We’ve added Apache Kafka to our Event Flow offerings, enabling you to build flexible, automated machine learning workflows and maximize their business impact.
Getting data from a database into Kafka is one of the most frequent use cases we see. For data integration between enterprise data sources when migrating from monolith to microservices, what better than CDC? We talked about breaking up a monolith and the importance of data observability previously. Now we’re showing you how to do it with a typical microservices architecture pattern including PostgreSQL, Debezium and Apache Kafka.
As a developer, you're no stranger to your vast and varied data environment… Or are you? The tremendous amount of data your organization collects is stored in various sources and formats. You need a way to understand where and what data is, to be able to do what you need to do: build amazing event-driven applications.
Over the last few months, Honeycomb’s platform team migrated to a new iteration of our ingest pipeline for customer events. Our migration to this newer architecture did not go too smoothly, as can be attested by our status page since February. There were also many near-incidents where we got paged and reacted quickly enough to avoid major issues. We’ve decided to write a full overview of all the challenges we had encountered, which you can can download.
Today, we are pleased to announce a partnership with Confluent to jointly develop and deliver an enhanced product experience to the Kafka-Elasticsearch community. Kafka is — and has been since the very early days — an important component of the Elastic ecosystem.
When you’re one of many developers commanding streaming applications running in Apache Kafka, you want enough data observability to fly your own data product to the moon. But you also want to boldly go where no developer has gone before to discover new applications. At the same time, you don’t want to be exposed to sensitive data that summons you to your compliance team, crashing you back down to earth.