Confluent Blog

Data can originate in a number of different sources—transactional databases, mobile applications, external integrations, one-time scripts, etc.—but eventually it has to be synchronized to a central data warehouse for analysis

Is event streaming or batch processing more efficient in data processing? Is an IoT system the same as a data analytics system, and a fast data system the same as

The latest ksqlDB release introduces long-awaited features such as tunable retention and grace period for windowed aggregates, new built-in functions including LATEST_BY_OFFSET, a peek at the new server API under

Event stream processing solves many business challenges, from big data ingestion and data integration, to real-time data processing and IoT. It gives you the ability to analyze big data streams

The world is changing fast, and keeping up can be hard. Companies must evolve their IT to stay modern, providing services that are more and more sophisticated to their customers.

We are pleased to announce the release of ksqlDB 0.7.0. This release features highly available state, security enhancements for queries, a broadened range of language/data expressions, performance improvements, bug fixes,

Apache Kafka® is often deployed alongside Elasticsearch to perform log exploration, metrics monitoring and alerting, data visualisation, and analytics. It is complementary to Elasticsearch but also overlaps in some ways,

When a company becomes overreliant on a centralized database, a world of bad things start to happen. Queries become slow, taxing an overburdened execution engine. Engineering decisions come to a

Now that we’ve learned about the processing layer of Apache Kafka® by looking at streams and tables, as well as the architecture of distributed processing with the Kafka Streams API

Part 2 of this series discussed in detail the storage layer of Apache Kafka: topics, partitions, and brokers, along with storage formats and event partitioning. Now that we have this