Top 5 Kafka Tools for Data Engineers in 2024

As the dependency on high-quality, real-time data availability increases, the need for event/data streaming tools becomes increasingly crucial. Apache Kafka has become one of the most trending event streaming platforms, and its popularity has led to wide organizational acceptance in various functions related to large-scale real-time data streams. 

Today, we will explore the Top 5 Kafka tools that every data engineer should know. These tools, selected based on their functionality, are designed to simplify Kafka management and monitoring complexities. Knowing these tools are widely used and effective should instill confidence in your data engineering efforts.

What is Kafka?

Before we discuss the best Kafka tools, let’s review Apache Kafka and its features. Kafka is an open-source event streaming platform that lets you publish, subscribe to, store, and process streams of records in real time. It’s designed to handle large volumes of data, making it ideal for applications that require high throughput, low latency, and fault tolerance. Whether you’re building real-time analytics, monitoring systems, or event-driven applications, Kafka helps you manage and process data streams efficiently.

Use Cases of Kafka

1. Messaging: Kafka is a good replacement for a more traditional message broker.
2. Website Activity Tracking: Kafka can create a user activity tracking pipeline on your website as a set of real-time publish-subscribe feeds. This implies that site activity (page views, searches, or other actions users take on the site) is published to central topics, with one topic per activity type. 
3. Metrics: This involves combining statistics from distributed applications to produce centralized operational data feeds.
4. Log Aggregation: It collects physical log files off servers and puts them in a central repository (a file server or HDFS) for processing. 
5. Stream Processing: Users depending on Kafka process data through multi-stage processing pipelines, ingesting the raw input data from topics in Kafka and aggregating, enriching, or otherwise transforming them into new topics for further consumption or follow-up processing.

Why do We Need Kafka Tools?

We need Kafka tools for several reasons, such as:

1. Simplify Management of Kafka: These tools simplify Kafka management by easing broker, topic, and partition operations.
2. Real-time Monitoring: It enables Kafka performance tracking, such as throughput, latencies, and consumer lag.
3. Cluster Health Monitoring: e.g., detect problems under replicated partitions or failed brokers.
4. Improved Troubleshooting: The Kafka toolset’s message tracing and log analysis are very important for debugging issues in production.

Top 5 Kafka Tools to Consider in 2024

1. Debezium

Debezium is an open-source distributed platform for change data capture( CDC). It is a set of source connectors for Apache Kafka Connect. Each connector ingests changes from a different database, using that database’s features for change data capture (CDC). In contrast to other approaches, such as periodic polling or dual writing, log-based CDC-as implemented by Debezium also has many other features.

Why Debezium?

There are various reasons to choose Debezium, such as:

1. It captures all data changes.
2. Emits change events with a very low delay without the CPU usage increase demanded by frequent polling. In other words, for MySQL or PostgreSQL, this delay is in the millisecond range.
3. Doesn’t require any changes in your data model, for instance, a “Last Updated” column.
4. It can capture deletes.

2. Kafka Streams

Kafka Streams is a client library for constructing stream-processing applications. It offers a high-level DSL(Domain-Specific Language) and APIs that can effectively process, transform, and analyze continuous streams of records. Using the Streams API within Apache Kafka, the solution fundamentally transforms input Kafka topics into output Kafka topics.

Why Kafka Streams?

1. Light-weight embeddable client library: Ease of use, simple structure to embed into Java applications, leverages existing tooling for packaging, deployment, and operations.
2. Only external dependency: Apache Kafka. Leverages Kafka’s partitioning model for horizontal scaling with strong ordering guarantees.
3. Provides fault-tolerant local state for efficient stateful operations such as windowed joins and aggregations.
4. Processes one record at a time. It allows millisecond-level latency and supports the event-time windowing with late-arriving records.

3. Kcat

kcat, formerly kafkacat, is a command-line utility for testing and debugging Apache Kafka deployments. You can use kcat with Kafka to produce, consume, list topic and partition information. Described as “netcat for Kafka,” it is an important tool for inspecting and creating data in Kafka.

Why Kcat?

