Confluent Blog

We talked about how easy it is to send osquery logs to the Confluent Platform in part 1. Now, we’ll consume streams of osquery logs, detect anomalous behavior using machine […]

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, […]

Osquery (developed by Facebook) is an open source tool used to gather audit log events from an operating system (OS). What’s unique about osquery is that it uses basic SQL […]

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 […]

Do you make New Year’s resolutions? The most I personally hear about them is people making a big show about how they don’t do them. And sure enough, I don’t […]

The combination of streaming machine learning (ML) and Confluent Tiered Storage enables you to build one scalable, reliable, but also simple infrastructure for all machine learning tasks using the Apache […]

A preview of Confluent Tiered Storage is now available in Confluent Platform 5.4, enabling operators to add an additional storage tier for data in Confluent Platform. If you’re curious about […]

I am pleased to announce the release of Confluent Platform 5.4. Like any new release of Confluent Platform, it’s packed with features. To make them easier to digest, I want […]

Netflix spent an estimated $15 billion to produce world-class original content in 2019. When stakes are so high, it is paramount to enable our business with critical insights that help […]

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 […]

Part 1 of this series discussed the basic elements of an event streaming platform: events, streams, and tables. We also introduced the stream-table duality and learned why it is a […]

This four-part series explores the core fundamentals of Kafka’s storage and processing layers and how they interrelate. In this first part, we begin with an overview of events, streams, tables, […]

This article shows how you can offload data from on-premises transactional (OLTP) databases to cloud-based datastores, including Snowflake and Amazon S3 with Athena. I’m also going to take the opportunity […]