![]() For example, there are datastores that are also used as mes‐ sage queues (Redis), and there are message queues with database-like durability guar‐ antees (Apache Kafka). They are optimized for a variety of different use cases, and they no longer neatly fit into traditional categories. Many new tools for data storage and processing have emerged in recent years. Periodically crunch a large amount of accumulated data (batch processing).Send a message to another process, to be handled asynchronously (stream pro‐ cessing). ![]() Allow users to search data by keyword or filter it in various ways (search indexes).Remember the result of an expensive operation, to speed up reads (caches).Store data so that they, or another application, can find it again later (databases). ![]() Reliable, Scalable, and Maintainable ApplicationsĪ data-intensive application is typically built from standard building blocks that pro‐ vide commonly needed functionality. ![]() Martin Kleppmann - Designing Data-Intensive Applications Part I. ![]()
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