allows API endpoints to be cloned and scaled in a matter of minutes. And it offers elastic scalability compared with on-premises deployments, enabling faster server deployment and application development, and allowing less costly compute.
REQUEST FOR INFORMATION (RFI) GUIDE: MOVING ANALYTIC WORKLOADS TO THE CLOUD
GigaOm
May 05, 2021
Recent trends in information management see companies shifting their focus to, or entertaining a notion for a first-time use of, a cloud-based solution for their data warehouse and analytic environment. In the past, the only clear choice for most organizations has been on-premises data solutions —oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for some or all of a company’s analytical needs. Additionally, the multitudinous architectures made possible by hybrid cloud make the question no longer “Cloud, yes or no?” but “How much?” and “How can we get started?”
This RFI will reflect on the top questions you should ask in making your product selection when moving information management and your analytical workload to the cloud.
SQL TRANSACTION PROCESSING, PRICE-PERFORMANCE TESTING: MICROSOFT SQL SERVER EVALUATION: AZURE VIRTUAL MACHINES VS. AMAZON WEB SERVICES EC2
GigaOm
March 01, 2021
This report outlines the results from a GigaOm Transactional Field Test, derived from the industry-standard TPC Benchmark E (TPC-E), to compare two IaaS cloud database offerings:
Microsoft SQL Server on Amazon Web Services (AWS) Elastic Cloud Compute (EC2) instances
Microsoft SQL Server Microsoft on Azure Virtual Machines (VM)
Both are installations of Microsoft SQL Server, and we tested Red Hat Enterprise Linux OS.
Designing Data: How a Pharmacy Benefit Management Firm Modernized its Data Architecture Around Microservices and Real-Time Integration
GigaOm
February 02, 2021
Our use case describes a pharmacy benefit manager (PBM) company that processes and pays prescription drug claims for its members. The company contracts with pharmacies and builds and maintains drug formularies. It also negotiates discounts and rebates with drug manufacturers, providing cost-effective prescription drug benefits for its clients and their members.
Modernize Data Warehousing: Beyond Performance, The Importance of Other Key Attributes
Vertica
February 02, 2021
Organizations looking for enterprise data warehouses (EDWs) cannot afford to base their evaluation on query price-performance alone. There’s much more to it. You also need capabilities that reduce the time needed for configuration, management, tuning, and other tasks that can take away from valuable time spent on business analytics.
This new whitepaper from McKnight Consulting Group explores factors that can reduce the costs of analytics far beyond performance, such as licensing structure, data storage, support for non-structured data, concurrency scaling, and much more. Download your copy today, and make a well-informed decision when choosing an EDW platform.
BENCHMARK REPORT: CONTAINERIZED SQL SERVER PERFORMANCE TESTING
GigaOm
January 13, 2021
We conducted an unbiased benchmark study of SQL Server 2019 running on the most promising Kubernetes platforms available on the market today. Our goal is to help each customer determine the best-suited platform to run Microsoft SQL Server based on their individual requirements.
Read the full benchmark report, which provides insight to help IT professionals, DevOps engineers, platform architects and information security practitioners evaluate a Kubernetes platform optimal for running I/O intensive Microsoft SQL Server applications.
CLOUD DATA WAREHOUSE PERFORMANCE TESTING
MCG
January 11, 2021
This report focuses on relational analytical databases in the cloud, because deployments are at an all-time high and poised to expand dramatically. This report outlines the results from a field test derived from the industry standard TPC Benchmark DS (TPC-DS) comparing five relational analytical databases based on scale-out cloud data warehouses.
MOVING THE ENTERPRISE ANALYTICAL DATABASE – A GUIDE FOR ENTERPRISES: STRATEGIES AND OPTIONS TO MODERNIZING DATA ARCHITECTURE AND THE DATA WAREHOUSE
GigaOm
January 01, 2021
The benefits of modern data architecture are as follows:
It ensures the ability of the data analysis function of the organization to actually do analysis rather than restrict it to data hunting and preparation almost exclusively.
It provides the ability to maneuver as an organization in the modern era of information competition with consistent, connected data sets with every data set playing a mindful and appropriate role.
It enables a company to measure and improve the business with timely key performance indicators, such as streamlining your supply chain or opening up new markets with new products and services supported by technology built for analytics.
This paper will help an organization understand the value of modernizing its data architecture and how to frame a modernization effort that delivers analysis capabilities, diverse yet connected data, and key performance measures.
SQL TRANSACTION PROCESSING PRICE-PERFORMANCE TESTING: AZURE VIRTUAL MACHINES VS. AWS EC2 INSTANCES
GigaOm
November 11, 2020
Get free access to this 30-page GigaOm Transactional Field Test, derived from the industry-standard TPC Benchmark E (TPC-E), to compare two IaaS cloud database offerings: Microsoft SQL Server on Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances; Microsoft SQL Server Microsoft on Azure Virtual Machines (VM). Both are installations of Microsoft SQL Server and we tested on both Windows Server OS and Red Hat Enterprise Linux OS.
APPLICATION CACHE PERFORMANCE PRODUCT EVALUATION: AZURE CACHE FOR REDIS
GigaOm
September 07, 2020
Applications and their performance requirements have evolved dramatically in today’s landscape. The cloud enables enterprises to differentiate and innovate with APIs and microservices at a rapid pace. Cloud providers, like Azure, allow microservice endpoints to be cloned and scaled in a matter of minutes. The cloud offers elastic scalability compared to on-premises deployments, enabling faster server deployment and application development and less costly compute. In this paper, we reveal the results of application performance testing we completed both with and without Azure Cache for Redis on top of Azure SQL Database and Azure Database for PostgreSQL.
MCG ENTERPRISE CONTRIBUTION RANKING REPORT FOR CLOUD DATA MANAGEMENT AND INTEGRATION FOR CLOUD DATA WAREHOUSES AND DATA LAKES
MCG
August 05, 2020
In this paper, we have undertaken an ambitious goal of evaluating the current vendor landscape and assessing which tools and platforms have made, or are in the process of making, the leap to this new generation of data management and integration capabilities.
CLOUD DATA WAREHOUSE PERFORMANCE TESTING
GigaOm
August 05, 2020
This report focuses on relational analytical databases in the cloud, because deployments are at an all-time high and poised to expand dramatically. This report outlines the results from a field test derived from the industry standard TPC Benchmark DS (TPC-DS) comparing five relational analytical databases based on scale-out cloud data warehouses.
