9781492050049 - learning spark: lightning-fast data analytics di damji, jules s; wenig, brooke; das, tathagata; lee, denny (38 risultati)

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Paperback. Condizione: Good. Updated to emphasize new features in Spark 2.4., 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.…

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Paperback or Softback. Condizione: New. Learning Spark: Lightning-Fast Data Analytics. Book.

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Paperback. Condizione: New. 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 2.4., 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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Paperback. Condizione: New. 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 2.4., 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.…

Paperback. Condizione: new. Paperback. 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 2.4., 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" Updated to emphasize new features in Spark 2.4., 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Condizione: New. 2020. 2nd ed. Paperback. . . . . .

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Condizione: New. 2020. 2nd ed. Paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condizione: Brand New. 2nd edition. 300 pages. 9.25x7.25x1.00 inches. In Stock.

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Paperback. Condizione: New. 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 2.4., 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.…

Taschenbuch. Condizione: Neu. Neuware -Data is bigger, arrives faster, and comes in a variety of formatsand 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 not Elektronisches Buch, 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 Englisch.…

Taschenbuch. Condizione: Neu. Neuware -Data is bigger, arrives faster, and comes in a variety of formatsand 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 not Elektronisches Buch, 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 Englisch.…

Taschenbuch. Condizione: Neu. Neuware -Data is bigger, arrives faster, and comes in a variety of formatsand 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 not Elektronisches Buch, 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.…

Condizione: New. Updated to emphasize new features in Spark 2.4., 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-lea.

Taschenbuch. Condizione: Neu. Neuware - Data is bigger, arrives faster, and comes in a variety of formatsand 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 not Elektronisches Buch, 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.…