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Learning Spark
Paperback
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- Book Synopsis
- 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
- About The Author
- Jules S. Damji is a senior developer advocate at Databricks and an MLflow contributor. He is a hands-on developer with over 20 years of experience and has worked as a software engineer at leading companies such as Sun Microsystems, Netscape, @Home, Loudcloud/Opsware, Verisign, ProQuest, and Hortonworks, building large scale distributed systems. He holds a B.Sc. and an M.Sc. in computer science and an MA in political advocacy and communication from Oregon State University, Cal State, and Johns Hopkins University, respectively.
- Product Details
-
- ISBN
- 9781492050049
- Format
- Paperback
- Publisher
- O'Reilly, (18 August 2020)
- Number of Pages
- 397
- Weight
- 563 grams
- Language
- English
- Dimensions
- 233 x 178 x 24 mm
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