If you want to master Big Data in 2022, but are unsure where to begin or which Big Data framework you should learn, then this is the place for you. I shared earlier the top Big Data courses online. Today, I will share the top 5 Big Data Frameworks that you can learn in 2022.
Big Data Analysis is an increasingly valuable skill in today's world due to the ever-increasing amount of data.
Fortune 500 companies and small businesses are both looking for people with the ability to extract useful insights from huge amounts of data. Big Data Frameworks such as Apache Spark, Flink and Storm can be of great help.
Amazon, NetFlix and NASA JPL are just a few of the companies that use Big Data frameworks such as Spark to extract meaning quickly from large data sets across a faulttolerant Hadoop cluster. Learning these frameworks and techniques can give you a competitive edge.
The 5 Best Big Data Frameworks for Java Developers in 2022
These are the top five Big Data Frameworks you can learn by 2022. Every framework has its own functionalities, so it is important to know what each one does for Big Data Programmers.
1. Apache Hadoop
Hadoop clusters are another term that might be familiar to you if you've heard of Big Data. Apache Hadoop, which is often synonymous with Big Data, is the most widely used Big Data Framework.
Apache Hadoop allows distributed processing large data sets across multiple computers by using simple programming models.
It can scale from one server to thousands of machines and offer local storage and computation. It is based on the Map Reduce pattern, which is crucial for creating a reliable, scalable and distributed software computing app.
If you are looking to get started with Big Data in 2022 then I strongly recommend Apache Hadoop. Frank Kane, the Udemy instructor, has The Ultimate Hands-On Hadoop Course. This is the best online course for learning Hadoop.
2. Apache Spark
Another popular Big Data framework is Apache Spark, which is growing in popularity every day. Learning Apache Spark 2022 is a great way to break into the Big Data Space.
Apache Spark is an in-memory, fast data processing engine that uses expressive APIs. This allows data workers to efficiently execute streaming, machinelearning, and SQL workloads that need fast iterative access.
Spark can be used for in-memory computing to perform ETL, machine learning and data science workloads with Hadoop. You can learn Apache Spark 2.0 with Java from Udemy if you are looking for a resource to help you in 2022.
3. Apache Hive
Apache Hive, a Big Data Analytics Framework that Facebook created to combine the scalability and popularity of Big Data frameworks.
Apache Hive can also be considered a Hadoop data processing tool. It's a querying tool for HDFS, and its syntax is very similar to our old SQL.
Hive, an open-source software that allows programmers to analyze large data sets using Hadoop, is called Open Source Software. It's an engine that transforms SQL-requests in MapReduce task chains.
It makes sense to learn Hadoop if you're learning it. If you have the resources to do so, then I recommend Hive to ADVANCE Hive. This is a great course for learning Hive, but it's not an easy one.
4. Apache Storm
Apache Storm is another Big Data Framework worth learning in 2022. This framework is designed to work with large amounts of real-time data. Storm's key features are scalability, and fast recovery from downtime.
Apache Storm is to stream processing real-time what Hadoop was to batch processing.
Storm allows you to build applications that are highly responsive to new data and respond within seconds or minutes. This includes monitoring spikes in payments gateway failures and finding the most trending topics on Twitter.
You can do simple data transformations or apply machine learning algorithms. This solution can be used with Java, Ruby, Python and Fancy. You can learn Apache Storm by Loony Corn at Udemy.
5. Apache Flink
Apache Flink, a robust Big Data processing framework that supports stream and batch processing, is something worth learning in 2022. It replaces Spark and Hadoop. It is the next-generation Big data engine for stream processing.
Hadoop is 2G, Spark 3G, and Apache Flink 4G in Big Data stream processing frameworks.
Spark was not a true stream processing framework. It was a makeshift to do it. Apache Flink, however, is a TRUE streaming engine with additional capacity to process Batch, Graph and Table processing as well as to run Machine Learning algorithms.
Flink is in high demand. Many well-known companies, such as Capital One (Bank), Alibaba(eCommerce), Uber Transport (Transportation), have already begun using Apache Flink for processing their Real-time Big Data. Thousands more are looking into it.
Thank you for reading this article. These Big Data Frameworks are a great resource for your colleagues and friends. Drop us a line if you have any feedback or questions.
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