Whats the first thing that comes to your mind when you think of “Big Data technologies”? . And what do think are the top ? Big data analytics tools for 2018 that are set to change the data analysis process.
For many, it’s an indefinite term that draws an image of huge servers, or some data which is big enough for humans to fathom. In real terms, Big Data technologies are so much deeper and broader than that. And it’s this term alone, that is helping several big companies in drive new revenue streams, gain a competitive advantage over business rivals and improve cost and operational efficiency.
Big data made simple – Looking at a wide array of uses of Big Data, I believe there are 5 different major areas in which Big data technologies can prove to be of very high value.
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Understanding and Targeting Customers
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Personal Quantification and Performance Optimisation
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Optimising Machine and Device Performance
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Financial Trading
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Understanding and Optimising Business Processes
It’s the right set of tools that make us conquer the world – To get the most out of the analytics process, its important choose the correct tool and be aware of top big data analytics tools and their working and usages.
I bring to you a list of top 5 analytics tools for 2018 that can help you in analyzing data and saving your effort.
Tableau Public : One of the most intuitive and the simplest tools available in the market for the analysts is Tableau Public. A Free service tool, Tableau Public lets you investigate a hypothesis, explore the data and check your intuitions at lightning speed. This data analytics tool communicates insights through data visualization which can be downloaded and be shared through social channels our email.
Rapid Miner: An open source analytics platform having a core of Java programming language provides machine learning procedures and data mining including data visualization, processing, statistical modeling, deployment, evaluation, and predictive analytics.
Known for its commercial and business applications, RapidMiner provides an integrated environment for business analytics, predictive analysis, data mining, and machine learning.
Few limitations that RapidMiner faces is the size constraints with respect t number of rows and the huge requirement of hardware resources, much more than ODM and SAS for the same task and data.
Hadoop: One simply can’t talk about big data without mentioning Big Data Hadoop . The Apache distributed data processing software is known for the ability to process extremely large data at high speeds in both structured as well as unstructured formats.
Apache Spark, Apache Storm, Apache Tez and MapReduce are different hadoop execution engines (software that runs on every server – node – cluster) that parallelize the execution of analytics application steps across the cluster.
R – Programming: R isn’t just a software, but also a high level statistical programming language used for statistical analysis, graphics representation and reporting. Project R is the software designed as a data mining tool written in R language and is widely used among data miners for developing statistical software and data analysis. R is worlds most widely used statistics programming language which is simple, well – developed and effective and includes conditionals, loops, user defined recursive functions and input and output facilities.
Catch a quick lesson about Project R and R language by clicking here!
IBM Big Data : Consisting of a solution portfolio IBM Watson Explorer, Infosphere streams, Infoshpere Big insights, IBM Smart Analytics System – IBM Big data Analytics solution offers its users the Next gen architecture for big data and analytics that delivers business insights that can help them in driving growth, reducing costs and reduce data storage.
So, these were my pick for the top 5 Analytics tools for 2018, let me know what tools you have been using for analyzing data.