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 Kafka Big Data Functions can improve your business!
Table of Contents
Introduction to 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.
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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
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.
The article introduced you to Apache Kafka and listed its key features. Moreover, it explained the 3 most popular applications of Kafka Big Data Function. The article provided a thorough discussion of each application and you can consider using them for your Big Data needs, owing to Kafka’s interoperability and feature-rich abilities.
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