Big Data Hadoop & Spark

Apache Kafka – An Introduction

Large data volume companies like Netflix, Microsoft, LinkedIn, and others use Kafka to process huge volumes of data. It is no wonder that Kafka is hugely popular and expanding on the technological horizon like no other software used for data analytics.

How does Kafka fit in Big Data?  

Big Data’s data lakes are filled and fed by Kafka’s data streams. Kafka Streams analyse and process data stored in Kafka as a client library. It scales efficient management concepts like processing time, event time, windowing support, and querying of application state. Kafka Streams have a low entry barrier enabling writing and running of POC’s on a single machine requiring running more instances to scale up workloads. 

What Is Kafka?

The Kafka platform for distributed streaming is useful where streams of data in Big Data are subscribed to and published. Kafka permits storage that is tolerant of faults and replicates data to multiple servers through the log partitions allowing applications to process records as and when they happen. 

By compressing and batching records and using IO diligently, Kafka is used for fast data stream decoupling where it finds application in streaming data into applications, data lakes, analytics involving systems with real-time streaming.

What Is Kafka Used For?

Kafka is used to collecting big data, for real-time data streaming to multiple servers and or perform analysis of real-time data. Among its popular uses are tracking website activity, processing data streams, collecting of key metrics, aggregation of logs, command-query-responsibility-segregation, CEP, ingesting data into Hadoop, Spark etc., real-time analytics, error recovery, message replays, and a whole range of micro-services like guaranteeing distributed-commit-logs of the data in memory.

What Is Apache Kafka?

Apache Kafka is a subscribe-publish distributed messaging system originally developed by LinkedIn and later merged into the Apache project. Kafka is by design a distributed, scalable, fast, partitioned commit log service which is replicated to multi-servers.

The Apache Kafka Database:

Apache Kafka is a Java and Scala written stream-processing open-source software platform developed by the Apache Software Foundation. It provides a low-latency high-throughput unified platform for handling real-time database feeds.

Apache Kafka design shares its architecture, features and components with most databases and speeds up the workload handling.

Apache Kafka architecture consists of many components.

These Kafka components are part of the architecture. A particular type of stream of messages is known as a Topic. Producers publish messages to a topic. Such messages which are published are stored in Kafka Clusters or Brokers which are a set of servers. A consumer can subscribe and consume published messages of a Topic using the data pulled from the Brokers.

Apache Kafka getting started involves a many-stepped method to install and run Apache Kafka and Zookeeper on an OS like Windows.

Kafka operations mean the

  • Cluster deployment to production with best practices and configurations that either should not or should be changed
  • Post-deployment activities and logistics like backup, rolling restart etc
  • Analyzing and monitoring a cluster’s statistics, interpreting, understanding normal behavior and alarm causes

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