EMPOWERING USERS TO MINE THE DATA LAKE, ENERGIZE A DATA CATALOG AND RETURN BIG ROI
GigaOm
July 14, 2020
In this Business Technology Impact report, we take a look at a large multi-national firm and its implementation of a data lake and data catalog. Within the company, a small team worked to transform the data lake from an underutilized, misunderstood ‘white elephant’ into a resource that drove the company’s growth and innovation.
HIGH PERFORMANCE APPLICATION SECURITY TESTING, PRODUCT EVALUATION: NGINX APP PROTECT VS. MODSECURITY (PLUS AWS WEB Application)
GigaOm
July 13, 2020
Data, web, and application security has evolved dramatically over the past few years. Just as new threats abound, the architecture of applications—how we build and deploy them—has changed. We’ve traded monolithic applications for microservices running in containers and communicating via application programming interfaces (APIs)—and all of it deployed through automated continuous integration/continuous deployment (CI/CD) pipelines. The frameworks we have established to build and deploy applications are optimized for time to market—yet security remains of utmost importance.
Our focus is specifically on approaches to securing apps, APIs, and microservices that are tuned for high performance and availability. We define “high performance” as companies that experience workloads of more than 1,000 transactions per second (tps) and require a maximum latency below 30 milliseconds across the landscape.
API AND MICROSERVICES MANAGEMENT PRODUCT EVALUATION: KONG ENTERPRISE, APIGEE EDGE, AND APIGEE EDGE MICROGATEWAY
GigaOm
July 07, 2020
Application programming interfaces, or APIs, are now a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations comprise a vast array of applications and systems, many of which have turned to APIs to exchange data as the glue to hold these heterogeneous artifacts together. In this paper, we reveal the results of performance testing we completed across three API and Microservices Management platforms: Kong Enterprise, Apigee Edge, and Apigee Edge Microgateway.
HIGH PERFORMANCE CLOUD DATA WAREHOUSE VENDOR EVALUATION
GigaOm
June 16, 2020
This authoritative report from GigaOm Research, a respected independent industry analyst firm, details the key performance and cost criteria to guide your cloud data warehouse selection.
“Price and performance are critical points of interest…our analysis reveals Avalanche to be the industry leader on this criterion.”
Highlights from the report:
Key factors to consider when evaluating a hybrid cloud data warehouse
Head-on vendor comparisons across performance and cost using industry standard TPC-H benchmark
Vendors analyzed include Actian Avalanche, Snowflake, Amazon Redshift, Microsoft Azure Synapse and Google BigQuery
Assessment of single user and multiple concurrent users scenarios
TRANSITIONING FROM POSTGRESQL TO AN ANALYTICAL DATABASE FOR HIGHER PERFORMANCE AND MASSIVE SCALE
Vertica
June 15, 2020
In today’s data driven world, where effective decisions are based on a company’s ability to access information in seconds or minutes rather than hours or days, selecting the right analytical database platform is critical.
Read this McKnight white paper to learn:
Which criteria to consider for an analytical database
The process for transitioning away from PostgreSQL
Transition success stories from Etsy, TravelBird and Nimble Storage
DATA PIPELINE PLATFORM COMPARISON
GigaOm
June 08, 2020
In this report, we compare the three major data pipeline platforms: Matillion, Stitch, and Fivetran; and run them through a series of selected tests that highlight their degree of automation, ease of setup, and documentation. We evaluated aspects that include the time and effort required to set up a source-destination connection, the degree of automation throughout the process, and the quality of documentation to support the effort. These areas address the three major “humps of work” we have encountered in our field work with data pipelines.
DATA PIPELINE PLATFORM COMPARISON
GigaOm
April 20, 2020
In this report, we compare the three major data pipeline platforms: Matillion, Stitch, and Fivetran; and run them through a series of selected tests that highlight their degree of automation, ease of setup, and documentation. We evaluated aspects that include the time and effort required to set up a source-destination connection, the degree of automation throughout the process, and the quality of documentation to support the effort. These areas address the three major “humps of work” we have encountered in our field work with data pipelines.
PRICE PERFORMANCE IN MODERN CLOUD DATABASE MANAGEMENT SYSTEMS
Teradata
April 15, 2020
The pace of relational analytical databases deploying in the cloud are at an all-time high. The goal of this paper is to provide information to help a customer make the best decision, looking at factors in cloud data platform pricing – such as scope, scale, deployment, etc. – and how to determine the ultimate success metric when it comes to making a decision on a cloud data warehouse deployment – price-performance.
CLOUD DATABASE PERFORMANCE – MCKNIGHT BENCHMARK REPORT
MCG
April 13, 2020
This third-party report from McKnight Consulting Group uses industry-standard benchmark principles to evaluate the performance of three cloud-optimized analytical platforms architected for the separation of compute and storage – Vertica in Eon Mode, Amazon Redshift, and an unnamed cloud data platform.
ENTERPRISE ANALYTIC SOLUTIONS
GigaOm
April 07, 2020
This report is the third in a series of enterprise roadmaps addressing cloud analytic databases. The last two reports focused on comparing vendors on key decision criteria that were targeted primarily at cloud integration. This report is an update to the 2019 Enterprise Roadmap: Cloud Analytic Databases. However, this time around we have new vendors and a new name. We’ve reviewed and adjusted our inclusion criteria. We’re now targeting the technologies that tackle the objectives of an analytics program, as opposed to the means by which they are achieving these objectives.
HIGH PERFORMANCE API MANAGEMENT TESTING
GigaOm
March 24, 2020
Application programming interfaces, or APIs, are now a ubiquitous method and de facto standard of communication among modern information technology applications. Large companies and complex organizations have turned to APIs for exchanging data to knit these heterogeneous systems together and turn data into a service. In this paper, we reveal the results of performance testing we completed with four full-lifecycle API management platforms.
SQL TRANSACTION PROCESSING, PRICE-PERFORMANCE TESTING
GigaOm
January 13, 2020
This report details the results of a Transactional Field Test, derived from the industry-standard TPC Benchmark E (TPC-E), compared:
Microsoft SQL Server 2019 on an Amazon Web Services (AWS) r5a.8xlarge Elastic Cloud Compute (EC2) instance with General Purpose (gp2) volumes
Microsoft SQL Server 2019 on an Azure E32as_v4 Virtual Machine (VM) with P30 Premium Storage drives
BENCHMARKING ENTERPRISE STREAMING DATA AND MESSAGE QUEUING PLATFORMS
GigaOm
January 09, 2020
This category of data is known by several names: streaming, messaging, live feeds, real-time, event-driven, and so on. This type of data needs special attention, because delayed processing can and will negatively affect its value—a sudden price change, a critical threshold met, an anomaly detected, a sensor reading changing rapidly, an outlier in a log file—all can be of immense value to a decision maker, but only if he or she is alerted in time to affect the outcome.
