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Item type | Location | Call Number | Status | Notes | Date Due |
---|---|---|---|---|---|
Book | AUM Main Library English Collections Hall | 006.312 L438 (Browse Shelf) | Available | invoice 2021/1473 | |
Book | AUM Main Library English Collections Hall | 006.312 L438 (Browse Shelf) | Available | invoice 2021/1473 |
006.312 I421Advances in data mining : | 006.312 I619Introduction to data mining / | 006.312 I619Introduction to data mining / | 006.312 L438Learning Spark : | 006.312 L438Learning Spark : | 006.312 M393Big data : |
Available to OhioLINK libraries
Data is bigger, arrives faster, and comes in a variety of formats-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, you'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
Electronic reproduction. Boston, MA : Safari, Available via World Wide Web
Mode of access: World Wide Web
Made available through: Safari, an O'Reilly Media Company
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