Abstract: 

As data processing becomes more real time, stream processing is becoming more important. Apache Flink makes it easier to build and manage stream processing applications. Flink’s new SQL interface is a great way to get started with Flink—and to build and maintain production applications.

Seth Wiesman offers an overview of Apache Flink via the SQL interface, covering stream processing and Flink’s various modes of use. Then you’ll use Flink to run SQL queries on data streams and contrast this with the Flink DataStream API.

Outline:

Section 1

Survey of Apache Flink and its interfaces
Intro into SQL on Flink
Unified API for batch and streaming
Executing SQL queries on Flink
Documentation walkthrough
Section 2

Hands-on exercise: Setting up the SQL CLI client
Running the first queries
SQL on DataStreams
Tables, streams, and materialized views
Supported operations
Event time and processing time
Section 3

Hands-on exercise: Setting running queries on data streams
Windowed queries
Event time queries
Processing time queries
Materializing queries
Section 4

Flink APIs, internals, connectors, and UDFs
Table API and SQL
DataStream API
Hands-on exercise: Working with the DataStream API

Bio: 

Coming Soon!