We will introduce and demonstrate a method for an organization to assess and benchmark—for their own current and future uses and workloads—the technologies currently available. We will begin by reviewing the landscape of streaming data and message queueing technology. They are alike in purpose—process massive amounts of streaming data generated from social media, logging systems, clickstreams, Internet-of-Things devices, and so forth. However, they also have a few distinctions, strengths, and weaknesses.
DELIVERING ON THE VISION OF MLOPS: A MATURITY-BASED APPROACH
GigaOm
August 13, 2019
This report is targeted at Business and IT decision-makers as they look to implement MLOps, which is an approach to deliver Machine Learning- (ML-) based innovation projects. As well as describing how to address the impact of ML across the development cycle, it presents an approach based on maturity levels such that the organization can build on existing progress.
MODERNIZING INSURANCE DATA PLATFORMS TO IMPROVE GOVERNANCE AND ENRICH CUSTOMER EXPERIENCE
GigaOm
June 13, 2019
Pekin Insurance is one of the nation’s most successful insurance providers, with combined assets of $2 billion, more than 800 employees, 1,500 agencies, and 8,500 independent agents. Pekin Insurance is on the fast path to a full overhaul and modernization of their data, from the platform, to quality, to governance, to enabling consumers. They have built a 3-year strategy focusing on Data & Analytics and are wrapping up the final year, focused on a robust data layer with a data lake and a data warehouse, on target, on budget, and within scope.
EMBEDDED DATABASE PERFORMANCE REPORT 2
MCG
June 06, 2019
Today, to fully harness data to gain a competitive advantage, embedded databases need a high level of performance to provide real-time processing at scale.
See SQLite, the traditional alternative to the file system approach for embedding data management into edge applications and Actian Zen perform.
See for yourself in this benchmark report by McKnight Consulting Group.
DATA WAREHOUSE IN THE CLOUD BENCHMARK PRODUCT PROFILE AND EVALUATION: AMAZON REDSHIFT, MICROSOFT AZURE SQL DATA WAREHOUSE, GOOGLE BIGQUERY, AND SNOWFLAKE DATA WAREHOUSE
GigaOm
April 22, 2019
This report outlines the results from the GigaOm Analytic Field Test based on an industry standard TPC Benchmark H (TPC-H)1 to compare Amazon Redshift, Azure SQL Data Warehouse, Google Big Query, and Snowflake Data Warehouse—four relational analytical databases based on scale-out cloud data warehouses and columnar-based database architectures. Despite these similarities, there are some distinct differences in the four platforms.
CLOUD DATA WAREHOUSE PERFORMANCE TESTING
GigaOm
April 19, 2019
This report focuses on relational analytical databases in the cloud, because deployments are at an all-time high and poised to expand dramatically. This report outlines the results from a GigaOm Analytic Field Test derived from the industry standard TPC Benchmark DS (TPC-DS)1 comparing Amazon Redshift, Azure SQL Data Warehouse, Google BigQuery, and Snowflake Data Warehouse — four relational analytical databases based on scale-out cloud data warehouses and columnar-based database architectures. Despite these similarities, there are some distinct differences between the four platforms.
SQL TRANSACTION PROCESSING, PRICE-PERFORMANCE TESTING ONE
GigaOm
April 17, 2019
This report outlines the results from a Transactional Field Test, derived from the industry-standard TPC Benchmark E (TPC-E), to compare two IaaS cloud database offerings:
Microsoft SQL Server on Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances
Microsoft SQL Server Microsoft on Azure Virtual Machines (VM)
CLOUD DATA WAREHOUSE PERFORMANCE TESTING
GigaOm
April 12, 2019
This report focuses on relational analytical databases in the cloud, because deployments are at an all-time high and poised to expand dramatically. This report outlines the results from a GigaOm Analytic Field Test derived from the industry standard TPC Benchmark DS (TPC-DS)1 comparing Amazon Redshift, Azure SQL Data Warehouse, Google BigQuery, and Snowflake Data Warehouse — four relational analytical databases based on scale-out cloud data warehouses and columnar-based database architectures. Despite these similarities, there are some distinct differences between the four platforms.
TEN MISTAKES TO AVOID IN DATA MATURITY AND MODERNIZATION
TDWI
April 04, 2019
Companies everywhere are realizing that data is a key asset that can directly impact business goals. Yet, in some enterprises, awareness of data’s value doesn’t translate into increased data maturity and modernization. Often treated as a drag-along to budgeted applications, data architecture can be accidental or happenstance—a casualty of a lack of focus. The opportunity now exists to influence the future and undertake highly data-focused projects in more modern, scalable, and usable ways. In this Ten Mistakes to Avoid, William McKnight identifies the misguided practices that cause the most friction in modernization efforts and the journey to higher data maturity. He offers tips on how to mature the environment that supports the asset upon which competition is forged today—data.
CLOUD ANALYTICS PERFORMANCE REPORT
Actian
March 14, 2019
This paper specifically compares two fully-managed, cloud-based analytical databases, Actian Avalanche and Amazon Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures that scale and provide high-speed analytics. It should be noted while our testing measures the cloud-based performance of both offerings, Avalanche, unlike Redshift, is also available as an on-premise offering, Vector. In addition, Vector is available for developers as a free on-premise community edition, as a download with support in both the Amazon Web Services (AWS) and Azure marketplaces with single-click deployment.
MODERNIZING INSURANCE DATA PLATFORMS TO IMPROVE GOVERNANCE AND ENRICH CUSTOMER EXPERIENCE
GigaOm
February 15, 2019
Pekin Insurance is one of the nation’s most successful insurance providers, with combined assets of $2 billion, more than 800 employees, 1,500 agencies, and 8,500 independent agents. Pekin Insurance is on the fast path to a full overhaul and modernization of their data, from the platform, to quality, to governance, to enabling consumers. They have built a 3-year strategy focusing on Data & Analytics and are wrapping up the final year, focused on a robust data layer with a data lake and a data warehouse, on target, on budget, and within scope.
