From banks and stock exchanges to hospitals and factories, Event Streaming is employed in a range of businesses that demand Real-Time Data access. Apache Kafka is a prominent Real-Time Data Streaming Software that uses an Open-Source Architecture to store, read, and evaluate Streaming Data. The main architectural ideas of the Kafka Clusters were created in response to the rising demand for Scalable High-Throughput Infrastructures that can store, analyze, and reprocess Streaming data.
To handle event streams, Kafka includes High-Level methods for Transformations, Aggregations, Joins, Windowing, and other operations. Users can obtain High Throughput, Low Latency, Compute Power, and more by leveraging its distributed nature and managing enormous amounts of data with ease.
Apache Kafka works on several servers in a Distributed Architecture, allowing it to make use of the processing power and storage capacities of various systems. Its distributed structure and efficient system for managing incoming data makes it one of the most reliable tools for Real-Time Data Analysis and Data Streaming that a company can use.
In this article, you will learn about the basics of Apache Kafka as well as the Architecture of Kafka Clusters.
Table of contents
What is Apache Kafka?
Apache Kafka is a Distributed Open-Source System for Publishing and Subscribing to a large number of messages from one end to the other. Kafka makes use of the Broker concept to duplicate and persist messages in a fault-tolerant manner while also separating them into subjects.
Kafka is used for creating Real-Time Streaming Data Pipelines and Streaming Applications that convert and send data from its source to its destination.
It enables developers to build applications that continuously produce and consume streams of data records using a Message Broker to route messages from Publishers (systems that transform data from Data Producers into the necessary format) to Subscribers (systems that manipulate or analyze data in order to find alerts and insights and deliver them to Data Consumers).
Linkedin’s Engineering Team created Kafka in 2010 to track numerous activity events generated on a LinkedIn webpage or app, such as message exchanges, page visits, adverts served, and so on.
Today, Apache Kafka is a part of the Confluent Stream Platform and has made a name for itself in the industry, processing billions of events every day.
Apache Kafka is extremely quick and ensures that data records are accurate. These data records are kept in the order in which they appear inside Kafka Clusters, which can span several servers or even Data Centers. Apache Kafka duplicates these records and splits them so that a large number of users can access the information at the same time.
Key Features of Apache Kafka
Apache Kafka offers the following collection of intuitive features:
- Low latency: Apache Kafka has an extremely low end-to-end latency, up to 10 milliseconds, for large volumes of data. This means that a data record produced to Kafka may be retrieved by the Consumer in a short amount of time. As it decouples the received message, it enables customers to recover it at any time.
- Seamless messaging functionality: Due to its unique ability to decouple messages and store them effectively, Kafka has the ability to publish, subscribe, and process data records in Real-Time.
With such seamless messaging features, dealing with large amounts of data becomes straightforward and easy, offering business communications and scalability a significant advantage over traditional communication options.
- High Scalability: It refers to a system’s capacity to sustain its performance when subjected to variations in application and processing demands. The distributed design of Apache Kafka allows it to handle increased volumes and speeds of incoming messages. Therefore, Kafka may be scaled up and down without causing any downtime.
- High Fault Tolerance: Kafka is very fault-tolerant and reliable since it duplicates and distributes your data often to other servers or Brokers. If one of the Kafka servers goes down, the data will be available on other servers, which you may access easily.
- Multiple Integrations: Kafka can interface with a variety of Data-Processing Frameworks and Services, including Apache Spark, Apache Storm, Hadoop, and Amazon Web Services. You may effortlessly integrate Kafka’s benefits into your Real-Time Data pipelines by connecting them with different applications.
Hevo Data, a No-code Data Pipeline, helps load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. It supports 100+ Data Sources including Apache Kafka, Kafka Confluent Cloud, and other 40+ Free Sources. You can use Hevo Pipelines to replicate the data from your Apache Kafka Source or Kafka Confluent Cloud to the Destination system. It loads the data onto the desired Data Warehouse/destination and transforms it into an analysis-ready form without having to write a single line of code.
Hevo’s fault-tolerant and scalable architecture ensures that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. Hevo supports two variations of Kafka as a Source. Both these variants offer the same functionality, with Confluent Cloud being the fully-managed version of Apache Kafka.
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Let’s look at some of the salient features of Hevo:
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- Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
- Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer.
- Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
- Schema Management: Hevo can automatically detect the schema of the incoming data and map it to the destination schema.
- Connectors: Hevo supports 100+ Integrations to SaaS platforms such as Apache Kafka, Kafka Confluent Cloud, FTP/SFTP, Files, Databases, BI tools, and Native REST API & Webhooks Connectors. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake, Firebolt, Data Warehouses; Amazon S3 Data Lakes; Databricks; and MySQL, SQL Server, TokuDB, DynamoDB, PostgreSQL Databases to name a few.
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- 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.
- Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within Data Pipelines.
- Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
What is Kafka Clusters?
Kafka with more than one broker is called Kafka Cluster. It can be expanded and used without downtime. Apache Kafka Clusters are used to manage the persistence and replication of messages of data, so if the primary cluster goes down, other Kafka Clusters can be used to deliver the same service without any delay.
Kafka Clusters Architecture Explained: 5 Major Components
In a Distributed Computing System, a Cluster is a collection of computers working together to achieve a shared goal. A Kafka cluster is a system that consists of several Brokers, Topics, and Partitions for both.
The key objective is to distribute workloads equally among replicas and Partitions. Kafka Clusters Architecture mainly consists of the following 5 components:
A Kafka Topic is a Collection of Messages that belong to a given category or feed name. Topics are used to arrange all of Kafka’s records. Consumer apps read data from Topics, whereas Producer applications write data to them.
In Kafka, Topics are segmented into a customizable number of sections called Partitions. Kafka Partitions allow several users to read data from the same subject at the same time.
The Partitions are arranged in a logical sequence. When configuring a Topic, the number of divisions is provided, however, it may be adjusted later.
The Partitions that make up a Topic are dispersed among the servers of the Kafka Clusters. Each server in the cluster is in charge of its own data and Partition requests. When a Broker receives the messages, it also receives a key.
The key can be used to indicate which Partition a message should be sent to. Messages with the same key are sent to the same Partition. This allows several users to read from the same Topic at the same time.
The Kafka Server is known as Broker, which is in charge of the Topic’s Message Storage. Each of the Kafka Clusters comprises more than one Kafka Broker to maintain load balance. However, since they are stateless, ZooKeeper is used to preserve the Kafka Clusters state.
It’s usually a good idea to consider Topic replication when constructing a Kafka system. As a result, if a Broker goes down, its Topics’ duplicates from another Broker can fix the situation.
A Topic with a Kafka Replication Factor of 2 will have one additional copy in a separate Broker. Further, the replication factor cannot exceed the entire number of Brokers accessible.
The Consumer Clients’ details and Information about the Kafka Clusters are stored in a ZooKeeper. It acts like a Master Management Node where it is in charge of managing and maintaining the Brokers, Topics, and Partitions of the Kafka Clusters.
The Zookeeper keeps track of the Brokers of the Kafka Clusters. It determines which Brokers have crashed and which Brokers have just been added to the Kafka Clusters, as well as their lifetime.
Then, it notifies the Producer or Consumers of Kafka queues about the state of Kafka Clusters. This facilitates the coordination of work with active Brokers for both Producers and Consumers.
Zookeeper also keeps track of which Broker is the subject Partition’s Leader and gives that information to the Producer or Consumer so they may read and write messages.
Within the Kafka Clusters, a Producer Sends or Publishes Data/Messages to the Topic. Different Kafka Producers inside an application submit data to Kafka Clusters in order to store a large volume of data.
It is important to note that the Kafka Producer delivers messages as quickly as the Broker can handle them. It does not wait for the Broker to acknowledge them.
When a Producer adds a record to a Topic, it is published to the Topic’s Leader. The record is appended to the Leader’s Commit Log, and the record offset is increased. Each piece of data that comes in will be piled on the cluster, and Kafka only exposes a record to a Consumer when it has been committed.
Hence, it is crucial that Producers must first obtain metadata about the Kafka Clusters from the Broker before sending any records. The Zookeeper metadata identifies which Broker is the Partition Leader, and a Producer always writes to the Partition leader.
A Kafka Consumer is someone who Reads or Consumes the Kafka Clusters Messages. Typically, Consumers have the option of reading messages starting at a certain offset or from any offset point they desire. As a result, customers can join the Kafka Clusters at any moment.
In Kafka, there are two categories of Consumers. The first is the Low-Level Consumer, which specifies Topics and Partitions as well as the offset from which to read, which can be either fixed or variable.
Next, we have High-Level Consumer (or Consumer groups), which consist of one or more Consumers.
The Broker will distribute messages based on which Consumers should read from which Partitions, as well as keeping track of the group’s offset for each Partition. It keeps track of this by requiring all customers to declare which offsets they have handled.
In this blog, you learned about Apache Kafka Clusters architecture, including concepts like Topics, Broker, Producers, and Consumers. Thousands of organizations use Apache Kafka to solve their big data problems. Understanding Kafka Clusters Architecture can assist you to better handle streams of data and implement data-driven applications effectively.
Maintaining the seamless working of the Apache Kafka in your Data Pipeline can be a Time-Consuming and Resource Intensive Task. You would have to spend a section of your Engineering Bandwidth to Develop, Design, Monitor and Maintain your Data Pipelines to extract complex data from Apache Kafka and all the applications used across your business. Alternatively, a more Efficient & Economical choice is employing a Cloud-Based ETL Tool like Hevo Data.
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