7 Must Know Big Data & Data Analytics Tools in 2019

Almost everything is being powered and enriched by Data today! Big data, the forearm of intelligent machines and devices in the 21st century, generated 10.6 Zettabytes of Global IP Traffic from cloud data centers in 2018, and almost 189.1 Billion in revenue from the business analytics services worldwide. Imagine the scope of this fast growing technical field in the upcoming years because these numbers are just the beginning.

If you are looking to switch to a highly rewarding career in tech, or want to advance your current profile to a higher notch, now would be the right time. It is, thus, highly recommended that you enroll yourself in a professional course in Big Data Analytics with PST Analytics because of their industrial repo for placements and top-notch faculty.

Also, if you are shifting to a career in Big Data and Analytics, the knowledge of the following tools will come in handy. Let us get started with a brief ‘What-How-Why’ of these!


The hands-on knowledge of Hadoop in your CV is one booster element that will put you ahead in the career race in this field. Hadoop is an open-source Framework crafted for the very purpose of storing mammoth amounts of data.  Hadoop is designed for excellent processing power to handle bulk data and possesses great redundancy which means no downtime due to hardware failure. To master Hadoop, it is crucial that one be thorough with Java language. Think of Hadoop as a very large ‘Data Warehousing’ system on which queries can be made for large datasets.


If your aptitude is pulling you towards Data analytics, perhaps Skytree will be a great tool for you to master. Skytree is Data Scientists’ one of the favorite analytics tool when it comes to predictive modelling. Skytree helps Data Scientists forge accurate predictive Machine Learning models that are seamless, high-performance and easy to use. Some of the notable features of Skytree are AI for Data Scientists, Scalable Algorithms, allow professionals to visualize the logic behind ML Decisions, allows interpretation of the models, offers predictive problem solving with data preparation capabilities, etc. Skytree is one of the most sought after analytics tool out there in the market.


When it comes to data, three things matter the most. They are, Volume, Variety and Velocity with the terms being self-explainable. MongoDB is an alternative to the existing databases that cannot handle high throughput inflowing data which is ‘Dynamic’ and ‘Variable’ in nature. Thus, MongoDB is a preferred Data Management system for Mobile apps, Content Management Systems, Social Media analytics, product catalogs, etc. Though, this tool comes with a learning curve starting from scratch. It will also demand that you know the basics of working on queries.

Apache Spark

Analytics tools favored by large organizations across the globe are certainly robust and reliable. Apache Spark is one of those tools. It is a powerful Big Data analytics tool which is open-source and offers 80 high-level operators that can be used to build parallel apps. Though, Apache Spark is majorly used by the organizations to process large datasets. It is also used to run applications in Hadoop cluster. Another noteworthy feature of Spark is the lightning fast processing and its ability to integrate with the existing Hadoop data. Spark provides in-built APIs coded in Scala, Java, and Python.


Thanks to Facebook for developing Cassandra as a NoSQL solution for the IT industry. Today, giants like Cisco, Twitter, Netflix and others use Cassandra to manage large volumes of streaming data. Cassandra is basically a distributed database designed to handle high throughput of data that flows in mass chunks and varies in nature. Cassandra is so designed that it has no scope of failure and is one of the most robust tools available out there to handle Big Data.


Integrating Big Data with applications and other databases can be a cumbersome task. Talend is a tool that makes it simple to do so. It is a renowned Big Data tool that automates Big Data integration, checks data quality and allows for seamless data management. Some of the qualities for which Talend is preferred for Big Data operations are simplified ETL and ELT ops, simplified MapReduce and Apache Spark, produced smarter quality data with ML and NLP, and provides agile DevOps ability to speed up projects of scale. Talend must be on your ‘To-Learn’ list as a Big Data professional.


Lumify is a strikingly multi-talented platform that allows for Big Data fusion, Visualization and Analysis. This tool helps Analytics professionals to identify correlations between different patterns between the data and analyze them via the analytics suite option. You can create automatic 2D and 3D graph visualization layouts, identify links between the entities on the graph, use ingest processing feature to sort textual data, videos, and images, and execute big data projects of large scale over it.

So, which tool would you like to learn for your next professional leap? Let us know in the comments below!