Must Read Spark Books
Apache Spark can accurately process a large amount of data in a record-fast time. It supports multiple languages and can compute better with multiple machine language processors. Knowing this type of programming language is a boon for emerging AI developers. Discover the top ten spark books for improving your big data programming skills. From beginner to expert, these books offer practical knowledge and in-depth insights to enhance your abilities. Start your journey towards becoming a spark programming expert today.
Key highlights
- Allows users to process large datasets across multiple machines in parallel.
- The books on this list cover many Spark-related topics, including basic and advanced programming concepts, machine learning, data processing, and optimization.
- These books cater to novice and expert developers who aim to enhance their understanding of Spark and programming for big data.
10 Most Recommended Spark Books
| Sr.no | Books | Author | Published |
Rating
|
| 1. | Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming | Gerard Maas , Francois Garillot | 2019 | Amazon: 4.4 Goodreads: 3.4 |
| 2. | Learning Spark Lightning-Fast Data Analytics | Jules Damji, Brooke Wenig, Tathagata Das, Denny Lee | 2020 | Amazon:4.7
Goodreads:4.4 |
| 3. | High-Performance Spark: Best Practices for Scaling and Optimizing Apache Spark | Holden Karau, Rachel Warren | 2017 | Amazon: 4.1
Goodreads: 3.9 |
| 4. | Spark in Action: Covers Apache Spark 3 with Examples in Java, Python, and Scala | Jean-Georges Perrin | 2020 | Amazon:4.3
Goodreads: 2.0 |
| 5. | Spark: The Definitive Guide: Big Data Processing Made Simple | Bill Chambers, Matei Zaharia | 2018 | Amazon: 4.5
Goodreads: 4.1 |
| 6. | Apache Spark in 24 Hours, Sams Teach Yourself | Jeffrey Aven | 2016 | Amazon 4.4
Goodreads- 4.1 |
| 7. | Mastering Spark with R: The Complete Guide to Large-Scale Analysis and Modeling | Javier Luraschi, Kevin Kuo , Edgar Ruiz | 2019 | Amazon 4.7 Goodreads -4.1 |
| 8. | Big Data Analytics with Spark: A Practitioner’s Guide to Using Spark for Large-Scale Data Analysis | Mohammed Guller | 2015 | Amazon 3.9 Goodreads- 4.2 |
| 9. | Spark Cookbook | Rishi Yadav | 2017 | Amazon:3.6
Goodreads: 3.5 |
| 10. | Advanced Analytics with Spark: Patterns for Learning from Data at Scale | Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills | 2017 | Amazon: 3.9
Goodreads: 3.9 |
Let us look at the Spark books and see which one best suits your needs:-
Book #1: Stream Processing with Apache Spark: Mastering Structured Streaming and Spark Streaming
Author: Gerard Maas, Francois Garillot
Book Review
It is made with full-proof concepts to ace the subject of stream processing with apache spark. The book includes real-world data processing tools for obtaining quicker insights from ever-increasing information. The book has a more theoretical approach for people of varied skill levels.
Key Takeaways from that Book
- Explore fundamental stream processing concepts concerning apache spark and delve deeper into the world of streaming jobs with Spark effortlessly.
- Significant highlights include spark streaming techniques, integrating other APIs to spark streaming, Spark as a distributed processing model, etc.
Book #2: Learning Spark Lightning-Fast Data Analytics
Author: Jules Damji, Brooke Wenig, Tathagata Das, Denny Lee
Book Review
Packed with detail from the learning objectives of apache spark incorporated in ML and get to the bottom of topics such as optimization/tuning and fundamentals of spark-shell. The book is a comprehensive guide to uncovering spark application concepts with many languages such as Python, java, Scala, etc.
Key Takeaways from that Book
- Has a lot of in-depth knowledge about running on a cluster, tuning, and debugging Spark, machine learning with MLlib, etc.
- Updates in this edition include new information on spark SQL, spark streaming, maven coordinates, etc.
Book #3: High-Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
Author: Holden Karau, Rachel Warren
Book Review
It gives a technical approach to dealing with real-world answers with no help but a spark. Does a great job in explaining the nuances of Spark code and the internal working of Spark alongside troubleshooting problems.
