May 1, 2024
Updated May 11, 2025
17 minute read
Batch processing is a method used by computers to process high-volume, repetitive data jobs. In essence, data is collected, stored, and then processed in groups or "batches." This approach allows systems to handle large amounts of data efficiently, often during off-peak hours when computing resources are more readily available, minimizing user interaction once the process begins. Imagine a busy post office that, instead of processing each letter individually as it arrives, waits until it has a large sack of mail and then sorts and sends it all at once – that's similar to how batch processing works.
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Find a path to becoming a Batch Processing. Learn more at:
OpenCourser.com/topic/tr769t/batch
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Batch Processing.
Presents a comprehensive guide to Apache Spark, discussing its architecture, programming models, and use cases for large-scale data processing, machine learning, and stream processing.
Provides a comprehensive overview of the principles and practices involved in designing data-intensive applications, offering insights into data modeling, storage, processing, and analysis.
Covers advanced techniques for data analysis and machine learning using Spark. It is relevant for those interested in applying batch processing for data-intensive analytics and machine learning tasks.
Offers a practical guide to building and managing data pipelines, covering essential concepts, design patterns, and best practices for ensuring scalability, reliability, and maintainability. It valuable resource for those designing and implementing batch processing pipelines.
Partially fits the topic as it explores website scalability, emphasizing distributed systems architectures and offering principles for building scalable and reliable web applications.
Offers a broad perspective on big data analytics, covering the entire lifecycle from strategic planning to implementation and integration. It includes real-world case studies and insights into the challenges and considerations involved.
Focuses on Apache Flink, a popular open-source framework for stream data processing, providing a deep dive into its architecture, programming model, and advanced applications.
Focuses on big data processing using Hadoop, covering fundamental concepts, practical implementation techniques, and advanced topics related to large-scale data analysis.
Provides a non-technical introduction to data science, focusing on the business applications of data mining and data-analytic thinking. It covers key concepts and techniques for extracting value from data, including batch processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/tr769t/batch
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