CLOUD DATA WAREHOUSE PERFORMANCE TESTING
GigaOm
February 11, 2019
This report focuses on relational analytical databases in the cloud, because deployments are at an all-time high and poised to expand dramatically. This report outlines the results from a GigaOm Analytic Field Test derived from the industry standard TPC Benchmark DS (TPC-DS)1 comparing Amazon Redshift, Azure SQL Data Warehouse, Google BigQuery, and Snowflake Data Warehouse — four relational analytical databases based on scale-out cloud data warehouses and columnar-based database architectures. Despite these similarities, there are some distinct differences between the four platforms.
CLOUD ANALYTICS PERFORMANCE REPORT
Actian
February 05, 2019
This paper specifically compares two fully-managed, cloud-based analytical databases, Actian Avalanche and Amazon Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures that scale and provide high-speed analytics. It should be noted while our testing measures the cloud-based performance of both offerings, Avalanche, unlike Redshift, is also available as an on-premise offering, Vector. In addition, Vector is available for developers as a free on-premise community edition, as a download with support in both the Amazon Web Services (AWS) and Azure marketplaces with single-click deployment.
CLOUD ANALYTICS PERFORMANCE REPORT
Actian
January 15, 2019
This paper specifically compares two fully-managed, cloud-based analytical databases, Actian Avalanche and Amazon Redshift, two relational analytical databases based on massively parallel processing (MPP) and columnar-based database architectures that scale and provide high-speed analytics. It should be noted while our testing measures the cloud-based performance of both offerings, Avalanche, unlike Redshift, is also available as an on-premise offering, Vector. In addition, Vector is available for developers as a free on-premise community edition, as a download with support in both the Amazon Web Services (AWS) and Azure marketplaces with single-click deployment.
ANALYST REPORT: API MANAGEMENT BENCHMARK REPORT
GigaOm
December 25, 2018
Application programming interfaces, or APIs, are now a ubiquitous method and de facto standard of communication among modern information technologies. The information ecosystems within large companies and complex organizations are a vast array of applications and systems, many of which have turned to APIs as the glue to hold these heterogeneous artifacts together.
This report examines the results of a performance benchmark completed with two popular API management solutions: Kong and Apigee—two full life-cycle API management platforms built with scale-out potential and architectures for large scale, high performance deployments. Despite these similarities, there are some distinct differences in the two platforms.
ANALYST REPORT: STATE OF DATA WAREHOUSE
GigaOm
October 25, 2018
Table of Contents
1 The Data Warehouse in the Organization
2 Relationships to Other Research Reports
3 The Data Warehouse Database
4 Analytic Store Platform Choices
5 Choosing the Data Warehouse Platform
6 The Cloud Analytic Database
7 Data Warehouse Flavors
7.1 The Customer Experience Transformation Data Warehouse
7.2 The Asset Maximization with IoT Data Warehouse
7.3 The Operational Extension Data Warehouse
7.4 The Risk Management Data Warehouse
7.5 The Finance Modernization Data Warehouse
7.6 The Product Innovation Data Warehouse
8 Key Takeaways
MOVING ANALYTIC WORKLOADS TO THE CLOUD: A TRANSITION GUIDE
GigaOm
June 15, 2018
Recent trends in information management see companies shifting their focus to, or entertaining a notion for a first-time use of, a cloud-based solution for their data warehouse and analytic environment. In the past, the only clear choice for most organizations has been on-premises data solutions —oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for some or all of a company’s analytical needs.
According to market research, through 2020, spending on cloud-based Big Data Analytics technology will grow 4.5x faster than spending for on-premises solutions. Due to the economics and functionality, use of the cloud should now be a given in most database selections. The factors driving data projects to the cloud are many.
Additionally, the multitudinous architectures made possible by hybrid cloud make the question no longer “Cloud, yes or no?” but “How much?” and “How can we get started?” This paper will reflect on the top decision points in determining what depth to move into the cloud and what you need to do in order to be successful in the move. This could be a move of an existing analytical workload or the move of the organization to the cloud for the first time. It’s “everything but the product selection.”
EMBEDDED DATABASE PERFORMANCE REPORT 1
MCG
April 12, 2018
This benchmark did a head-to-head comparison of Actian Zen and InfluxDB for IoT time series data, both installed on a Raspberry Pi running Linux ARM/Raspbian using their native APIs (NoSQL).
SECTOR ROADMAP: MODERN MASTER DATA MANAGEMENT
GigaOm
June 22, 2017
This Sector Roadmap is focused on master data management (MDM) selection for multiple data domains across the enterprise. In this Sector Roadmap, vendor solutions are evaluated over seven Disruption Vectors: cloud offerings, collaborative data management, going beyond traditional hierarchies, big data integration, machine learning-enabled, APIs and data-as-a-service, and onboard analytics.
High Performance Application Security Testing
Gigaom
November 01, 2021
Data, web, and application security have evolved dramatically over the past few years. Just as new threats abound, the architecture of applications—how we build and deploy them—has changed. We’ve traded monolithic applications for microservices running in containers and communicating via application programming interfaces (APIs)—and all of it deployed through automated continuous integration/continuous deployment (CI/CD) pipelines. The frameworks we have established to build and deploy applications are optimized for time to market—yet security remains of utmost importance.
Enterprise Analytic Solutions 2021 v4.0
Gigaom
September 03, 2021
Enterprises from every industry and at every scale are working to leverage data to achieve their strategic objectives—whether those are to become more profitable, effective, risk tolerant, prepared, sustainable, and/or adaptable in an ever-changing world. An enterprise’s data maturity must grow at pace with the business and its needs to achieve agility and resilience—otherwise it will be hamstrung or tripped up by limited data capabilities. A mature analytic data management strategy includes the ability to adapt.
NoSQL databases have seen a lot of growth in the past several years. The realities of the pandemic have exposed how important it is to have a reliable, agile technology infrastructure in place with proven software like the open source Apache Cassandra — which is crucial for business continuity, low latency, and being able to effectively support increased data traffic. Demand for data at all companies will only increase in the coming years, and the reality is, much of this is unexposed cost that may create an unsustainable burden for many organizations.
Integrating Hadoop leverages the discipline of data integration and applies it to the Hadoop open-source software framework for storing data on clusters of commodity hardware. It is packed with the need-to-know for managers, architects, designers, and developers responsible for populating Hadoop in the enterprise, allowing you to harness big data and do it in such a way that the solution:
Complies with (and even extends) enterprise standards
Integrates seamlessly with the existing information infrastructure
Fills a critical role within enterprise architecture.
