Chris Riccomini

(He / him / his)

Distinguished Engineer @WePay

Chris Riccomini is a software engineer, startup investor, and advisor with more than a decade of experience at major tech companies such as PayPal, LinkedIn, and WePay. He has been involved in open source throughout his career and is the author of Apache Samza. He's recently written The Missing README with co-author Dmitriy Ryaboy.

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PANEL DISCUSSION

Modern Data Engineering Panel

Data Engineering is a vast field that concerns itself with efficient access to data based on the needs of a business. Though data is the prized entity from which a company extracts insights, data doesn't exist in a void. It first needs to be stored somewhere and then an API needs to be provided such that a client can access this data. Therein lie the opportunity and the challenge. We have seen an explosion in technologies in the field of data engineering (OLTP DBs, OLAP DBs, Data Streams, Big Data Processing, Search Engines, Graph Processing and Graph Serving Engines, Caches, Block and Object Stores, etc.... ). When you consider the myriad ways these puzzle pieces can be put together to build a modern data engineering stack, you soon find that no 2 stacks resemble one another. 

What does a data engineer need to know in order to be successful in today's world? What are some best practices, pitfalls, and ways to think about building a low cost-of-ownership, high-quality data platform? What technologies are non-starters and why? What technologies are hidden gems? Finally, what should the industry think about and what is coming next? Join our panel of experts as we explore these questions in order to shed light on these areas. 
 

Date

Tuesday Nov 17 / 03:30PM EST (40 minutes)

Track

Modern Data Engineering

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PANEL DISCUSSION

Managing Data at Scale

Since the advent of the internet, the need for reliable, low latency access to data has grown at a rapid pace. Data Infrastructure, which was once a single monolithic database, has evolved into a tapestry of point solutions tied together by data movement infrastructure (e.g. data replication streams). What was once the domain of DBAs is now accessed by engineers, analysts, ops, and often non-technical folks as well. A simple set of tables has become a complex latticework of data sets, streams, batch jobs, and the like. With this increase in complexity comes challenges and new concerns.

Some of the concerns we will tackle will be:

  • How do companies manage the ever-growing complexity in modern data ecosystems? 
  • How does data operations keep track of tens of thousands of daily job executions and particularly failures? 
  • How do the security, governance, and compliance folks ensure that the right people have access to the right data fields in order to preserve end-user privacy? 
  • What are the contracts between data producers & data consumers & how are they enforced?
    • How do data producers shield data consumers from breaking changes in schemas? 
    • How do data consumers find the data sets they need and how are they notified if those data sets are end-of-life’d?

Date

Monday Nov 8 / 02:10PM EST (40 minutes)

Track

Modern Data Architectures, Pipelines, & Streams

Topics

Data StreamsData EngineeringDatabase

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