Data Algorithms with Spark: Recipes and Design Patterns for Scaling Up using PySpark

English | 2022 | ISBN: ‎ 1492082384 | 438 pages | True PDF | 12.58 MB

Apache Spark’s speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark.

PySpark algorithms using the PySpark driver and shell script. With this book, you will:

• Learn how to select Spark transformations for optimized solutions
• Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions()
• Understand data partitioning for optimized queries
• Build and apply a model using PySpark design patterns
• Apply motif-finding algorithms to graph data
• Analyze graph data by using the GraphFrames API
• Apply PySpark algorithms to clinical and genomics data
• Learn how to use and apply feature engineering in ML algorithms
• Understand and use practical and pragmatic data design patterns

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