Integrating Hadoop covers the gamut of the setup, architecture and possibilities for Hadoop in the organization, including:
Supporting an enterprise information strategy
Organizing for a successful Hadoop rollout
Loading and extracting of data in Hadoop
Managing Hadoop data once it's in the cluster
Utilizing Spark, streaming data, and master data in Hadoop processes - examples are provided to reinforce concepts.
Information Management: Strategies for Gaining a Competitive Advantage with Data (The Savvy Manager's Guides)
Morgan Kaufman
January 09, 2014
Information Management: Gaining a Competitive Advantage with Data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Information Management will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart decisions by understanding how everything from workload allocation to data stores fits together.
McKnight Associates, Inc.
Conversion Services International
May 09, 2015
William founded and grew McKnight Associates, Inc. during 1998-2005 to placement in the Inc. 500, the Dallas 100 (twice) and the Collin (county) 60. He sold the company to a public firm in 2005.
Ernst&Young Southwest Entrepreneur of the Year Finalist
Ernst & Young
May 01, 2015
Ernst&Young Southwest Entrepreneur of the Year Finalist is an award that recognizes the accomplishments of entrepreneurs in the Southwest region. The award honors businesses that have achieved remarkable success by demonstrating strategic vision, financial performance, innovation, and community and industry impact.
The Evolution of the Data Platform and What It Means to Data Warehousing
Astera
May 25, 2021
Business landscapes are in hyperdrive. Data volumes are exploding, modern sources are rapidly taking over old legacy systems, and organizations are continually seeking advanced analytics solutions to deliver unparalleled customer experiences and tap into new revenue streams. Amidst all the data chaos, you need an agile, well-knitted, responsive data warehouse architecture that provides information clarity, tackles complexity, and delivers accurate, trusted insights at lightspeed for game-changing decision making.
Opening a new dimension of agility, automation, and simplicity, Astera Software is introducing a next-gen data warehouse automation solution that allows businesses to go from data ingestion to BI & analytics in a matter of hours, all through a single platform.
Whether you want to build a new data warehouse or modernize a legacy architecture, we are bringing a solution that holds the key to unlock the agility and efficiency needed to drive your initiative.
Information - The Next Natural Resource | William McKnight | TEDxUTD
YouTube
May 26, 2015
Big Data is ubiquitous and continuously growing. William reflects on the trends of Big Data and demonstrates that its not the question of how much information we have but how we use that information wisely.
William functions as Strategist, Lead Enterprise Information Architect, and Program Manager for sites worldwide utilizing the disciplines of data warehousing, master data management, business intelligence and big data. Many of his clients have gone public with their success stories.
He is author of the book “Information Management: Strategies for Gaining a Competitive Advantage with Data.”
189: From Data Chaos to Data Maturity – McKnight Consulting Group
Spotify
April 04, 2023
William McKnight, President at McKnight Consulting Group discusses the importance of data maturity in achieving digital transformation and the crucial role of data architects in helping organizations become data-driven.
Gary Fowler and William McKnight: How To Combat Infobesity
YouTube
March 28, 2023
GSD Presents
How To Combat Infobesity with William McKnight
Guest:
William McKnight, Founder & President at McKnight Consulting Group
William McKnight has been recognized as the #1 global influencer in big data, cloud, and data center by Thinkers 360 and the #1 global influencer in master data management by Onalytica in 2022.
He is the Founder and President of McKnight Consulting Group, which advises many of the Global 2000 companies, including Pfizer, Verizon, UnitedHealth Group, Dell, Oracle, and Scotiabank, on ways to faster grow their businesses with big data.
They've been recognized as one of the Inc. 5000 fastest-growing private companies in the US two times and their clients in 14+ countries have reaped tremendous ROI and turned data into a real corporate asset.
My Career in Data Episode 22: William McKnight, President, McKnight Consulting Group
YouTube
March 01, 2023
Welcome back to a new episode of My Career in Data – a DATAVERSITY Talks podcast where we sit down with professionals to discuss how they have built their careers around data.
This week we're happy to chatting with William McKnight, the President and Founder of McKnight Consulting Group, about the importance of commitment and how his athletic pursuits have informed his perspective.
Welcome back to a new episode of My Career in Data – a DATAVERSITY Talks podcast where we sit down with professionals to discuss how they have built their careers around data.
This week we're happy to chatting with William McKnight, the President and Founder of McKnight Consulting Group, about the importance of commitment and how his athletic pursuits have informed his perspective.
Maximizing the Power of Enterprise Data
Terminal Value
February 08, 2023
Doug and William spoke about maximizing the value of enterprise data is essential for large companies to make better decisions, it involves organizing data from different systems and extracting insights from it. This process can be costly, requiring specialized tools and skilled personnel, but is necessary.
Business owners should pay attention to the analytics to gain insights and inform strategic decisions, and having the right strategy and vision is important to know what to look for and ask.
Data Architecture for the CEO
Data Leadership for Everyone
June 17, 2021
William has advised many of the world’s best-known organizations. His strategies form the information management plan for leading companies in various industries. He is a prolific author and a popular keynote speaker and trainer. He has performed dozens of benchmarks on leading database, data lake, streaming and data integration products. William is the #1 global influencer in data warehousing and master data management and he leads McKnight Consulting Group, which has twice placed on the Inc. 5000 list.
President of the Data Warehousing Institute Dallas chapter
TDWI
January 01, 1016
Represented the organization in my local community and promoted its values and mission. As President, I worked with a team of volunteers to organize and lead local events such as seminars, meetings, and social networking opportunities. Additionally, I was responsible for promoting TDWI’s educational activities, such as certification programs, and strengthening the local chapter by networking and developing relationships with vendors and other organizations.
Architecture, Products, and Total Cost of Ownership of the Leading Machine Learning Stacks
Dataversity
March 09, 2023
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion.
In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.
BUSINESS IS BUSINESS! AND IT’S BETTER WITH ANALYTICS
DM Radio
February 16, 2023
This webinar will explore how analytics can help businesses make better decisions and improve their bottom line. We will discuss how analytics can be used to identify trends, uncover opportunities, and make more informed decisions. We will also discuss how analytics can be used to measure performance, identify areas of improvement, and create strategies for success. Finally, we will discuss how analytics can be used to create a competitive advantage and drive growth. Attendees will leave with a better understanding of how analytics can be used to improve their business.
