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Beschreibung
This book explains how to scale Apache Spark 3 to handle massive amounts of data, either via batch or streaming processing. It covers how to use Spark¿s structured APIs to perform complex data transformations and analyses you can use to implement end-to-end analytics workflows.
This book covers Spark 3's new features, theoretical foundations, and application architecture. The first section introduces the Apache Spark ecosystem as a unified engine for large scale data analytics, and shows you how to run and fine-tune your first application in Spark. The second section centers on batch processing suited to end-of-cycle processing, and data ingestion through files and databases. It explains Spark DataFrame API as well as structured and unstructured data with Apache Spark. The last section deals with scalable, high-throughput, fault-tolerant streaming processing workloads to process real-time data. Here you'll learn about Apache Spark Streaming¿s execution model, the architecture of Spark Streaming, monitoring, reporting, and recovering Spark streaming. A full chapter is devoted to future directions for Spark Streaming. With real-world use cases, code snippets, and notebooks hosted on GitHub, this book will give you an understanding of large-scale data analysis concepts--and help you put them to use.
Upon completing this book, you will have the knowledge and skills to seamlessly implement large-scale batch and streaming workloads to analyze real-time data streams with Apache Spark.
What You Will Learn
Master the concepts of Spark clusters and batch data processing
Understand data ingestion, transformation, and data storage
Gain insight into essential stream processing concepts and different streaming architectures
Implement streaming jobs and applications with Spark Streaming
Understand data ingestion, transformation, and data storage
Gain insight into essential stream processing concepts and different streaming architectures
Implement streaming jobs and applications with Spark Streaming
Who This Book Is For
Data engineers, data analysts, machine learning engineers, Python and R programmers
This book explains how to scale Apache Spark 3 to handle massive amounts of data, either via batch or streaming processing. It covers how to use Spark¿s structured APIs to perform complex data transformations and analyses you can use to implement end-to-end analytics workflows.
This book covers Spark 3's new features, theoretical foundations, and application architecture. The first section introduces the Apache Spark ecosystem as a unified engine for large scale data analytics, and shows you how to run and fine-tune your first application in Spark. The second section centers on batch processing suited to end-of-cycle processing, and data ingestion through files and databases. It explains Spark DataFrame API as well as structured and unstructured data with Apache Spark. The last section deals with scalable, high-throughput, fault-tolerant streaming processing workloads to process real-time data. Here you'll learn about Apache Spark Streaming¿s execution model, the architecture of Spark Streaming, monitoring, reporting, and recovering Spark streaming. A full chapter is devoted to future directions for Spark Streaming. With real-world use cases, code snippets, and notebooks hosted on GitHub, this book will give you an understanding of large-scale data analysis concepts--and help you put them to use.
Upon completing this book, you will have the knowledge and skills to seamlessly implement large-scale batch and streaming workloads to analyze real-time data streams with Apache Spark.
What You Will Learn
Master the concepts of Spark clusters and batch data processing
Understand data ingestion, transformation, and data storage
Gain insight into essential stream processing concepts and different streaming architectures
Implement streaming jobs and applications with Spark Streaming
Understand data ingestion, transformation, and data storage
Gain insight into essential stream processing concepts and different streaming architectures
Implement streaming jobs and applications with Spark Streaming
Who This Book Is For
Data engineers, data analysts, machine learning engineers, Python and R programmers
Über den Autor
Alfonso Antolínez García is a senior IT manager with a long professional career serving in several multinational companies such as Bertelsmann SE, Lafarge, and TUI AG. He has been working in the media industry, the building materials industry, and the leisure industry. Alfonso also works as a university professor, teaching artificial intelligence, machine learning, and data science. In his spare time, he writes research papers on artificial intelligence, mathematics, physics, and the applications of information theory to other sciences.
Zusammenfassung
Covers Apache Spark application development using PySpark and SQL APIs
Explains building Apache Spark data analytics workflow and analyzing real-time data
Discusses Apache Spark with other stream processing tools, such as Apache Flink, Storm, and Kafka
