Top 5 Big Data Frameworks Developer Should Learn

If one of your goals is to learn about Big Data (and you are looking for information on the best Big Data Frameworks), then you have come to the right place.

Previously, I shared the best Big Data online courses, and today, I am going to share the top 5 Big Data frameworks which you can learn.

Given the ever-increasing abundance of data, Big Data Analysis is a very valuable skill to have. Both Fortune 500 and small companies are looking for competent people who can derive useful insights from their huge piles of data. That's where Big Data frameworks like Apache Hadoop, Apache Spark, Flink, Storm, and Hive can help.

Companies like Amazon, eBay, Netflix, NASA JPL, and Yahoo all use Big Data frameworks (like Spark) to quickly extract meaning from massive data sets across fault-tolerant Hadoop clusters.

Learning how to use these frameworks and techniques can provide you with a competitive advantage.

You can pick and choose what to learn by considering your needs, your experience, and your programming language preference because most of the Big Data frameworks can support major programming languages (Python, Java, and Scala).

Top 5 Big Data Frameworks You can Learn

Without wasting any more of your time, here is a list of the top 5 Big Data frameworks you can learn now.

Each of these frameworks provides different functionalities and knowing what they do is essential for any Big Data programmer.

1. Apache Hadoop

You may have heard about Hadoop clusters. For many people, Apache Hadoop and Big Data are interchangeable, and why not? Apache Hadoop is probably the most popular Big Data Framework out there.

Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers while using simple programming models.

It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.

It's based upon the popular MapReduce pattern and is key for developing a reliable, scalable, and distributed software computing application.

If you want to start mastering Big Data, I highly recommend you learn Apache Hadoop. I recommend you get your training from The Ultimate Hands-On Hadoop course by none other than Frank Kane on Udemy. It's one of the most comprehensive, yet up-to-date courses to learn Hadoop online.

2. Apache Spark

If you want to get ahead in the Big Data space, learning Apache Spark can be a great start.

Apache Spark is a fast, in-memory data processing engine with elegant and expressive development APIs. This allows data workers to efficiently execute streaming, machine learning, or SQL workloads that require fast iterative access to datasets.

You can use Spark for in-memory computing for ETL, machine learning, and data science workloads.

If you want to learn Apache Spark, I highly recommend you join Apache Spark 2.0 with Java -Learn Spark by a Big Data Guru on Udemy.

If you need more options to explore Spark with other programming languages like Scala and Python then Frank Kane's Apache Spark with Scala --- Hands On with Big Data! and Taming Big Data with Apache Spark and Python --- Hands-On! courses are definitely worth looking at.

3. Apache Hive

Apache Hive is a Big Data Analytics framework that was created by Facebook to combine the scalability of one of the most popular Big Data frameworks.

You can also think of Apache Hive as a data processing tool on Hadoop. It is a querying tool for HDFS and the syntax of its queries is similar-ish to SQL.

Apache Hive is an open-source software that lets programmers analyze large data sets on Hadoop. It is an engine that turns SQL requests into chains of MapReduce tasks.

If you are learning Hadoop then it makes sense to learn Hive as well and I highly recommend Hive to ADVANCE Hive (Real-time usage): Hadoop querying tool course by J Garg. It's an advanced course to learn Hive.

4. Apache Storm

Apache Storm is a Big Data Framework that is worth learning about. This framework is focused on working with a large flow of data in real-time. The key features of Storm are scalability and quick recovery after downtime.

Apache Storm is to real-time stream processing as what Hadoop is to batch processing.

Using Storm, you can build applications that need to be highly responsive to the latest data and can react to requests within seconds or minutes.

For example, it can be used in applications such as those needed in finding the latest trending topics on Twitter or those needed in monitoring spikes in payment gateway failures.

From simple data transformations to applying machine learning algorithms, you can work with Storm with the help of Java, Python, and Ruby.

If you want to learn Apache Storm, I suggest the Learn By Example: Apache Storm course by Loony Corn on Udemy.

5. Apache Flink

Apache Flink is another robust Big Data processing framework that works for stream and batch processing and is worth learning about.

It is the successor to Hadoop and Spark. It is a 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 initially used as a makeshift platform for stream processing. Apache Flink however, is a true streaming engine with added capacity to perform batch, graph, table processing, and also to run machine-learning algorithms.

The demand for Flink in the market is already increasing. Many renowned companies like Capital One (bank), Alibaba (eCommerce), Uber (transportation) have already started using Apache Flink to process their massive amounts of data in real-time, and thousands of others are diving into it.

If you want to learn Apache Flink, I suggest you start with Apache Flink | A Real-Time & Hands-On course on Flink by J Garg on Udemy. It's a complete, In-depth & HANDS-ON practical course to learn Apache Flink.

In conclusion, the above covered are the 5 best Big Data frameworks you can learn.

These frameworks are really useful and in-demand. Learning them can improve your skills and boost your resume thus advancing your career.

If the five aforementioned frameworks aren’t enough to satisfy your data appetite, Apache Heron is a new and shiny Big Data processing engine. Twitter developed it as a new generation replacement for Storm.

Thanks for reading this article. If you enjoyed this piece, then please share it with your friends and colleagues. If you have any questions or feedback, then please drop me a line.

P.S. - If you want to become a full stack developer and are looking for the best Java framework a full stack developer should learn then I suggest you join the Go Java Full Stack with Spring Boot and React course by Ranga Karnam on Udemy. It's an excellent course to take.

Previously published here