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Mastering Apache Flink
Harness the Power of Stream Processing and Batch Data Analytics
 
Are you ready to dive into the world of stream processing and batch data analytics with Apache Flink? "Mastering Apache Flink" is your comprehensive guide to unlocking the full potential of this cutting-edge framework for real-time data processing. Whether you're a data engineer looking to optimize data flows or a data scientist aiming to derive insights from large datasets, this book equips you with the knowledge and tools to master the art of Flink-based data processing.

Mastering Apache Flink

£7.95Price

  • 1.Introduction to Apache Flink
    1.1.Understanding Stream Processing
    1.2.Evolution of Apache Flink
    1.3.Use Cases and Applications
    1.4.Getting Started with Flink
    2.Flink Architecture and Concepts
    2.1.Flink Architecture Overview
    2.2.Dataflow Model
    2.3.JobManager and TaskManager
    2.4.Data Sources and Sinks
    2.5.State Management
    2.6.Fault Tolerance
    3.Flink Data Processing
    3.1.Data Transformation Operations
    3.2.Windowing and Time Processing
    3.3.Keyed vs. Non-Keyed Operations
    3.4.Stateful Processing
    3.5.Process Functions
    4.Working with Flink APIs
    4.1.Flink's Java and Scala APIs
    4.2.DataStream and DataSet APIs
    4.3.Event Time vs. Processing Time
    4.4.Watermarks and Time Characteristics
    4.5.Type Serialization and Deserialization
    5.Flink Ecosystem Integration
    5.1.Flink and Apache Kafka
    5.2.Flink and Apache Hadoop
    5.3.Flink and Apache Hive
    5.4.Flink and Apache Cassandra
    5.5.Flink and Apache NiFi
    6.Flink Deployment and Operations
    6.1.Cluster Setup and Configuration
    6.2.Resource Management
    6.3.High Availability Setup
    6.4.Monitoring and Metrics
    6.5.Troubleshooting Common Issues
    7.Advanced Flink Concepts
    7.1.Custom Windowing and Triggers
    7.2.Stateful CEP (Complex Event Processing)
    7.3.Dynamic Scaling
    7.4.Savepoints and State Migration
    7.5.Tuning for Performance
    8.Flink Batch Processing
    8.1.Flink's Batch Processing Capabilities
    8.2.DataSet API for Batch Processing
    8.3.Optimizations for Batch Workloads
    8.4.Integrating Batch and Stream Processing
    9.Flink ML and Graph Processing
    9.1.Machine Learning with Flink
    9.2.Graph Processing with Gelly
    9.3.Use Cases and Examples
    10.Flink in Production
    10.1.Designing Reliable Pipelines
    10.2.Scalability and Elasticity
    10.3.Best Practices for Production Deployment
    10.4.CI/CD for Flink Jobs
    11.Case Studies and Real-World Examples
    11.1.Fraud Detection System
    11.2.Real-time Analytics for E-commerce
    11.3.IoT Data Processing
    11.4.Social Media Sentiment Analysis
    11.5.Financial Market Analysis
    12.Future Trends and Developments
    12.1.Flink's Role in the Streaming Data Landscape
    12.2.Integration with Cloud Services
    12.3.Advancements in State Management
    12.4.Enhanced ML and AI Capabilities
    12.5.Community and Project Roadmap
    13.Appendix
    13.1.Flink Configuration Reference
    13.2.Glossary of Terms
    13.3.Additional Resources
    About the author

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