Apache Flink | A Real Time & Hands-On course on Flink
Apache Flink | A Real Time & Hands-On course on Flink
A Complete In-depth and HANDS-ON practical course on a technology better than Spark for Stream processing – Apache Flink
Created by J Garg – Real Time Learning | 6 hours on-demand video course
Apache Flink is the successor to Hadoop and Spark. It is the next generation Big data engine for Stream processing. If Hadoop is 2G, Spark is 3G then Apache Flink is the 4G in Big data stream processing frameworks. Actually Spark was not a true Stream processing framework, it was just a makeshift to do it but Apache Flink is a TRUE Streaming engine with added capacity to perform Batch, Graph, Table processing and also to run Machine Learning algorithms. Apache Flink is the latest Big data technology and is rapidly gaining momentum in the market. It is assumed that same like Apache Spark replaced Hadoop, Flink can also replace Spark in the coming near future.
Demand of Flink in market is already swelling. Many big companies from multiple Industry domains have already started using Apache Flink to process their Real-time Big data and thousands other are diving into.
What’s included in the course ?
- Complete Apache Flink concepts explained from Scratch to Real-Time implementation.
- Each and Every Apache Flink concept is explained with a HANDS-ON Flink code of it.
- Includes even those concepts, the explanation to which is not very clear even in Flink official documentation.
- For Non-Java developer’s help, All Flink Java codes are explained line by line in such a way that even a non -technical person can understand.
- Flink codes and Datasets used in lectures are attached in the course for your convenience.
- All the codes are updated with latest Flink version.
- Implement 3 Real-time Case Studies using Flink.
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Who this course is for:
- Students who want to learn Apache Flink from SCRATCH to its Live Project Implementation.
- Who are new to Stream processing and want to learn a Stream processing framework which is better than Spark.
- Software engineers who feel they missed an early opportunity to get into Hadoop & Spark.
- Hadoop & Spark Developers who want to upgrade themselves to Apache’s latest Big Data Streaming Engine.
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