Lessons Learned from Scaling the Tech Stack of a Modern Analytics Platform
Who is this presentation for?Data engineers, data architects, data scientists, data analysts
In a given day, analysts write more than 300,000 new lines of SQL in Periscope Data, on top of the millions of lines already executing on the platform. As the average analyst on the platform has increased their workload to use it for more than 20+ hours per week, Periscope has scaled up dramatically with dozens of terabytes of RAM supporting 1,000+ data teams around the world.
In this session, Tom O’Neill, CTO & Co-Founder of Periscope Data will discuss lessons learned from scaling up Periscope Data to support incredibly large volumes of data and queries from its data teams. He’ll highlight the process of migrating from Heroku to Kubernetes and discovering new ways to leverage its power, plus other developments and changes to the tech stack, including warehouse and data processing tools and partnerships, that have allowed Periscope users to delve deeper into new data science and machine learning analysis.
Prerequisite knowledgeFamiliarity with or interest in ETL concepts, data warehousing, and programming languages like SQL, Python and R
What you'll learn
As the Co-Founder and CTO of Periscope Data, Tom O’Neill is responsible for overseeing the technology vision for the company and leading the engineering and product teams. Previously he worked on the machine learning algorithms behind the search results at Microsoft Bing. He has a bachelor’s degree in computer science from the University of Rochester.
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