8 Big Data Tools You need to Know
Big Data has become an integral part of businesses today and companies are increasingly looking for people who are familiar with Big Data analytics tools. Employees are expected to be more competent in their skill sets and showcase talent and thought processes that would complement the organizations’ niche responsibilities. The so-called in-demand skills that were popular so far have been done away with and if there’s something hot today, it’s Big Data analytics.
We’ve been mentioning a lot about upskilling and switching to analytics to tackle this retrenchment season and this article will help you further understand about the tools you need to master to become a skilled data scientist companies are looking for. So, if you’re someone looking to switch to Big Data analytics and confused on the tools you should learn to make a successful jump, here’s a comprehensive list to consider.
Big Data is sort of incomplete without Hadoop and expert data scientists would know that. An open-source framework, Hadoop offers massive storage for all kinds of data. With its amazing processing power and capability to handle innumerable tasks, Hadoop never allows you to ponder over hardware failure. Though you need to know Java to work with Hadoop, it’s worth every effort. Knowing Hadoop will put you ahead in the recruitment race.
MongoDb is a contemporary alternative to databases. It’s the best for working on data sets that vary or change frequently or the ones that are semi or unstructured. Some of the best uses of MongoDB include storage of data from mobile apps, content management systems, product catalogs and more. Like Hadoop, you can’t get started with MongoDB instantly. You need to learn the tool from scratch and be aware of working on queries.
Used by industry players like Cisco, Netflix, Twitter and more, it was first developed by the social media giant Facebook as a NoSQL solution. It’s a distributed database that is high-performing and deployed to handle mass chunks of data on commodity servers. Cassandra offers no space for failure and is one of the most reliable Big Data tools.
It’s an open-source framework that allows experts to work on interactive analyses of large scale datasets. Developed by Apache, Drill was designed to scale 10,000+ servers and process in seconds petabytes of data and millions of records. It supports tons of file systems and databases such as MongoDB, HDFS, Amazon S3, Google Cloud Storage and more.
This open-sourced enterprise search engine is developed on Java and released under the license of Apache. One of its best functionalities lies in supporting data discovery apps with its super-fast search capabilities.
HCatalog allows users to view data stored across all Hadoop clusters and even allows them to use tools like Hive and Pig for data processing, without having to know where the datasets are physically present. A metadata management tool, HCatalog also functions as a sharing service for Apache Hadoop.
One of the best workflow processing systems, Oozie allows you to define a diverse range of jobs written or programmed across multiple languages. Moreover, the tool also links them to each other and conveniently allows users to mention dependencies.
Last but definitely not the least, Storm supports real-time processing of unstructured data sets. It is reliable, fault-proof and is compatible with any programming language. Hailing from the Apache family of tools, Twitter now owns Storm as an open-sourced real-time distributed computing framework.
So, these were the eight powerful tools you need to master if you are keen on switching to Big Data analytics. If you’re unsure of how to get started with them, remember that there are online courses that will help you specialize on these tools and become certified experts as well. With the time being right, master the tools and switch to a rewarding career today.