{"id":67024,"date":"2018-08-29T16:34:48","date_gmt":"2018-08-29T16:34:48","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=67024"},"modified":"2021-01-28T08:11:57","modified_gmt":"2021-01-28T08:11:57","slug":"real-time-big-data-pipeline","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/real-time-big-data-pipeline\/","title":{"rendered":"Real-time Big Data Pipeline with Hadoop, Spark & Kafka"},"content":{"rendered":"

Defined by 3Vs that are velocity, volume, and variety of the data, big data sits in a separate row from the regular data. Though big data was the buzzword for the last few years for data analysis, the new fuss about big data analytics is to build up a real-time big data pipeline. In a single sentence, to build up an efficient big data analytic system for enabling organizations to make decisions on the fly.<\/span><\/p>\n

In a real-time big data pipeline, you need to consider factors like real-time fraud analysis, log analysis, predicting errors to measure the correct business decisions.<\/span><\/p>\n

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Hence, to process such high-velocity massive data on a real-time basis, the highly reliable data processing system is the demand of the hour. There are many open-source tools and technologies available in the market to perform real-time big data pipeline operations. In this blog, we will discuss the most preferred ones – Apache Hadoop, Apache Spark, and Apache Kafka.<\/span><\/p>\n

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