In the first two parts of our Inside Flink blog series, we explored the benefits of stream processing with Flink and common Flink use cases for which teams are choosing to leverage the popular framework to unlock the full potential of streaming. Specifically, we broke down the key reasons why developers are choosing Apache Flink® as their stream processing framework, as well as the ways in which they are putting it into practice.
One of the most important questions in architecting a data platform is where to store and archive data. In a blog series, we’ll cover the different storage strategies for Kafka and introduce you to Lenses’ S3 Connector for backup/restore. But in this first blog, we must introduce the different Cloud storage options available. Later blogs will focus on specific solutions, explain in more depth how this maps to Kafka and then how Lenses manage your Kafka topic backups.
Is Windows your favorite development environment? Do you want to run Apache Kafka® on Windows? Thanks to the Windows Subsystem for Linux 2 (WSL 2), now you can, and with fewer tears than in the past. Windows still isn’t the recommended platform for running Kafka with production workloads, but for trying out Kafka, it works just fine. Let’s take a look at how it’s done.
Various factors can impede an organization's ability to leverage Confluent Cloud, ranging from data locality considerations to stringent internal prerequisites. For instance, specific mandates might dictate that data be confined within a customer's Virtual Private Cloud (VPC), or necessitate operation within an air-gapped VPC. However, a silver lining exists even in such circumstances, as viable alternatives remain available to address these specific scenarios.
Kafka Connect is an open source data integration tool that simplifies the process of streaming data between Apache Kafka® and other systems. Kafka Connect has two types of connectors: source connectors and sink connectors. Source connectors allow you to read data from various sources and write it to Kafka topics. Sink connectors send data from the topics to another endpoint.
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.
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.
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.
