Learning Spark, 2nd Edition:
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 show...
Gespeichert in:
| Beteiligte Personen: | , , , |
|---|---|
| Körperschaft: | |
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
[Erscheinungsort nicht ermittelbar]
O'Reilly Media, Inc.
2020
|
| Ausgabe: | 2nd edition. |
| Links: | https://learning.oreilly.com/library/view/-/9781492050032/?ar |
| Zusammenfassung: | 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. |
| Beschreibung: | Online resource; Title from title page (viewed January 25, 2020) |
| Umfang: | 1 Online-Ressource (300 Seiten) |
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| spelling | Damji, Jules VerfasserIn aut Learning Spark, 2nd Edition Damji, Jules 2nd edition. [Erscheinungsort nicht ermittelbar] O'Reilly Media, Inc. 2020 1 Online-Ressource (300 Seiten) Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Online resource; Title from title page (viewed January 25, 2020) 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. Lee, Denny VerfasserIn aut Wenig, Brooke VerfasserIn aut Das, Tathagata VerfasserIn aut Safari, an O'Reilly Media Company. MitwirkendeR ctb |
| spellingShingle | Damji, Jules Lee, Denny Wenig, Brooke Das, Tathagata Learning Spark, 2nd Edition |
| title | Learning Spark, 2nd Edition |
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| title_short | Learning Spark, 2nd Edition |
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