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Kafka

Introducing Confluent Platform 7.5

Introducing Confluent Platform version 7.5, which offers a range of new features to enhance security, improve developer efficacy, and strengthen disaster recovery capabilities. Building on the innovative feature set delivered in previous releases, Confluent Platform 7.5 makes enhancements to three categories of features: The following explores each of these enhancements and dives deep into the major feature updates and benefits.

Flink in Practice: Stream Processing Use Cases for Kafka Users

In Part One of our “Inside Flink” blog series, we explored the critical role of stream processing and why developers are increasingly choosing Apache Flink® over other frameworks. In this second installment, we'll showcase how innovative teams across every industry and size are putting stream processing into practice – from streaming data pipelines to train ML models or more timely analytics to fraud detection in finance and real-time inventory management in retail.

Introducing Versioned State Store in Kafka Streams

Since the introduction of stream processing, there have been three certainties in life: death, taxes, and out-of-order data. As a stream processing library built for Apache Kafka, Kafka Streams processes data in offset order. When out-of-order data is present, offset order differs from timestamp order and care must be taken to ensure that processing results respect timestamp order where appropriate.

Building a Real-time Snowflake Data Pipeline with Apache Kafka

In today's data-driven world, organizations seek efficient and scalable solutions for processing and analyzing vast amounts of data in real-time. One powerful combination that enables such capabilities is Snowflake, a cloud-based data warehousing platform, and Apache Kafka, a distributed streaming platform.

Stream Processing Simplified: An Inside Look at Flink for Kafka Users

There was a huge amount of buzz about Apache Flink® at this year’s Kafka Summit London. From an action-packed keynote to standing-room only breakout sessions, it's clear that the Apache Kafka® community is hungry to learn more about Flink and how the stream processing framework fits into the modern data streaming stack.

How Let's Encrypt Powers Confluent Cloud to Automate Its Certificate Operations

Since the inception of our cloud journey, we have extensively utilized Let's Encrypt because it has been very reliable, fully automated, open, and free. Today, we’re proud to become an official sponsor of Let’s Encrypt. In this blog post, we’re celebrating this event by explaining our journey with Let’s Encrypt, how we integrate with their service, and why we chose them.

Ably Kafka Connector 3.0: Increased throughput, improved error handling, Confluent Cloud accreditation

We are excited to announce the release of the Ably Kafka Connector 3.0. Version 3 brings a host of improvements, including: Overall, the Ably Kafka connector v3.0 makes the management of Kafka pipelines extension to millions of web and mobile users simpler and more reliable.

Streaming Pipelines to Data Warehouses - Use Case Implementation

Data pipelines do much of the heavy lifting in organizations for integrating, transforming, and preparing data for subsequent use in data warehouses for analytical use cases. Despite being critical to the data value stream, data pipelines fundamentally haven't evolved in the last few decades. These legacy pipelines are holding organizations back from really getting value out of their data as real-time streaming becomes essential.

The Tech Executive's Guide to Data Streaming Systems

In today's fast-paced business world, relying on outdated data can prove to be an expensive mistake. To maintain a competitive edge, it's crucial to have accurate real-time data that reflects the status quo of your business processes. With real-time data streaming, you can make informed decisions and drive value at a moment's notice. So, why would you settle for being simply data-driven when you can take your business to the next level with real-time data insights??

Streaming Pipelines to Databases - Use Case Implementation

Data pipelines do much of the heavy lifting in organizations for integrating and transforming and preparing the data for subsequent use in downstream systems for operational use cases. Despite being critical to the data value stream, data pipelines fundamentally haven't evolved in the last few decades. These legacy pipelines are holding organizations back from really getting value out of their data as real-time streaming becomes essential.