Enterprise Kafka Resources

Apache Kafka is an open source stream processing platform that thousands of organizations, including 80% of the Fortune 100, rely on for their high-performance data pipelines, streaming analytics, and data integration for business-critical applications.

This guide includes enterprise Kafka resources to help teams using Kafka optimize and scale their deployments. All the content here was authored by OpenLogic Kafka experts — if you need more guidance for your specific Kafka implementation, contact us for a consultation. 

Table of Contents

1. Apache Kafka Releases and Support Lifecycle
2. How Kafka Works and Related Technologies
3. Streaming Data With Kafka
4. Apache Kafka Alternatives
5. OpenLogic: Your Enterprise Kafka Partner

Apache Kafka Releases and Support Lifecycle

Created at LinkedIn and then donated to the Apache Software Foundation in 2011, Kafka has been a top-level project since version 0.8 was developed in 2013. Since Kafka 1.0 debuted in 2017, minor releases have been rolled out roughly every four months, or three times per year.

Review the Kafka lifecycle >>
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* OpenLogic LTS available

The community support window for Kafka minor releases is one year. Here’s what the community says about their release cadence and EOL policy:

By implementing strategies for continuous integration and rolling upgrades, organizations can keep up with the latest versions of Kafka. It’s important to remember that there are no patches in community open source software, only new versions, so being able to upgrade quickly, reliably, and confidently is the best way to avoid security vulnerabilities and bugs. If you are not able to upgrade regularly, OpenLogic offers Kafka Long-Term Support which includes security patches for three years.

Confluent also sells a commercial version of Kafka which may appeal to teams that lack the internal expertise to manage their own Kafka implementation. However, there are trade-offs that come with outsourcing your streaming data infrastructure and we recommend comparing Apache Kafka vs. Confluent Kafka to understand the benefits and drawbacks of open source vs. proprietary Kafka solutions.

How Kafka Works and Related Technologies

While Kafka can be used in an application as a message handler, it is not a message broker; it is a stream processor. It can run as a cluster, with records stored in topics, and topics divided into partitions. Knowing when to use a true message broker, such as Apache ActiveMQ, and when to use a streaming event platform like Kafka can make all the difference in a new project. 

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Kafka Connect

Kafka Connect provides a platform to reliably stream data to/from Apache Kafka and external data sources/destinations. It’s a centralized hub for basic data integration between data platforms (such as databases, index engines, file stores, and key-value repositories), integrating them by allowing developers to share and reuse connectors. 

Read more about Kafka Connect >>

Kafka Cluster Configuration

Configuring a Kafka cluster is about carefully balancing throughput, latency, fault tolerance, and scalability. Every decision you make, from partition counts to replication policies, impacts how efficiently your cluster can process streams of data and handle recovery after failures.

Explore strategies for Kafka cluster configuration >>

Kafka Partitions

Kafka partitions work by creating multiple logs from a single topic log and spreading them across one or more brokers. Partitions are what makes Kafka scalable, and the most common partitioning strategies are round robin partitioning and message key partitioning.

Discover how to get started with Kafka partitions >>

ZooKeeper and Kafka Raft (KRaft)

ZooKeeper provides distributed configuration services, synchronization services, leadership election services, and a naming registry for a number of open source projects including Kafka, Hadoop, Spark, SOLR, NiFi, and HBase. Kafka and ZooKeeper work together to form a complete Kafka Cluster ⁠— with ZooKeeper providing distributed clustering services, and Kafka handling the actual data streams and connectivity to clients.

Learn about using Kafka with ZooKeeper >>

However, ZooKeeper is being removed in Kafka 4 and replaced with Kafka Raft (KRaft) metadata mode, so understanding the upgrade path is critical. 

Find out how to run Kafka in KRaft Mode >>

Kafka MirrorMaker

Kafka MirrorMaker is a tool for mirroring data between Apache Kafka clusters. It leverages the Kafka Connect framework to replicate data, which improves resiliency. MirrorMaker 2.0 is the current version that comes bundled with the Kafka installation package.

