Confluent Blog

Organizations define standards and policies around the usage of data to ensure the following: Data quality: Data streams follow the defined data standards as represented in schemas Data evolvability: Schemas

Building data pipelines isn’t always straightforward. The gap between the shiny “hello world” examples of demos and the gritty reality of messy data and imperfect formats is sometimes all too

This blog post presents the use cases and architectures of REST APIs and Confluent REST Proxy, and explores a new management API and improved integrations into Confluent Server and Confluent

Have you ever had to write a program that needed to handle any data payload that could be thrown at you? If so, did you always have to update the

Event modeling has always been a pain point in organizations. From figuring out the standard format of your schemas, processing said data models effectively, and finally testing before you deploy

A fundamental challenge with today’s “data explosion” is finding the best answer to the question, “So where do I put my data?” while avoiding the longer-term problem of data warehouses,

Stream processing applications, including streaming ETL pipelines, materialized caches, and event-driven microservices, are made easy with ksqlDB. Until recently, your options for interacting with ksqlDB were limited to its command-line

When operating cloud infrastructure, “time is money” is more than a cliché—it is interpreted literally as every processing second stacks up on the monthly bill. ksqlDB strives to reduce these

In the article Should You Put Several Event Types in the Same Kafka Topic?, Martin Kleppmann discusses when to combine several event types in the same topic and introduces new

Confluent Cloud supports Schema Registry as a fully managed service that allows you to easily manage schemas used across topics, with Apache Kafka® as a central nervous system that connects