Inhaltsverzeichnis
Part I. Apache Spark Batch Data Processing
Chapter 1: Introduction to Apache Spark for Large-Scale Data Analytics
1.1. What is Apache Spark?
1.2. Spark Unified Analytics
1.3. Batch vs Streaming Data
1.4. Spark EcosystemChapter 2: Getting Started with Apache Spark
2.2. Scala and PySpark Interfaces
2.3. Spark Application Concepts
2.4. Transformations and Actions in Apache Spark2.5. Lazy Evaluation in Apache Spark
2.6. First Application in Spark
2.7. Apache Spark Web UI
Chapter 3: Spark Dataframe API
Chapter 4: Spark Dataset API
Chapter 5: Structured and Unstructured Data with Apache Spark
5.1. Data Sources
5.2. Generic Load/Save Functions
5.3. Generic File Source Options
5.4. Parquet Files
5.5. ORC Files
5.6. JSON Files
5.7. CSV Files
5.8. Text Files
5.9. Hive Tables
5.10. JDBC To Other DatabasesChapter 6: Spark Machine Learning with MLlib
Part II. Spark Data Streaming
Chapter 7: Introduction to Apache Spark Streaming
7.1. Apache Spark Streaming's Execution Model
7.2. Stream Processing Architectures
7.3. Architecture of Spark Streaming: Discretized Streams
7.4. Benefits of Discretized Stream Processing
7.4.1. Dynamic Load Balancing
7.4.2. Fast Failure and Straggler Recovery
Chapter 8: Structured Streaming
8.1. Streaming Analytics
8.2. Connecting to a Stream
8.3. Preparing the Data in a Stream
8.4. Operations on a Streaming Dataset
Chapter 9: Structured Streaming Sources
9.1. File Sources
9.2. Apache Kafka Source
9.3. A Rate Source
Chapter 10: Structured Streaming Sinks
10.1. Output Modes
10.2. Output Sinks
10.3. File Sink
10.4. The Kafka Sink
10.5. The Memory Sink
10.6. Streaming Table APIs
10.7. Triggers
10.8. Managing Streaming Queries
10.9. Monitoring Streaming Queries
10.9.1. Reading Metrics Interactively
10.9.2. Reporting Metrics programmatically using Asynchronous APIs
10.9.3. Reporting Metrics using Dropwizard
10.9.4. Recovering from Failures with Checkpointing
10.9.5. Recovery Semantics after Changes in a Streaming Query
Chapter 11: Future Directions for Spark Streaming
11.1. Backpressure
11.2. Dynamic Scaling
11.3. Event time and out-of-order data
11.4. UI enhancements
11.5. Continuous Processing
Chapter 12: Watermarks. A deep survey of temporal progress metrics
Details
| Erscheinungsjahr: | 2023 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xiii
403 S. 7 s/w Illustr. 67 farbige Illustr. 403 p. 74 illus. 67 illus. in color. |
| ISBN-13: | 9781484293799 |
| ISBN-10: | 1484293797 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Antolínez García, Alfonso |
| Auflage: | 1st ed. |
| Hersteller: |
Apress
Apress L.P. |
| Maße: | 254 x 178 x 23 mm |
| Von/Mit: | Alfonso Antolínez García |
| Erscheinungsdatum: | 06.06.2023 |
| Gewicht: | 0,786 kg |
Über den Autor
Alfonso Antolínez García is a senior IT manager with a long professional career serving in several multinational companies such as Bertelsmann SE, Lafarge, and TUI AG. He has been working in the media industry, the building materials industry, and the leisure industry. Alfonso also works as a university professor, teaching artificial intelligence, machine learning, and data science. In his spare time, he writes research papers on artificial intelligence, mathematics, physics, and the applications of information theory to other sciences.
Zusammenfassung
Covers Apache Spark application development using PySpark and SQL APIs
Explains building Apache Spark data analytics workflow and analyzing real-time data
Discusses Apache Spark with other stream processing tools, such as Apache Flink, Storm, and Kafka
Inhaltsverzeichnis
Part I. Apache Spark Batch Data Processing
Chapter 1: Introduction to Apache Spark for Large-Scale Data Analytics
1.1. What is Apache Spark?
1.2. Spark Unified Analytics
1.3. Batch vs Streaming Data
1.4. Spark EcosystemChapter 2: Getting Started with Apache Spark
2.2. Scala and PySpark Interfaces
2.3. Spark Application Concepts
2.4. Transformations and Actions in Apache Spark2.5. Lazy Evaluation in Apache Spark
2.6. First Application in Spark
2.7. Apache Spark Web UI
Chapter 3: Spark Dataframe API
Chapter 4: Spark Dataset API
Chapter 5: Structured and Unstructured Data with Apache Spark
5.1. Data Sources
5.2. Generic Load/Save Functions
5.3. Generic File Source Options
5.4. Parquet Files
5.5. ORC Files
5.6. JSON Files
5.7. CSV Files
5.8. Text Files
5.9. Hive Tables
5.10. JDBC To Other DatabasesChapter 6: Spark Machine Learning with MLlib
Part II. Spark Data Streaming
Chapter 7: Introduction to Apache Spark Streaming
7.1. Apache Spark Streaming's Execution Model
7.2. Stream Processing Architectures
7.3. Architecture of Spark Streaming: Discretized Streams
7.4. Benefits of Discretized Stream Processing
7.4.1. Dynamic Load Balancing
7.4.2. Fast Failure and Straggler Recovery
Chapter 8: Structured Streaming
8.1. Streaming Analytics
8.2. Connecting to a Stream
8.3. Preparing the Data in a Stream
8.4. Operations on a Streaming Dataset
Chapter 9: Structured Streaming Sources
9.1. File Sources
9.2. Apache Kafka Source
9.3. A Rate Source
Chapter 10: Structured Streaming Sinks
10.1. Output Modes
10.2. Output Sinks
10.3. File Sink
10.4. The Kafka Sink
10.5. The Memory Sink
10.6. Streaming Table APIs
10.7. Triggers
10.8. Managing Streaming Queries
10.9. Monitoring Streaming Queries
10.9.1. Reading Metrics Interactively
10.9.2. Reporting Metrics programmatically using Asynchronous APIs
10.9.3. Reporting Metrics using Dropwizard
10.9.4. Recovering from Failures with Checkpointing
10.9.5. Recovery Semantics after Changes in a Streaming Query
Chapter 11: Future Directions for Spark Streaming
11.1. Backpressure
11.2. Dynamic Scaling
11.3. Event time and out-of-order data
11.4. UI enhancements
11.5. Continuous Processing
Chapter 12: Watermarks. A deep survey of temporal progress metrics
Details
| Erscheinungsjahr: | 2023 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Inhalt: |
xiii
403 S. 7 s/w Illustr. 67 farbige Illustr. 403 p. 74 illus. 67 illus. in color. |
| ISBN-13: | 9781484293799 |
| ISBN-10: | 1484293797 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Antolínez García, Alfonso |
| Auflage: | 1st ed. |
| Hersteller: |
Apress
Apress L.P. |
| Maße: | 254 x 178 x 23 mm |
| Von/Mit: | Alfonso Antolínez García |
| Erscheinungsdatum: | 06.06.2023 |
| Gewicht: | 0,786 kg |
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