Get details on Kafka MirrorMaker >> 

Streaming Data With Kafka

Modern systems generate more data than ever before. And, for complex enterprise systems, facilitating that data in real-time is key to business success. Harnessing data streams at scale is a challenge for many organizations, but Kafka is a low-latency, highly durable stream processing platform capable of handling huge volumes of data.

Stream Processing

Within the Kafka platform, the streams API helps with wiring applications to manipulate streams, functioning as a stream processor. Events are managed in real-time and routed from different sources to different destinations continuously. This is done with all the necessary controls for dependable usability to write, store, and process small and large volumes of data.

See use cases for Kafka stream processing >>   
Watch webinar about harnessing streaming data with Kafka >>

Kafka Streaming with Spark

In the current technology landscape, existing applications and workloads are looking for expedited delivery. With real-time data lakes, it is possible to leverage Spark and Kafka in multi-data environments that demand capturing and processing in seconds.

Discover data streaming with Kafka and Spark >>   
Watch webinar about real-time Kafka data lakes >>   
Download white paper on Cassandra, Kafka, and Spark >>

Performance

Achieving optimal Kafka performance requires a deep understanding of its event-driven architecture as well as a commitment to ongoing maintenance. Knowing anti-patterns to avoid and how to balance throughput and latency are essential to keeping your cluster from becoming a bottleneck. 

Read about Kafka performance tuning >>  

Security

Given how harmful a data breach can be to an organization’s reputation, it’s important to keep your Kafka environment secure. This means not running the default configuration and regularly upgrading, as well as implementing encryption, hardening, and authentication processes.

Get Kafka security best practices >>

Kafka on Kubernetes

Running Kafka on Kubernetes allows it to be deployed in the most native way possible. When combined with Strimzi, managing and running Kafka Clusters on a container orchestration system like Kubernetes is easy — whether that's on-prem, or across public, private, or hybrid clouds.

Apache Kafka Alternatives

Many of the technologies that often get compared are not true 1:1 Kafka alternatives, but have some overlapping capabilities and similar enterprise use cases.

Kafka vs. ActiveMQ

Kafka, as an event streaming platform, is typically used differently than ActiveMQ, which is an open source message broker.

Understand the differences between Kafka vs. ActiveMQ >>
Compare IBM MQ vs. Kafka vs. ActiveMQ >>

Kafka vs. RabbitMQ

RabbitMQ is an open-source distributed message broker. While both Kafka and RabbitMQ use a publish/subscribe messaging system, Kafka takes a pull-based approach, whereas RabbitMQ employs a push model.

Explore Kafka vs. RabbitMQ >>

Kafka vs. Redis

Although they often get compared, Kafka is an open source stream processing platform, while Redis is a general purpose in-memory data store, so the two technologies serve very different functions within application data infrastructure.

Read more about Kafka vs. Redis >> 

Kafka Streams vs. Apache Flink

Although initially created for different use cases, Apache Flink and Kafka Streams API have a lot of overlap in their application. While both can solve stream processing challenges, their differences in deployment, architecture, and more are worth considering before making a decision.

Read more about Kafka Streams vs. Flink >>

Kafka vs. Hadoop

Despite some overlap in Big Data use cases, Kafka and Hadoop are not really 1:1 solutions and both play an important role in the landscape of data management infrastructure. It's critical to understand why organizations might select one or the other, or implement both as part of their streaming data strategy. 

Find out when to choose Kafka vs. Hadoop >> 

OpenLogic: Your Enterprise Kafka Partner

If you’re using Kafka at scale, when things go wrong, they go wrong at the same scale.

OpenLogic offers two solutions for enterprise Kafka:

SLA-Backed Kafka Support and LTS

Our experienced enterprise architects can help configure, improve, and maintain your entire Kafka stack, including Kafka for data lakes, Kafka on bare metal, Kafka with Kubernetes operators, and Kafka with Prometheus and Grafana. We also offer long-term support to extend the life of EOL versions. 

Get Kafka Support Explore Kafka LTS