In this book, we go beyond the boundaries of traditional batch processing and seamlessly integrate the dynamic world of streaming data. If you come from the streaming world, we provide a database perspective for stream processing. Streaming databases bridge the gap between data at rest and data in motion.
Streaming Platforms.
Streaming platforms like Apache Kafka are distributed, scalable, and fault-tolerant systems designed to handle real-time data streams. They provide a powerful infrastructure for ingesting, storing, and processing large volumes of continuous data from various sources.
Most streaming platforms have a construct called partitions. These constructs mimic WALs in a database. Transactions are appended to partitions like transactions to a WAL. Streaming platforms can hold many partitions to distribute the stream load to promote horizontal scaling. Partitions are grouped in abstractions called topics to which applications either publish or consume transactions.
Contents.
Foreword.
Preface.
1. Streaming Foundations.
2. Stream Processing Platforms.
3. Serving Real-Time Data.
4. Materialized Views.
5. Introduction to Streaming Databases.
6. Consistency.
7. Emergence of Other Hybrid Data Systems.
8. Zero-ETL or Near-Zero-ETL.
9. The Streaming Plane.
10. Deployment Models.
11. Future State of Real-Time Data.
Index.
About the Authors.
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