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?