What is Kafka Big Data? How it works, Benefits & Applications

Today, huge amounts of data are being generated by multiple devices, live streams, and systems every second, and more and more devices are connecting to the internet daily. The first need in Big Data applications is to solicit data from multiple sources and store them in a central repository.  Before that can be done, the data might need to be pre-processed, moved, and cataloged. Apache Kafka acts as a smart middle layer and decouples your diverse, real-time data pipelines. It can collect data from publisher sources like Web and Mobile Applications, Databases, Logs, Flume, Streams, Message-oriented middleware, etc. 

Apache Kafka is used for real-time streaming and analytics of big data. This article will introduce you to Apache Kafka and describe its key features. Furthermore, it will explain 3 major Kafka Big Data Applications that are widely used in business today. Read along to learn more about how Apache Kafka Big Data Functions can improve your business!

What is Apache Kafka?

Kafka is a distributed event streaming platform developed by Apache for high-throughput, fault-tolerant data processing. It is designed to handle real-time data feeds and stream processing. Kafka is capable of processing large-scale data streams in real-time, making it ideal for environments where fast, continuous data flow is essential.

Its ability to handle millions of messages per second allows businesses to analyze and respond to data instantly. Kafka’s distributed architecture also ensures scalability, allowing it to grow with the increasing volume of data while maintaining high availability and fault tolerance.

How Apache Kafka Works?

• A Kafka cluster is composed of multiple brokers (servers) working together to handle data streams, ensuring high availability and fault tolerance. Each broker stores a portion of the data, and the cluster automatically balances load and replicates data across brokers to maintain reliability and scalability.
• Kafka relies on a distributed architecture, consisting of brokers, producers, consumers, and zookeepers.
• Producers send messages to Kafka topics, while consumers read these messages.
• Kafka topics are partitioned and replicated across brokers for scalability and fault tolerance.
• Zookeeper ensures the coordination and management of Kafka brokers.

Introduction to Apache Kafka Big Data Function

Kafka is a stream-processing platform that ingests huge real-time data feeds and publishes them to subscribers in a distributed, elastic, fault-tolerant, and secure manner. Kafka can be easily deployed on infrastructures starting from bare metal to docker containers. 

Kafka can handle huge volumes of data and remains responsive, this makes Kafka the preferred platform when the volume of the data involved is big to huge. It’s reliable, stable, flexible, robust, and scales well with numerous consumers. 

Kafka can work seamlessly with most popular software like HBase, Flume, Spark for real-time ingesting and feed Data Warehouses/lakes like Hadoop, Azure, Redshift, etc. Kafka can be used for real-time analysis as well as to process real-time streams to collect Big Data. 

Key Features of Kafka Big Data Function

The following features make Kafka Big Data popular among the masses: 

• Data processed by Kafka can be fed into Cassandra, Hadoop, S3, Storm, Flink, etc. 
• Kafka can deliver data to real-time as well as batch systems, all at the same time without any performance degradation. 
• Kafka can be used as a scalable message store that can replicate quickly and provide very high throughput. 

Kafka Big Data Function Applications

Although Kafka has numerous applications, the following 3 applications are used most widely:

1. Using Kafka Big Data Function as a Data Source
2. Using Kafka Big Data Function as a Data Processor
3. Using Kafka Big Data Function to Re-sync Nodes

1) Using Kafka Big Data Function as a Data Source

The very first use Kafka is put into is as a Data Broker or Data source. Kafka is used here as a multi-subscription system. The same published data set can be consumed multiple times, by different consumers. Kafka’s built-in redundancy offers reliability and availability, at all times. 

One popular combination is to use Kafka with Hadoop and Spark, where Hadoop stores the data in HDFS and performs Data Analytics, and Kafka acts as the data aggregator and distributor. You will need to know Kafka internals and configure its parameters for the approach to work. 

2) Using Kafka Big Data Function as a Data Processor

The second role that Kafka can play is that of a Data processor and Analyzer. Kafka provides a client library for analyzing data called Kafka streams. It can be used to process and analyze data and send the results to an external application or back to Kafka core. 

Apart from common data transformation operations such as Map, Filter, Join, and Aggregations out of the box; Kafka streams allow you to connect your custom data processors, e.g. Java classes, to achieve the transformations you need. Apart from Kafka core acting as the internal messaging layer, Kafka streams have no other external dependency. 

To understand how Kafka streams work, we have to understand 3 basic concepts:

• Stream: It is an unbounded, continuously updating data set, which can be assumed to be an ordered, replayable and fault-tolerant sequence of immutable data records( or key-value pairs). A stream processor is a processing step that inputs data, one record at a time, applies some processing to it, and produces 1 or more output records, to be consumed by its downstream processors. 
• Time: The notion of time in Kafka Streams can be event time ( when an event occurs), ingestion time( when the event/data record was saved by Kafka with a timestamp), and processing time ( when the event was processed). Using these abstractions, Kafka will define some operations like windowing/periodic functions/aggregations. 
• State Stores: This is somewhat similar to an HTTP session in web applications. Here, Kafka streams allow you to process a record/event based on the state of its previous record OR its implications on the next record. This abstraction allows you to perform sophisticated stream processing like joining streams, grouping streams, and conditional processing. 

Kafka Streams offers fault-tolerance and automatic recovery for local state stores. Also, other processes, different from the one that created the state store, can query the state store through a feature called Interactive Queries. Kafka Streams can be connected to your Visualization tools, to generate the final charts/graphs for decision-makers. 

Hence, these features make Kafka streams a feature-rich and reliable data analytics engine. The downside here is that if your desired processing is not inbuilt in Kafka, you will need to program your own custom data processors. 

3) Using Kafka Big Data Function to Re-sync Nodes

Another important use Kafka can be put to is to re-sync your nodes( data stores) and restore the state. You can also use it for Log Aggregation, Messaging, Click-stream Tracking, Audit Trails, and much more. 

You can configure Kafka to retain records based on time limits, size-based limits, and compaction. 

Conclusion

In this article, we’ve explored Apache Kafka, its key features, and its three most popular big data applications: as a data source, a data processor, and for re-syncing nodes. Kafka is an essential tool for businesses looking to handle massive amounts of real-time data efficiently. However, managing and integrating data from various sources can still be complex. Hevo Data offers an ideal solution to streamline your data integration and ETL processes, without the need for complex coding.

With its easy-to-use interface and seamless integration with 150+ data sources, Hevo simplifies data management, making it an excellent choice for businesses looking to build modern, efficient data pipelines. Sign up for Hevo’s 14-day free trial and experience seamless data migration.

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