Part 1. The theory crippled by awesome examples

1. So, what is Spark, anyway?

2. Architecture and flow

3. The majestic role of the dataframe

4. Fundamentally lazy

5. Building a simple app for deployment

6. Deploying your simple app

Part 2. Ingestion

7. Ingestion from files

8. Ingestion from databases

9. Advanced ingestion: finding data sources and building your own

10. Ingestion through structured streaming

Part 3. Transforming your data

11. Working with SQL

12. Transforming your data

13. Transforming entire documents

14. Extending transformations with user-defined functions

15. Aggregating your data

Part 4. Going further

16. Cache and checkpoint: Enhancing Spark’s performances

17. Exporting data and building full data pipelines

18. Exploring deployment constraints: Understanding the ecosystem

Appendixes

appendix A Installing Eclipse

appendix B Installing Maven

appendix C Installing Git

appendix D Downloading the code and getting started with Eclipse

appendix E A history of enterprise data

appendix F Getting help with relational databases

appendix G Static functions ease your transformations

appendix H Maven quick cheat sheet

appendix I Reference for transformations and actions

appendix J Enough Scala

appendix K Installing Spark in production and a few tips

appendix L Reference for ingestion

appendix M Reference for joins

appendix N Installing Elasticsearch and sample data

appendix O Generating streaming data

appendix P Reference for streaming

appendix Q Reference for exporting data

appendix R Finding help when you’re stuck