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Learning Spark Vital Source e-bog
Jules S. Damji, Brooke Wenig, Tathagata Das og Denny Lee
(2020)
O'Reilly Media, Inc
435,00 kr.
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Learning Spark
Lightning-Fast Data Analytics
Jules S. Damji, Brooke Wenig, Tathagata Das og Denny Lee
(2020)
Sprog: Engelsk
O'Reilly Media, Incorporated
699,00 kr.
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Detaljer om varen
- 2. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: O'Reilly Media, Inc (Juli 2020)
- Forfattere: Jules S. Damji, Brooke Wenig, Tathagata Das og Denny Lee
- ISBN: 9781492049999
Data is bigger, arrives faster, and comes in a variety of formatsâ??and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data 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 step-by-step walk-throughs, code snippets, and notebooks, youâ??ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug 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
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Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: -1 sider kan printes ad gangen
Copy: højest -1 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- 2. Udgave
- Paperback: 300 sider
- Udgiver: O'Reilly Media, Incorporated (August 2020)
- Forfattere: Jules S. Damji, Brooke Wenig, Tathagata Das og Denny Lee
- ISBN: 9781492050049
Data is bigger, arrives faster, and comes in a variety of formatsâ??and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark.
Updated to include Spark 3.0, this second edition shows data engineers and data 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 step-by-step walk-throughs, code snippets, and notebooks, youâ??ll be able to:
- Learn Python, SQL, Scala, or Java high-level Structured APIs
- Understand Spark operations and SQL Engine
- Inspect, tune, and debug 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
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