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Exploratory data analytics is a key phase in data science that deals with investigating data to extract insights.


In a world of big data, exploring massive datasets is a challenge, since it requires technologies that are scalable, fast, and feature rich. Apache Flink-the popular stream-processing platform-is well suited for this effort. This course focuses on exploring datasets with SQL on Apache Flink. Instructor Kumaran Ponnambalam starts off by reviewing the relational APIs that Flink provides for big data analytics. Kumaran then takes a deeper look at the Table API and SQL functions. He explores various SQL capabilities available for exploring data, including filtering, aggregations and joins. To wrap up, he provides a use case project that allows you to practice your new skills.

Topics include:

Connectors and integrations available in Flink APIs

Creating tables from a CSV

Selecting and filtering table data

Using aggregation functions in SQL

Joining tables

Windowing on streams

Event with Flink tables

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