Showing ROI for Your Analytic Project
Dataversity
February 09, 2023
Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like.
Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.
23 things I’ve learned about data quality in 23 years of consulting by 2023
TechTarget
February 06, 2023
Data quality is a difficult concept to quantify while still being vital enough to sabotage any project, strategic initiative, or even an entire business. What's going on with the quality of your data? Is the data well-suited for all of its uses? Is your data everywhere – in operational databases, data warehouses, data lakes, master data management, etc.?
Information management expert William McKnight will go over 23 lessons he’s learned about data quality, frequently the hard way, in this presentation. Any problem with data quality, and quite possibly with data, that you may be experiencing is likely the result of someone failing to take one of these lessons to heart. Focusing on one or more of these could be the key to achieving understanding between parties and, ultimately, finding a solution to your data quality, and consequently data, problems.
In this presentation, you’ll discover up to 23 facts about data quality as well as fixes for the issues that the absence of data quality causes.
Analytics ROI Best Practices
Dataversity
January 18, 2023
Analytics plays a critical role in supporting strategic business initiatives. Despite the apparent value of providing the data infrastructure for these initiatives, many executives question the economic feasibility of business intelligence and analytics. This requires information professionals to calculate and present the business value in terms business executives can understand.
Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements.
This session provides a framework to help IT professionals research, measure, and present the economic value of a proposed or existing analytics initiative. The session will provide practical advice about how to calculate ROI, the formulas in use, and how to collect necessary information.
2023 Trends in Enterprise Analytics
Dataversity
January 12, 2023
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Data Architecture Best Practices for Advanced Analytics
Dataversity
January 10, 2023
Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need.
There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.
MLOps – Applying DevOps to Competitive Advantage
Dataversity
December 08, 2022
MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in:
Faster time to market of ML-based solutions
More rapid rate of experimentation, driving innovation
Assurance of quality, trustworthiness, and ethical AI
MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.
This session will be informative and helpful in uncovering some of the challenges and nuances of MLOps program development and platform selection.
Setting the Budget for the ML Stack for Analytics
BrightTalk
December 07, 2022
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They also need a platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. Above all, they need a worry-less experience with the architecture and its components.
A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $2M to $14M. Get this data point as you take the next steps on your journey.
Using Automation to Build a Sustainable Data Warehouse
Astera
November 29, 2022
Join us in this webinar and watch industry experts share insights on building a sustainable data warehouse architecture to meet the data requirements of modern enterprises.
Assessing New Database Capabilities – Multi-Model
Dataversity
November 15, 2022
Today’s enterprises have an unprecedented variety of data store choices to meet the needs of the varied workloads of an enterprise because there is no one-size-fits-all when it comes to data stores. Putting in place data stores to support a modern enterprise that is now reliant on data can lead to confusion and chaos.
Enterprises have many needs for databases, including for cache, operational, data warehouse, master data, ERP, analytical, graph data, data lake, time series data, and numerous other specific needs.
While vendor offerings have exploded in recent years, in due time frameworks will integrate components into what amounts to, for practical purposes, a single offering for multiple workloads, perhaps even for the enterprise.
A multi-model database is a database that can store, manage, and query data in multiple models, such as relational, document-oriented, key-value, graph (triplestore), and column store.
An enterprise will find reduced overhead and other synergies from choosing a single vendor for these workloads.
This session will explore the multi-model option and some criteria that decision makers should evaluate when choosing a multi-model solution.
Graph Database Use Cases
Dataversity
October 20, 2022
Graph databases may be the unsung heroes of data platforms. They are poised to expand dramatically in the next few years as the nature of important analytics data expands dramatically into understanding. We live and work today in a highly connected world where individuals and their relationships brand perceptions, consumer behaviors, and many other business success factors. Where patterns are involved in relationships, it is imperative to understand them. Graph databases are the technology that is best-suited to determining and understanding data relationships.
This code-lite session will help you determine why, how, and where to apply graphs in your enterprise.
Measuring Data Quality Return on Investment
Enterprise Analytics Online
October 19, 2022
Data Quality is an elusive subject that can defy measurement and yet be critical enough to derail any project, strategic initiative, or even a company. The data layer of an organization is a critical component because it is so easy to ignore the quality of that data or to make overly optimistic assumptions about its efficacy. Having Data Quality as a focus is a business philosophy that aligns strategy, business culture, company information, and technology in order to manage data to the benefit of the enterprise. It is a competitive strategy. However, you can’t improve what you can’t measure. We need a means for measuring the quality of our data. Abstracting quality into a set of agreed-upon data rules and measuring the occurrences of quality violations provides the measurement in the methodology.
Assessing New Databases: Translytical Use Cases
Dataversity
October 13, 2022
Organizations run their day-in-and-day-out businesses with transactional applications and databases. On the other hand, organizations glean insights and make critical decisions using analytical databases and business intelligence tools.
The transactional workloads are relegated to database engines designed and tuned for transactional high throughput. Meanwhile, the big data generated by all the transactions require analytics platforms to load, store, and analyze volumes of data at high speed, providing timely insights to businesses.
Thus, in conventional information architectures, this requires two different database architectures and platforms: online transactional processing (OLTP) platforms to handle transactional workloads and online analytical processing (OLAP) engines to perform analytics and reporting.
Today, a particular focus and interest of operational analytics includes streaming data ingest and analysis in real time. Some refer to operational analytics as hybrid transaction/analytical processing (HTAP), translytical, or hybrid operational analytic processing (HOAP). We’ll address if this model is a way to create efficiencies in our environments.
Is Our Information Management Mature?
Dataversity
June 09, 2022
Maturity frameworks have varying levels of information management maturity. Each level corresponds to not only increased data maturity, but also increased organizational maturity and bottom-line ROI. There are recommended targets to achieve an effective information management program. The speaker’s maturity framework sequences the information management activities for your consideration. It is based on real client roadmaps. This webinar promises to offer a wealth of ideas for key quick wins to benefit the organization’s information management program.
Attendees can self-assess their current information management capabilities as we go through Data Strategy, organization, architecture, and technology, yielding an overall view of the current level of information management maturity.
This webinar provides a foundation for enhancing current data and analytic capabilities and updating the strategy and plans for achievement of improved information management maturity, aligned with major initiatives.
This is always a hot topic when the speaker gives it, so be sure to come plot your shop on the curve.
