Apache Flink: Exploratory Data Analytics with SQL

Description

In this course, you will learn :

• Exploratory data analytics is a critical stage in data science that involves investigating data in order to extract insights.
• Exploring massive datasets in a big data world is difficult because it necessitates scalable, fast, and feature-rich technologies. The popular stream-processing platform Apache Flink is well suited for this endeavour.
• This course focuses on using SQL to explore datasets on Apache Flink.
• Kumaran Ponnambalam begins by going over the relational APIs that Flink provides for big data analytics. Kumaran then delves into the Table API and SQL functions.
• He investigates the various SQL capabilities available for data exploration, such as filtering, aggregations, and joins. Finally, he provides a use case project for you to practise your new skills.

Syllabus :

1. Flink Relational APIs

• What is Apache Flink?
• Flink relational APIs
• Integrations and connectors
• Course prerequisites
• Setting up the exercise files

 

2. Basic Batch Analytics

• Creating a table environment
• Creating tables from a CSV
• Selecting table data
• Filtering data in tables
• Writing tables to files

 

3. Advanced Batch Analytics

• Aggregations on tables
• Ordering and limiting data
• Adding new columns
• Joining tables
• Working with datasets

 

4. Streaming SQL

• Challenges with streaming SQL
• Dynamic tables
• Appending and retracting data
• Consuming Kafka sources
• Running continuous queries

 

5. Advanced Streaming Analytics

• Windowing on streams
• Using tumbling and sliding windows
• Writing tables to Kafka
• Working with data streams
• Using event time

 

6. Use Case Project

• Use case problem definition
• Read source data into a Flink table
• Compute total scores
• Compute aggregations

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