1. kcat is lightweight, fast, and super flexible; it’s the go-to tool for developers and Kafka administrators alike.
2. Command Line Kafka Client: Consume and easily produce messages from the command line to Kafka topics.
3. Supports various formats: It can produce and consume messages in several formats, such as Avro, JSON, and plain text.
4. Consumer Group Management: One can easily view and manage Kafka consumer group offsets, which makes troubleshooting easier.

4. Grafana

Grafana is an open-source interactive visualization platform that allows users to transform their data into charts and graphs integrated into one dashboard. These can then be shared with other members or even other teams for collaboration or an extensive look into the data and its implications. 

Why Grafana?

1. Unify data, not database: You don’t have to ingest data to a backend store or vendor database. Instead, Grafana takes a unique approach to providing a “single-source-of-truth” by unifying your existing data wherever it lives.
2. Real-time Visualizations: It enables users to create dynamic dashboards for real-time visualization of Kafka performance.
3. Alerting: This applies when performance thresholds are reached; this will warn the team of Kafka performance degradation.
4. Prometheus Integration: Integrate Prometheus to scrap Kafka metrics to store data in time-series format.

5. Kafka UI

UI for Apache Kafka is an easy-to-use software that brings observability into your data flows, helping you spot and troubleshoot issues much more quickly while driving optimum performance. Its lightweight dashboard makes it pretty easy to monitor the key metrics of your Kafka clusters: Brokers, Topics, Partitions, Production, and Consumption.

Why Kafka UI?

1. While command-line tools like kcat are great for power users, Kafka UI provides an accessible interface for users of all experience levels. 
2. You can configure your Kafka clusters using UI.
3. You can explore and monitor kafka related metadata changes in OpenDataDiscovery platform. 
4. View detailed metrics on Kafka brokers, including memory usage, CPU load, and more.

How to Choose the Right Kafka Tool for Yourself?

Apache Kafka tools are essential for maintaining efficiency and ensuring the stability, scalability, and reliability of Kafka clusters in production environments. Deciding which tool to choose depends on your use case and requirements and should be decided by your team of data engineers for better performance. However, there are a few basic parameters that you should keep in mind while choosing the right tool.

1. Asses your Use Case: Before going for any tool, clearly define your use case and assess the needs of your company. If you need to build a real-time application, then you should opt for Kafka stream, whereas Debezium is the obvious choice for anything related to Change Data Capture. 
2. Evaluate Tool Features and Capabilities: Before choosing any tool, take into account aspects like ease of use, integration capabilities, performance, scalability, etc.
3. Evaluate Cost and Licensing: Consider aspects like open-source vs. commercial and support and maintenance costs when choosing the right tool for your budget.
4. Conduct a proof of concept: Before fully committing to any tool, make sure you run a few test cases or conduct a Proof of Concept(PoC) to evaluate the performance and effectiveness of these tools in your tech stack. 

How Hevo Fits Seamlessly in your Kafka Tech Stack?

If you’re looking for an ETL tool that fits your business requirements and seamlessly integrates with Kafka, you have reached the right place. Although the above-mentioned tools are effective, they can be complex and have a steep learning curve. 

Meet Hevo, an automated data pipeline platform that provides the best of both tools. Hevo offers:

1. A user-friendly interface that is simpler and better than Kafka UI.
2. Unlike the tools mentioned above, which only focus on a specific aspect, Hevo provides an all-in-one platform for data integration, ETL (Extract, Transform, Load), and real-time data streaming.
3. It supports 150+ connectors, providing all popular sources(including Kafka) and destinations for your data migrations.
4. The drag-and-drop feature and custom Python code transformation allow users to make their data more usable for analysis. 
5. A transparent, tier-based pricing structure.
6. Excellent 24/7 customer support. 

These features combine to place Hevo at the forefront of the ELT market.

Conclusion 

Choosing the right Kafka tool requires evaluating your use case and tool features, considering integration and performance, and understanding cost implications. This blog will help you select a tool that best fits your needs and ensures a proper, efficient implementation of Kafka.

If you want a simple, two-step method for managing your Kafka Data, ditch all other tools and go for Hevo. It is a reliable and cost-effective tool that caters to all your business requirements. Sign up for Hevo’s 14-day free trial and experience seamless data migration.

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