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Data is getting bigger, arriving faster, and coming in varied
formats-and it all needs to be processed at scale for analytics or
machine learning. How can you process such varied data workloads
efficiently? Enter Apache Spark. Updated to emphasize new features
in Spark 2.4., this second edition shows data engineers and
scientists why structure and unification in Spark matters.
Specifically, this book explains how to perform simple and complex
data analytics and employ machine-learning algorithms. Through
discourse, code snippets, and notebooks, you'll be able to: Learn
Python, SQL, Scala, or Java high-level APIs: DataFrames and
Datasets Peek under the hood of the Spark SQL engine to understand
Spark transformations and performance Inspect, tune, and debug your
Spark operations with Spark configurations and Spark UI Connect to
data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka
Perform analytics on batch and streaming data using Structured
Streaming Build reliable data pipelines with open source Delta Lake
and Spark Develop machine learning pipelines with MLlib and
productionize models using MLflow Use open source Pandas framework
Koalas and Spark for data transformation and feature engineering
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