Key Takeaways from that Book
- Major highlights involve working with key, value data, and RDDs with the help of beautiful illustrations that are easy to understand.
- Gives in-depth notes on advanced Spark focused on an introduction to high-performance Spark, effective transformations, going beyond scale, data frames, datasets, etc.
Book #4: Spark in Action: Covers Apache Spark 3 with Examples in Java, Python, and Scala
Author: Jean-Georges Perrin
Book Review
The book is loaded with definitive theory knowledge and precise code blocks at the right point to build concepts thoroughly. Most Spark codes are in Java language, and the book requires prerequisite knowledge in Spark for beginners.
Key Takeaways from that Book
- Exploring deployment constraints, building full data pipelines, cache, and checkpoint in a brief yet everlasting way.
- Figuring out spark application architecture, querying distributed datasets with spark SQL, spark pyspark, etc.
Book #5: Spark: The Definitive Guide: Big Data Processing Made Simple
Author: Bill Chambers, Matei Zaharia
Book Review
Introducing an excellent method of learning pyspark, big data, and running Spark on a cluster in a comprehensive guide. The book meets the needs of system developers and data engineers, providing valuable insights to effectively carry out their tasks., such as statistical models and repeatable production applications.
Key Takeaways from that Book
- Provides analysis on lower-level APIs and application development in a concise effect.
- Focuses on concepts like packaging production applications and MLlib for classification or recommendation for better understanding.
Book #6: Apache Spark in 24 Hours, Sams Teach Yourself
Author: Jeffrey Aven
Book Review
Specially curated for anyone wanting to learn apache spark in record time for faster implementation of big data systems. Sewn together with a straightforward approach and step-by-step learning guide that will give you a solid foundation at a logical progression.
Key Takeaways from that Book
- Quick overview of lessons in programming with apache spark, extensions to Spark, stream processing with Spark, etc.
- Apply Spark applications with Scala, spark cluster applications, and Kafka with cutting-edge functional programming techniques.
Book #7: Mastering Spark with R: The Complete Guide to Large-Scale Analysis and Modeling
Author: Javier Luraschi, Kevin Kuo, Edgar Ruiz
Book Review
This spark book is an introductory boon to people skilled in R and looking for places to enhance their knowledge. Likely to be a more immersive experience for people who are either data scientists or system engineers.
Key Takeaways from that Book
- Contains an excellent introduction to Sparklyr alongside a wide range of modeling frameworks for geospatial analysis and graph processing.
- Generate statistical methods for extracting big data and predicting outcomes with a practical and easy-to-follow viewpoint.
Book #8: Big Data Analytics with Spark
Author: Mohammed Guller
Book reviews
A manual cum tutorial how-to book efficient enough to tackle the problems arising in building spark concepts. It does an excellent job of briefing the users with a comprehensive spark, keeping the way of programming in Scala unchanged.
Key Takeaways from that Book
- A well-structured technique for learning machine learning basics with the help of Spark.
- Highlights the usage of spark core, interactive visualization with spark-shell, and spark streaming.
Book #9: Spark Cookbook
Author: Rishi Yadav
Book Review
Brought to spark enthusiasts by the author with 17 years of experience in system design, spark cookbook doesn’t fail to deliver to-the-point data content briefly without leaning too much into technical jargon. Equipped with queries of spark SQL code and examples of real-time streaming software, it will be a worthwhile read.
Key Takeaways from that Book
- Learn to optimize and graph processing using graphs and cluster optimization in an easy and controlled manner.
- Includes various types of machine learning to get started with recommendation engine algorithms.
- Fully packed with codes and illustrations to guide you anytime you get lost.
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Book #10: Advanced Analytics with Spark: Patterns for Learning from Data at Scale
Author: Sandy Ryza (Author), Uri Laserson (Author), Sean Owen (Author), Josh Wills
Book Review
This is a brilliant take on getting a thorough knowledge of advanced analytics with the careful, in-depth procedure that takes you on a ride with real-life case studies to gain a deeper understanding. The writing style is easy to follow and crystal clear.
Key Takeaways from that Book
- Learn the recommender engine in the form of an audio scribbler data set
- Build advanced concepts with the help of geospatial, temporal data analysis, bdg project, etc.
- The prerequisite is knowing about beginner-level statistics and programming languages such as Scala, java, or Python.
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