Apache Flink: Exploratory Data Analytics with SQL

Learn how to use Apache Flink relational APIs—the Table API and SQL—for batch and real-time exploratory data analytics.

Introduction

• Apache Flink for exploratory analysis

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

Conclusion

• Next steps