Methods of Organizational Change Management
Dataversity
September 23, 2021
The disparity between expecting change and managing it — the “change gap” — is growing at an unprecedented pace. This has put many information management shops into traction as they initiate large, complex projects needed to stay competitive.
Information management professionals and business leaders must concern themselves with the organization’s acceptance of these efforts. To be successful in achieving the larger enterprise goals, these initiatives must transform the organization. However, it takes more than wishful thinking to bridge the gap.
The complexities of engaging behavioral and enterprise transformation are too often underestimated at great peril because the “soft stuff” is truly hard.
What Is My Enterprise Data Maturity 2021
Dataversity
September 22, 2021
Maturity frameworks have varying levels of Data Management maturity. Each level corresponds to not only increased data maturity but also increased organizational maturity and bottom-line ROI. There are recommended targets to achieve an effective information management program. The speaker’s maturity framework sequences the information management activities for your consideration. It is based on real client roadmaps. This webinar promises to offer a wealth of ideas for key quick wins to benefit the organization’s information management program.
Attendees can self-assess their current information management capabilities as we go through Data Strategy, organization, architecture, and technology, yielding an overall view of the current level of information management maturity.
This webinar provides a foundation for enhancing current data and analytic capabilities and updating the strategy and plans for the achievement of improved information management maturity, aligned with major initiatives.
Using Data Platforms That Are Fit-For-Purpose
Dataversity
August 19, 2021
We must grow the data capabilities of our organization to fully deal with the many and varied forms of data. This cannot be accomplished without an intense focus on the many and growing technical bases that can be used to store, view, and manage data. There are many, now more than ever, that have merit in organizations today.
This session sorts out the valuable data stores, how they work, what workloads they are good for, and how to build the data foundation for a modern competitive enterprise.
2045: A World Shaped by Artificial Intelligence
Dataversity
August 09, 2021
How will technology and society change in the next 25 years? In this session I look forward to the next 25 years. The year 2045 may seem far away, but we already have predictions about the technological innovations prevalent in 2045. Hint: Artificial Intelligence will have a huge impact.
Platforming the Major Analytic Use Cases for Modern Engineering
Dataversity
May 13, 2021
We’ll describe some use cases as examples of a broad range of modern use cases that need a platform. We will describe some popular valid technology stacks that enterprises use in accomplishing these modern use cases of customer churn, predictive analytics, fraud detection, and supply chain management.
In many industries, to achieve top-line growth, it is imperative that companies get the most out of existing customer relationships. Customer churn use cases are about generating high levels of profitable customer satisfaction through the use of knowledge generated from corporate and external data to help drive a more positive customer experience (CX).
Many organizations are turning to predictive analytics to increase their bottom line and efficiency and, therefore, competitive advantage. It can make the difference between business success or failure.
Fraudulent activity detection is exponentially more effective when risk actions are taken immediately (i.e., stop the fraudulent transaction), instead of after the fact. Fast digestion of a wide network of risk exposures across the network is required in order to minimize adverse outcomes.
Supply chain leaders are under constant pressure to reduce overall supply chain management (SCM) costs while maintaining a flexible and diverse supplier ecosystem. They will leverage IoT, sensors, cameras, and blockchain. Major investments in advanced analytics, warehouse relocation, and automation, both in distribution centers and stores, will be essential for survival.
High Performance Cloud Data Warehouse Vendor Evaluation
GigaOm
April 09, 2021
This free 1-hour webinar brings GigaOm analyst William McKnight and special guest, Cloudera’s Bill Zhang, Director of Data Warehouse Product Management to discuss the intriguing results from an in-depth Analytic Field Test derived from the industry-standard TPC-DS benchmark to compare leading cloud data warehouse offerings: Amazon Redshift, Azure Synapse, Snowflake, Google BigQuery and Cloudera Data Warehouse.
Data-driven organizations rely on analytic databases to load, store, and analyze volumes of data at high-speed to derive timely insights. Data volumes within modern organization’s information ecosystems are rapidly expanding—placing significant demands on legacy architectures. Today, to fully harness their data to gain a competitive advantage, businesses need modern, scalable architectures and high levels of performance and reliability to provide timely analytical insights. Companies are attracted to fully-managed cloud services.
How To Reduce Your Total Cost of Ownership for Cassandra
DataStax
April 06, 2021
Everybody wants data without limits - infinite scale, zero-downtime and software velocity without going over budget. The weapon of choice is Apache Cassandra, but how do you control data costs and complexity without sacrificing performance? The answer to this is serverless Cassandra! No more nodes, no more servers, no more idle/wasted capacity.
Join us as GigaOm presents the results of their study that empirically validates how serverless Cassandra saved 76% on TCO compared with self-managed Cassandra. Learn:
How to model expected costs of Cassandra workloads
How serverless Cassandra reduces infrastructure and operational costs
What are the criteria and tradeoffs for migration decisions?
Modernize Data Warehousing – Beyond Performance
Vertica
April 06, 2021
Configuration, management, tuning and other tasks can take away from valuable time spent on business analytics. If a platform leads to coding workarounds, non-intuitive implementations and other problems, it can make a big impact on long-term resource usage and cost. A lot of enterprise analytics platform evaluations focus on query price-performance to the exclusion of other features that can have a huge impact on business value, and can cause major headaches if you don’t take them into consideration.
In this webinar, we’ll go beyond price-performance, and focus on everything else needed to modernize your data warehouse.
High Performance Cloud Data Warehouse Vendor Evaluation
GigaOm
March 24, 2021
This free 1-hour webinar brings GigaOm analyst William McKnight and special guest, Cloudera’s Bill Zhang, Director of Data Warehouse Product Management to discuss the intriguing results from an in-depth Analytic Field Test derived from the industry-standard TPC-DS benchmark to compare leading cloud data warehouse offerings: Amazon Redshift, Azure Synapse, Snowflake, Google BigQuery and Cloudera Data Warehouse.
Comparing the Enterprise Analytic Solutions
Dataversity
March 11, 2021
Data is the foundation of any meaningful corporate initiative. Fully master the necessary data, and you’re more than halfway to success. That’s why leverageable (i.e., multiple use) artifacts of the enterprise data environment are so critical to enterprise success.
Build them once (keep them updated), and use again many, many times for many and diverse ends. The data warehouse remains focused strongly on this goal. And that may be why, nearly 40 years after the first database was labeled a “data warehouse,” analytic database products still target the data warehouse.
