- Learning Apache Flink
- Learning Apache Flink
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Customer Feedback
- Preface
- 1. Introduction to Apache Flink
- 2. Data Processing Using the DataStream API
- 3. Data Processing Using the Batch Processing API
- Data sources
- Transformations
- Map
- Flat map
- Filter
- Project
- Reduce on grouped datasets
- Reduce on grouped datasets by field position key
- Group combine
- Aggregate on a grouped tuple dataset
- MinBy on a grouped tuple dataset
- MaxBy on a grouped tuple dataset
- Reduce on full dataset
- Group reduce on a full dataset
- Aggregate on a full tuple dataset
- MinBy on a full tuple dataset
- MaxBy on a full tuple dataset
- Distinct
- Join
- Cross
- Union
- Rebalance
- Hash partition
- Range partition
- Sort partition
- First-n
- Broadcast variables
- Data sinks
- Connectors
- Iterations
- Use case - Athletes data insights using Flink batch API
- Summary
- 4. Data Processing Using the Table API
- Registering tables
- Accessing the registered table
- Operators
- The select operator
- The where operator
- The filter operator
- The as operator
- The groupBy operator
- The join operator
- The leftOuterJoin operator
- The rightOuterJoin operator
- The fullOuterJoin operator
- The union operator
- The unionAll operator
- The intersect operator
- The intersectAll operator
- The minus operator
- The minusAll operator
- The distinct operator
- The orderBy operator
- The limit operator
- Data types
- SQL
- Use case - Athletes data insights using Flink Table API
- Summary
- 5. Complex Event Processing
- 6. Machine Learning Using FlinkML
- 7. Flink Graph API - Gelly
- 8. Distributed Data Processing with Flink and Hadoop
- 9. Deploying Flink on Cloud
- 10. Best Practices
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