Real-Time Data in the form of events from sources such as Databases, sensors, mobile devices, cloud services, and software applications is crucial for business growth. A Community Distributed Message Streaming Platform like Apache Kafka helps ensure a continuous flow of data so that businesses can take strategic decisions in Real-Time. Kafka is unified solution offering services to read, write, store & process thousands of messages per second.
You can Install Kafka on Mac, Windows, Linux Operating Systems as well as deploy it in the cloud. Apache Kafka provides several versions of the software on its website to Install Kafka on Mac. You can either Manually download the Kafka package and Install Kafka on Mac or use the single-line Homebrew commands on Mac.
In this article, you will learn how to effectively Install Kafka on Mac Manually as well as via Homebrew commands.
Table of Contents
What is Apache Kafka?
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Apache Kafka is a Popular Open-Source Distributed Stream Data Ingesting & Processing Platform. Providing an end-to-end solution to its users, Kafka can efficiently read & write streams of events in real-time with constant import/export of your data from other data systems.
Its Reliability & Durability allows you to store streams of data securely for as long as you want. With its Best-in-Class performance, Low latency, Fault Tolerance, and High Throughput, Kafka can handle & process thousands of messages per second in Real-Time.
Launched as an Open Source Messaging Queue System by LinkedIn in 2011, Kafka has now evolved into a Full-Fledged Event Streaming Platform. It is an excellent tool for building Real-Time Streaming Data Pipelines and Applications that adapt to the Data Streams. You can easily Install Kafka on Mac, Windows & Linux OS. Adding to its Flexibility, Kafka works for both Online & Offline Message Consumption.
Key Features of Apache Kafka
Over the Years, Apache Kafka has grown into a one-stop solution for all the Stream-Processing needs. Some of its eye-catching features are:
- Scalability: Owning to its unique architecture, Kafka can easily handle scaling of all of its 4 elements i.e. event producers, event processors, event consumers, and event connectors without any downtime.
- Time-Based Data Retention: Kafka offers a simple yet effective approach towards Fault Tolerance. It persistently writes and replicates all your data to the disk. You can also set the Retention Limit and recall the stored data based on those periods.
- Unbelievably Fast: With the ability to handle TBs of messages, Kafka provides high throughput for both Publishing and Subscribing messages. It automatically Decouples Data Streams, thereby providing low latency.
- Durability: Topics stored by Kafka maintain a Partitioned Structured Commit Log that continuously keeps track of all the records that are present as well as adds the new ones in Real-Time. Since the partitions are distributed and replicated across multiple servers, Kafka provides a durable Fault-Tolerant Ecosystem in cases of server failure.
- Integration Support: Catering to everyone’s needs, Kafka provides Integration points via Connector API, allowing you to expand and grow. You can build integrations with third-party solutions, other messaging systems, and legacy applications. You achieve this effortlessly via the pre-built connectors of Cloud-Based ETL Tools like Hevo Data.
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- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
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How to Install Kafka on Mac?
To install Kafka on Mac, you can follow the easy steps given below:
Step 1: Install Java on Mac Manually or via HomeBrew
Before you begin to Install Kafka on Mac you should ensure that Java version 8+ is installed on your system. You can check that by running the java -version command on the terminal. If it is installed, you will get the following output:
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If you have not installed Java yet, then follow the steps given below to install Java on Mac:
- Step 1: Visit the Official Java Downloads Page and click on the download link for the .dmg file.
- Step 2: Double click the downloaded file and follow the installation instructions to install Java on Mac. After successful installation, you may delete the .dmg file for the setup.
If you have Homebrew installed on Mac, you install Java on Mac via these 2 simple commands:
$ brew tap caskroom/cask
$ brew cask install java
Step 2: Download & Install Kafka on Mac Manually or via HomeBrew
To Install Kafka on Mac Manually, follow these steps:
- Step 1: Visit the Official Apache Kafka Downloads page and click on the version you want to download under the Binary Downloads section.
- Step 2: After you click on the version link, Apache will provide a mirror link for downloading Kafka. Save the file to your desired location.
- Step 3: Open up the terminal and navigate to the folder where the downloaded Kafka file is present. You can now extract the contents of the file from the downloaded ‘tar’ File by the following commands:
$ tar -xzf kafka_2.13-3.0.0.tgz
$ cd kafka_2.13-3.0.0
Following these instructions completes the manual approach to Manually Download & Install Kafka on Mac. If you have Homebrew installed on your Mac, you can Install Kafka on Mac in a single step by running the following command:
$ brew install kafka
Step 3: Run ZooKeeper & Kafka
Currently, it is necessary to Run ZooKeeper alongside Kafka as it is responsible for Kafka’s Cluster Management. You don’t need to install ZooKeeper separately as it comes with the Apache Kafka Installation package. Though, as reported by Apache, Zookeeper will not be required by Kafka in the future versions.
- Step 1: From the root folder of Apache Kafka, you can run ZooKeeper by executing the following command:
$ bin/zookeeper-server-start.sh config/zookeeper.properties
On successful execution of this command, you will see an output as follows:
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- Step 2: Now, open a new terminal window & run the following command from the root of Apache Kafka to start the Kafka environment:
$ bin/kafka-server-start.sh config/server.properties
After this, you will see the output below confirming the successful run of the Kafka broker.
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Similarly, on the Zookeeper terminal, a message for a new session will be displayed. This completes the process to Install Kafka on Mac.
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Conclusion
In this article, you have learned how to effectively Install Kafka on Mac both Manually and via Homebrew. To Install Kafka on Mac and successfully run it, you need to ensure that the Java 8+ version is installed beforehand. Kafka provides an Open-Source environment for collecting, processing, storing, and analyzing data at scale in Real-Time. Due to its Flexibility, Reliability & Best-in-Class Performance, Kafka has several use cases in organizations globally such as Building Data Pipelines, Leveraging Real-Time Data Streams, enabling Operational Metrics, and Data Integration across countless sources.
For a more In-depth and Holitisic Analysis of Business Performance and Financial Health, it is essential to consolidate from Apache Kafka and all the other applications used across your business. However, to extract this complex data with everchanging Data Connectors, you would require to invest a section of your Engineering Bandwidth to Integrate, Clean, Transform & Load data to your Data Warehouse or a destination of your choice. On the other hand, a more Effortless & Economical choice is exploring a Cloud-Based ETL Tool like Hevo Data.
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Hevo Data, a No-code Data Pipeline can seamlessly transfer data from a vast sea of sources such as Apache Kafka & Kafka Confluent Cloud to a Data Warehouse or a Destination of your choice to be visualised in a BI Tool. It is a reliable, completely automated, and secure service that doesn’t require you to write any code!
If you are using Apache Kafka & Kafka Confluent Cloud as your Message Streaming Platform and searching for a Stress-Free Alternative to Manual Data Integration, then Hevo can effortlessly automate this for you. Hevo, with its strong integration with 100+ sources & BI tools(Including 40+ Free Sources), allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.
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