Data & Analytics 2021: Defining Trends and Trajectories
Brighttalk
February 24, 2021
Gartner predicts that “by 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling”. In 2020, data analytics has evolved together with AI to produce more accurate predictions and educated suggestions. Join Eric Topham and a panel of industry experts and thought leaders to find out how data and analytics will transform human-machine relationships.
This month's episode of The Business Intelligence Report will look at the defining data and analytics trends to watch in 2021. Some of the topics to be covered during this session will include:
- The growing importance of external data
- Trends of how organizations are finding and using external data
- How data visualization enriches analytics and helps reveal correlations
- The evolution of dashboards. Where are we now and where are we headed?
- Data visualization in real-time for timely, actionable insights
- Visual analytics use cases. Building a data narrative to achieve business results.
Increasing Artificial Intelligence Success with Master Data Management
Dataversity
February 11, 2021
Companies all over the world are going through a digital transformation now, which in many cases, is all about maturing the data environment and the use of data. Master data is key to this effort. All transformative projects require master data and usually many subject areas. Current efforts to deliver master data to the enterprise are cumbersome, inefficient, and met with limited acceptance.
We’ll look at enterprise use cases of artificial intelligence and show the master data that is needed. We’ll see what some MDM vendors are doing with AI and how the future of MDM will be shaped by looking at some specific MDM actions influenced by AI.
Modernize Data Warehousing: Beyond Performance, the Importance of Other Key Attributes
Vertica
February 02, 2021
Configuration, management, tuning and other tasks can take away from valuable time spent on business analytics. If a platform leads to coding workarounds, non-intuitive implementations and other problems, it can make a big impact on long-term resource usage and cost. A lot of enterprise analytics platform evaluations focus on query price-performance to the exclusion of other features that can have a huge impact on business value, and can cause major headaches if you don’t take them into consideration.
2021 Trends in Enterprise Analytics
Vertica
January 14, 2021
It is a fascinating, explosive time for enterprise analytics.
It is from the position of analytics leadership that the mission will be executed, and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics.
The coming years will be full of big changes in enterprise analytics and Data Architecture. William will kick off the third year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.
Why Organizations Don’t Change When They Need To
Dataversity
January 12, 2021
So you have a great idea for the data in your organization. Maybe it’s been acknowledged by some prominent leaders, but nothing ever happens. When the speaker has done his Action Plans for organizations over the years, he’s heard more questions from clients directed elsewhere in the organization about how to get the initiatives moving than he has heard about the initiatives he is creating. Organizations are mostly not good at moving good ideas forward.
Why does this happen and what can be done about it? The speaker will share his experience with utilizing his favorite skill – getting things done in enterprises.
Dislodge the logjams, make data a key asset, and make your organization an attractive, progressive place for data talent in 2021.
Thriving in the Data Age
Brighttalk
January 12, 2021
According to a recent survey, two-thirds of organisations are expecting the value and amount of data to grow almost 5x by 2025. The ‘Data Age’ is here to stay, and the way that we prepare now and the actions we take today will impact our ability to thrive. Embracing technologies like 5G, IoT, blockchain and edge computing, will increase the volume of data produced, and the ways in which data can be used productively to transform business strategy.
Join us in this final episode as we discuss:
- How cloud both fuels the ‘Data Age’ and can help navigate it
- How different industries, including retail, healthcare, finserv, manufacturing and the private sector are embracing the ‘Data Age’
Data Lineage and Its Contribution to Governance and Organizational Imperatives
Manta
January 07, 2021
How to assess an organization’s information management level
What the winning approaches to data maturity are
How data lineage contributes to accountable data governance in an organization
How various data roles in an organization benefit from using lineage and use it for various tasks and projects (data governance, impact and root-cause analyses, data quality, audits, and more)
Unveiling a New Dimension of Agile Data Warehousing- Astera Product Launch Event
Astera
August 12, 2020
Business landscapes are in hyperdrive. Data volumes are exploding, modern sources are rapidly taking over old legacy systems, and organizations are continually seeking advanced analytics solutions to deliver unparalleled customer experiences and tap into new revenue streams. Amidst all the data chaos, you need an agile, well-knitted, responsive data warehouse architecture that provides information clarity, tackles complexity, and delivers accurate, trusted insights at lightspeed for game-changing decision making.
Opening a new dimension of agility, automation, and simplicity, Astera Software is introducing a next-gen data warehouse automation solution that allows businesses to go from data ingestion to BI & analytics in a matter of hours, all through a single platform.
Whether you want to build a new data warehouse or modernize a legacy architecture, we are bringing a solution that holds the key to unlock the agility and efficiency needed to drive your initiative.
Analytic Platforms Should Be Columnar Orientation
Dataversity
November 11, 2019
A columnar database is an implementation of the relational theory, but with a twist. The data storage layer does not contain records. It contains a grouping of columns.
Due to the variable column lengths within a row, a small column with low cardinality, or variability of values, may reside completely within one block while another column with high cardinality and longer length may take a thousand blocks. In columnar, all the same data — your data — is there. It’s just organized differently (automatically, by the DBMS).
The main reason why you would want to utilize a columnar approach is simply to speed up the native performance of analytic queries.
Learn about the columnar orientation and how it can be effective for your needs. This is the native orientation of many databases and several others that have optional column-oriented storage layers.
There is also the equivalent in the cloud storage world, which is open format Parquet.
Location: Worldwide Date Available:
May 09th, 2019 Fees: Depends on scope
Submission Date:
May 09th, 2019 Service Type: Service Offered
There is a demand for information to be an asset that fuels organizational growth. Greater volumes of data are generated and service level expectations continue to rise. Tolerance for poor quality is lessened, and the underlying complexity and load on the systems is amplified. Discussions around data warehouses, data integration, big data, business intelligence, and data management occur more frequently. Probably you’ve implemented one or more of these projects but at the end of the day, you need to take your information management capabilities to the next level.
MCG’s 2- to 8-week Information Management Action Plan (IMAP) is designed for organizations that want to advance their analytical capabilities across the spectrum of information management. Information management comprises the disciplines of data warehousing, big data, business intelligence, master data management and data governance. These disciplines are incredibly interrelated. Operating them in silos will not lead to success.
MCG will analyze your needs and provide expert advice across the process, people, and technology that drive your organization and are so critical to its success. MCG has built over 40 Action Plans for our clients and contributed to business successes worldwide.
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