This book is primarily intended for those looking to use Apache Spark. Apache Spark is a robust platform for Big Data applications that explores a lot of cutting-edge techniques. The book provides numerous exceptional examples while clearly and methodically describing the Spark architecture.
This book does not include in-depth introductions to some of the analytics techniques you can use in Apache Spark, such as machine learning. Instead, it provides a reader with information that helps them become familiar with the rather brilliant Spark programming models. Instead, assuming you have a foundational understanding of machine learning, we demonstrate how to use these techniques using libraries in Spark. The presentation of the material is excellent, and the explanations are quite helpful and aid in the understanding. This lovely book is about the wonderful Spark system.

Apache Spark:

A unified computing engine and collection of libraries for parallel data processing on computer clusters are called Apache Spark. The most actively maintained open source engine for this task as of this writing is Spark, which makes it a required tool for every developer or data scientist interested in large data. With libraries for a variety of workloads like SQL, streaming, and machine learning, as well as support for various popular programming languages (Python, Java, Scala, and R), Spark can be utilized anywhere from a laptop to a cluster of hundreds of servers. Because of this, it is simple to implement and scale up to handle massive data processing or extremely high scales.


Apache Spark is one of the most active projects in the Hadoop ecosystem due to its many advantages. Spark can quickly conduct analytical queries against any size of data using in-memory caching and efficient query execution. You have access to a range of programming languages for creating your applications thanks to Apache Spark’s native support for Java, Scala, R, and Python. These APIs simplify things for your developers by hiding the complexities of distributed processing behind straightforward, high-level operators, which significantly reduces the amount of necessary code. Several workloads, including interactive queries, real-time analytics, machine learning, and graph processing, can be handled on Apache Spark. Multiple workloads can be combined smoothly by a single application. In order to significantly speed up machine learning algorithms that frequently run a function on the same dataset, Spark additionally reuses data by employing an in-memory cache. The construction of Data Frames, an abstraction over the Resilient Distributed Dataset (RDD), which is a collection of objects cached in memory and reused in various Spark operations, allows for the reuse of data. Due to the huge reduction in latency, Spark is now significantly faster than MapReduce, especially when performing machine learning and interactive analytics.

Topics you are going to study:

1. What Is Apache Spark?

2. A Gentle Introduction to Spark.

3. A Tour of Spark’s Toolset.

4. Structured API Overview.

5. Basic Structured Operations.

6. Working with Different Types of Data.

7. Aggregations.

8. Joins.

9. Data sources.

10. Spark SQL.

11. Datasets.

12. Resilient Distributed Datasets (RDDs).

13. Advanced RDDs.

14. Distributed Shared Variables.

15. How Spark Runs on a Cluster.

16. Developing Spark Applications.

17. Deploying Spark.

18. Monitoring and Debugging.

19. Performance Tuning.

20. Stream Processing Fundamentals. 

21. Structured Streaming Basics.

22. Event-Time and Stateful Processing.

23. Structured Streaming in Production.

24. Advanced Analytics and Machine Learning Overview.

25. Preprocessing and Feature Engineering. 

26. Classification.

27. Regression.

28. Recommendation.

29. Unsupervised Learning.

30. Graph Analytics.

31. Deep Learning.

32. Language Species: Python and R.

33. Ecosystem and Community.

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