
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.x., 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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Apache Spark is a lightning-fast unified analytics engine for big data and machine learning. It was originally developed at UC Berkeley in 2009. · The largest open Customer reviews: Learning Spark: Lightning - Amazon.com
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Contribute to karajan1001/learning-spark-lightning-fast-data-analysis development by creating an account on GitHub. Learning Spark: Lightning-Fast Big Data Analysis / Edition 1
Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to Learning Spark: Lightning-Fast Big Data Analysis - Amazon.in
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Written by the developers of Spark, this book will have data scientists and engineers up and running in no Learning Spark: Lightning-Fast Big Data Analysis Learning Spark: Lightning-Fast Data Analytics - Bookshop
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. Books :: Learning Spark: Lightning-Fast Data Analytics
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