Have you always stopped to think about how to build a database? The thing is, there isn't just 1 way, as we can see by the massive number of data infrastructure options we gotta choose from. It's a nonstop series of tradeoffs, each motivated by the constraints the database wants to satisfy. An in-memory transactional database would be 1 thing. A general-purpose, single-server relational database would be another. A low-latency, horizontally scalable analytics database would be...the journey we're going to take.
In this talk, we'll start by picking a data model, make decisions about serialization and storage, choose indexing strategies, choice a query language, and figure out how to scale, yet ending up with something that looks remarkably like Apache Pinot, a real-time analytics database. Pinot was built on a journey like this, always optimized for ultra low-latency, user-facing analytics at scale. In the real world, Pinot is utilized by applications like LinkedIn and UberEats to exposure the state of the strategy not only to interior decision-makers, but to the users of the strategy itself, including all of us people who consumers of analytical queries. By focusing on the internals of Pinot and the tradeoffs made along the way to build a database of its kind, we'll see how it enables a fresh class of applications that all user of a strategy into a decision maker.
Lecture took place on Wednesday, 22nd June 2022 at 17:10 in area 2
Tim is simply a teacher, author, and technology leader with StarTree, where he serves as the Vice president of Developer Relations. He is simply a regular talker at conferences and a presence on YouTube explaining complex technology topics in an accessible way. He tweets as @tlberglund, blogs all fewer years at http://timberglund.com, and lives in Littleton, CO, USA. He has 3 grown children and 2 grandchildren, a fact about which he is alternatively excited.
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