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Paige Roberts

Open Source Relations Manager at Vertica

Hamilton, United States


Paige Roberts (@RobertsPaige) has worked as an engineer, trainer, support technician, technical writer, marketer, product manager, and a consultant in the last 24 years. She has built data engineering pipelines and architectures, documented and tested open source analytics implementations, spun up Hadoop clusters, picked the brains of stars in data analytics, worked with different industries, and questioned a lot of assumptions. She's worked for companies like Data Junction, Pervasive, Bloor Group, Hortonworks, Syncsort, and Vertica. Now, she promotes understanding of Vertica, distributed data processing, open source, high scale data engineering architecture, and how the analytics revolution is changing the world.

Available For: Speaking
Travels From: Texas
Speaking Topics: Analytics, Machine Learning, Data Architecture

Paige RobertsPoints

Points based upon Thinkers360 patent-pending algorithm.

Thought Leader Profile

Portfolio Mix

Company Information

Company Type: Company

Areas of Expertise

AI 30.12
Analytics 36.89
Big Data 37.97
Cloud 30.43
Digital Transformation
IoT 30.36
Predictive Analytics 30.27
DevOps 76.67

Industry Experience


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6 Article/Blogs
Container Boom: Should Databases Be Containerized?
June 11, 2021

Several years back, the application technology industry had this concept of breaking big applications up into smaller independent components, microservices, and deploying each in its own container. The container idea has some pretty cool advantages it turns out:

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Tags: Analytics, Big Data, Cloud

Why is Cloud Repatriation Happening?
March 16, 2021
More and more organizations who went all-in on cloud early are now finding that some analytics workloads are better on-premises and are pulling those workloads back.

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Tags: Analytics, Big Data, Cloud

Natural Language Processing Augmented Analytics
February 03, 2021
It’s Like Your Data Saying, “Ask Me Anything”

Analytics only makes an impact when it’s put to work to do a job automatically, or more often, help people do their jobs. The more people who can use analytics, the more valuable it becomes. And nearly every role could benefit from answers their company’s data could provide. What stops analytics from becoming part of everyone’s daily routine? It isn’t a slacking data engineering team, or an imperfect data architecture, it’s the interface. If I need to know something for my job, instead of learning complex SQL queries, or interpreting a bunch of graphs, why can’t I just ask?

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Tags: AI, Analytics, Big Data

Deliver Analytics Like Amazon Delivers Packages
August 31, 2020
Instead of focusing on where the data lives, focus on making the analytics experience as smooth as possible for everyone in your organization.

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Tags: Analytics, Cloud, Predictive Analytics

Evolution of the Modern Data Warehouse
July 24, 2020

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Tags: Analytics, Cloud, Predictive Analytics

Can Presto SQL on Hadoop Replace Your Data Warehouse?
July 06, 2020
Presto is the best of the SQL on Hadoop open source bunch. Why not just use it and ditch your analytical database? Uber knows why …

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Tags: Analytics, Big Data, Predictive Analytics

1 Book
97 Things Every Data Engineer Should Know
July 06, 2021
From the Preface
Data engineering as a distinct role is relatively new, but the responsibilities have existed for decades. Broadly speaking, a data engineer makes data available for use in analytics, machine learning, business intelligence, etc. The introduction of big data technologies, data science, distributed computing, and the cloud have all contributed to making the work of the data engineer more necessary, more complex, and (paradoxically) more possible. It is an impossible task to write a single book that encompasses everything that you will need to know to be effective as a data engineer, but there are still a number of core principles that will help you in your journey.

This book is a collection of advice from a wide range of individuals who have learned valuable lessons about working with data the hard way.

To save you the work of making their same mistakes, we have collected their advice to give you a set of building blocks that can be used to lay your own foundation for a successful career in data engineering. In these pages you will find career tips for working in data teams, engineering advice for how to think about your tools, and fundamental principles of distributed systems.

There are many paths into data engineering, and no two people will use the same set of tools, but we hope that you will find the inspiration that will guide you on your journey. So regardless of whether this is your first step on the road, or you have been walking it for years we wish you the best of luck in your adventures.

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Tags: Analytics, Big Data, DevOps

1 Keynote
Strategies to Modernize Your Data & Analytics Architecture
June 30, 2020
Data warehouses were analytics workhorses for decades, but couldn’t handle modern data volumes, types, and advanced analyses like machine learning. Big Hadoop promises about the data lake didn’t pan out. Learn how successful past, current and future architectures combine strengths of data lakes and data warehouses to make something better than both.

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Tags: Analytics, Big Data, Predictive Analytics

3 Speaking Engagements
Data Con LA 2021 - In-Database Machine Learning with Jupyter
DataCon LA
September 29, 2021
Jupyter with Python code is a productive way to prepare models, but putting machine learning models into production at scale may require re-building the entire workflow. Using the same interactive tools, but letting a distributed database do the work could get ML models into production in minutes, not months.

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Tags: Analytics, AI, Big Data

Making Production Data Accessible for Data Science at Scale
Big Data London
September 22, 2021
The data warehouse has been an analytics workhorse for decades for business intelligence teams. Unprecedented volumes of data, new types of data, and the need for advanced analyses like machine learning brought on the age of the data lake. Now, many companies have a data lake for data science, a data warehouse for BI, or a mishmash of both, possibly combined with a mandate to go to the cloud. The end result can be a sprawling mess, a lot of duplicated effort, a lot of missed opportunities, a lot of projects that never made it into production, and a lot of financial investment without return. Technical and spiritual unification of the two opposed camps can make a powerful impact on the effectiveness of analytics for the business overall.

- Look at successful data architectures from companies like Philips, The TradeDesk, Climate Corporation, …
- Learn to eliminate duplication of effort between data science and BI data engineering teams
- See a variety of ways companies are getting AI and ML projects into production where they have real impact, without bogging down essential BI
- Study analytics architectures that work, why and how they work, and where they’re going from here

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Tags: Analytics, Big Data, IoT

Python + MPP Database = Large Scale AI/ML Projects in Production Faster
April 28, 2021
Getting Python data science work into large scale production at companies like Uber, Twitter or Etsy requires a whole new level of data engineering. Economies of scale, concurrency, data manipulation and performance are the bread and butter of MPP analytics databases. Learn how to take advantage of MPP scalability and performance to get your Python work into production where it can make an impact.

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Tags: AI, Big Data, Predictive Analytics



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