Apache Kafka is a distributed Publish-Subscribe Messaging platform explicitly designed to handle Real-time Streaming data. It helps in Distributed Streaming, Pipelining, and replay of data feeds for quick, scalable workflows. In today’s disruptive tech era, raw data needs to be processed, reprocessed, evaluated and managed in real-time.
Apache Kafka has proved itself as a great asset when it comes to performing message streaming operations. The main architectural ideas of Kafka were created in response to the rising demand for Scalable high-throughput infrastructures that can store, analyze, and reprocess streaming data.
In this article, you will gain information about GCP Kafka. You will also gain a holistic understanding of Apache Kafka, Google Cloud Platform, their key features, the need for installing GCP Kafka, and the steps for installing GCP Kafka. Read along to find out in-depth information about GCP Kafka.
Table of Contents
What is Apache Kafka?
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Apache Kafka was originally developed at LinkedIn to address their need for Monitoring Activity Stream Data and Operational Metrics such as CPU, I/O usage, and request timings. Subsequently, in early 2011, it was Open-Sourced through the Apache Software Foundation. Apache Kafka is a Distributed Event Streaming Platform written in Java and Scala. It is a Publish-Subscribe (pub-sub) Messaging Solution used to create Real-Time Streaming Data Pipelines and applications that adapt to the Data Streams.
Kafka deals with Real-Time volumes of data and swiftly routes it to various consumers. It provides seamless integration between the information of producers and consumers without obstructing the producers and without revealing the identities of consumers to the producers.
Kafka Core concepts:
- Producer: An application that sends data (message records) to the Kafka server.
- Consumer: An application that receives data from the Kafka server in the form of message records.
- Broker: A Kafka Server that acts as an agent/broker for message exchange.
- Cluster: A collection of computers that each run one instance of the Kafka broker.
- Topic: The data stream is given an arbitrary name.
- Zookeeper: A server/broker that stores a large number of shared pieces of information.
You can also have a look at Kafka documentation.
Key Features of Apache Kafka
Apache Kafka provides the following features such as communicating through messaging and stream processing to enable real-time data storage and analysis.
- Persistent messaging: Any type of information loss cannot be tolerated in order to gain real value from big data. Apache Kafka is built with O(1) Disc Structures that deliver constant-time performance even with very high volumes of stored messages (in the TBs).
- High Throughput: Kafka was designed to work with large amounts of data and support Millions of Messages per Second.
- Distributed event streaming platform: Apache Kafka facilitates Message Partitioning across Kafka servers and distributing consumption over a cluster of consumer systems while ensuring per-partition ordering semantics.
- Real-time solutions: Messages created by producer threads should be instantly available to consumer threads. This characteristic is essential in event-based systems like Complex Event Processing (CEP).
What is Google Cloud Platform?
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Google Cloud Platform (GCP) provides computing resources necessary for developing and deploying applications on the web. By creating an application using the platform, Google automatically keeps track of all its resources, including Storage, Processing Power, and Network Connectivity. Instead of leasing a server or a DNS address, as is the case with normal websites with GCP, you pay for the resources used by your application.
Key Features of Google Cloud Platform
Some of the key features of Google Cloud Platform are as follows:
- Big Data: GCP offers a dedicated Big Data solution for clients with such needs. Some of the features include BigQuery, which allows users to run SQL-like commands on large chunks of data.
- Hosting: GCP offers two hosting solutions for customers: the AppEngine, the Platform-as-a-Service, and Compute Engine that acts as Infrastructure-as-a-Service.
- Containers: These come in handy for PaaS applications since they help boost app deployment.
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What is the need for Installing GCP Kafka?
Despite its many benefits, Apache Kafka is a difficult technology to implement. In production, on-premises Kafka clusters are difficult to set up, scale, and manage. You must provision machines and configure Kafka when establishing an on-premises infrastructure to run Kafka. You must also design the distributed machine cluster to ensure availability, ensure data storage and security, set up monitoring, and carefully scale data to accommodate load changes. The infrastructure must then be maintained by replacing machines as they fail and performing routine patching and upgrading.
An alternative approach is to use Kafka as a Cloud-managed service, such as GCP Kafka. The Kafka infrastructure is provisioned, built, and maintained by a third-party vendor such as Google. You are in charge of developing and running the applications. This makes it simple to deploy Kafka without the need for specialized Kafka infrastructure management knowledge. You devote less time to infrastructure management and more time to creating value for your company. As a result, GCP can be used as a third-party vendor, leading to GCP Kafka Installation.
Installation of GCP Kafka
It is assumed that you already have access to Google Cloud Account.
You can follow these steps to install a single node GCP Kafka VM.
- Step 1: Log in to your GCP account.
- Step 2: Go to the “GCP products and services” menu i.e, the hamburger icon at the top left corner present at the top left corner of the window.
- Step 3: Click on the “Cloud Launcher” option.
- Step 4: In the search bar that appears, search for Kafka.
- Step 5: You will see multiple options.
- For a single node setup, you can the Google VM Image.
- You can also try the Bitnami single node image.
- There is also a multi-node Bitnami Image. They did, however, design it for production use with larger VM configurations. The Google image will suffice for your learning and initial purposes.
Image Source: Self
- Step 6: Select the Kafka VM Image.
- Step 7: In the window that appears, on scrolling down you will come across the Kafka version, Operating system, and other packages.
- Step 8: While your GCP Kafka usage is free, there is a cost associated with the VM for CPU, Memory, and disc space. However, Google charges you on an hourly basis. You also get a year of free credit.
- Step 9: Click the “Launch on Compute Engine” button.
- Step 10: In the next window, you can review and change some settings, but the default settings are adequate.
- Step 11: Scroll down to the bottom of the page and click the “Deploy” button. You must now wait a few minutes for GCP to start your single node GCP Kafka VM.
- Step 12: From your deployment page, you can SSH to the GCP Kafka VM, or you can go to your homepage by clicking on the hamburger icon in the top left corner, then to the compute engine page, and SSH to your GCP Kafka VM.
- Step 13: When you’re finished with your work, select the GCP Kafka VM and stop it. Your billing will be terminated. You can return the following day, select the GCP Kafka VM, and restart it.
- Step 14: Your GCP Kafka VM is already preconfigured. All services are operational. You can begin using it immediately.
Conclusion
In this article, you have learned about GCP Kafka Installation. This article also provided information on Apache Kafka, Google Cloud Platform, their key features, the need for installing GCP Kafka, and the steps for installing GCP Kafka in detail. For further information on Kafka Debezium Event Sourcing, Azure Kafka Integration, Apache Kafka Queue, you can visit the following links.
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Manisha is a data analyst with experience in diverse data tools like Snowflake, Google BigQuery, SQL, and Looker. She has hadns on experience in using data analytics stack for various problem solving through analysis. Manisha has written more than 100 articles on diverse topics related to data industry. Her quest for creative problem solving through technical content writing and the chance to help data practitioners with their day to day challenges keep her write more.