5 Prominent Big Data Analytics Tools to Learn in 2020

Data, data and data. This seems to be what our world is swimming and immersing in. Why? The answer is simple: simply everything we use, such as mobile phones, and with it, all that it has, such as the social media, churn out unimaginable amounts of data.

Let us give you some perspective about data in our real world:

• Some 1.2 trillion searches are made on Google every year;
• Facebook users send out something like 31million+ messages and view more than 2.75 million videos every single minute;
• For just gathering data, American companies spent over $57 billion in 2017. It is no wonder that the Gartner forecast, made in 2012, that Big Data would account for six million jobs in the US in 2015, actually outgrew the estimates by a third;
• Five exabytes of information created from the dawn of history until 2003 is now created every two days globally!

While these figures are fascinating, why we need to be so obsessed with them is that all these gargantuan numbers could mean nothing if we did not now how to use this data. This is why the technology by which this is done, namely Data Analytics (or Big Data Analytics), is such a hot topic in the tech world today.

Like all other tools and technologies, Big Data Analytics is built on tools,
frameworks and processes. Which are these? Let us look at the 5 most prominent Big Data Analytics tools to learn in 2020:

Microsoft HDInsight:

A Spark and Hadoop cloud service, Azure HDInsight, Microsoft HDInsight comes in two categories for providing Big Data: Standard and Premium. In offering an enterprise-scale cluster that helps organizations run their big data workloads on; Microsoft HDInsight comes with the following features:

• Its analytics capability comes an industry-leading Service Level Agreement, which makes it highly trustworthy
• It security and monitoring are enterprise-grade
• It is versatile, since it offers data protection of assets on-premises and its governance controls are on the cloud
• Well suited for a number of areas, as its platform is suited for developers and scientists alike
  
  

Tableau Public

Another of the 5 prominent Big Data Analytics tools for 202; Tableau Public uses Tableau’s USP, data visualization, for analyzing Big Data. With a limit of a humongous million rows, Tableau Public is amazingly easy to use.

It has these prominent features:

• Its interactive data visualizations can be published on the www for free;
• It comes with utmost ease of use, as prior programming skills are not required to use it.

Apache Hadoop

This Java based Big Data Analytics tool is also free. This software framework makes it easy to store huge amounts of data effectively on the cloud through clusters. Not only can Apache Hadoop run in parallel on a cluster; it can also process insanely large amounts of data across all the nodes of the cluster seamlessly.

Apache Hadoop comes with these interesting features:

• Data processing is both fast and flexible with this tool
• Since the nodes can communicate to each other quickly, Apache Hadoop facilitates a very effective level of performance.

Oracle Analytics Cloud

The way in which Oracle Analytics Cloud has been growing in the past few years has been belying the industry thought that Big Data Analytics has never been Oracle’s forte. Its analytics cloud comes with these features:

• The USP of this platform lies in the fact that its strong infrastructure enables it to bring different sources of data together, by which it offers different kinds of automation capabilities separately for different types of analytics and Big Data analysis use-cases.
• It offers self-service Big Data Analytics, a mainstay of the analytics world.

Cloudera

Cloudera brings enterprise-class deployment of big Data Analytics, which it does through a combination of Apache Spark, Apache Hadoop, Apache Impala and many others. This platform aids in the collection, processing, administering, managing, discovery, modeling, and distribution of virtually unlimited data.

These are a coupe of its prominent features:

• Its distribution, as we have just seen, is vast and comprehensive
• Better security and governance, and greater ease of use and implementation.

Wrap up:

In our closing thoughts, we would like you take a look at some of the online courses that Simpliv offers on Big Data. We are confident you will find these very useful.

Please feel free to share your thoughts about this blog!