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Four Tableau products a quick review and explanation

I want to share you what are the Products most popular.

Total four products. Read the details below.

Tableau desktop-(Business analytics anyone can use) - Tableau  Desktop  is  based  on  breakthrough technology  from  Stanford  University  that  lets  you drag & drop to analyze data. You can connect to  data in a few clicks, then visualize and create interactive dashboards with a few more.

We’ve done years of research to build a system that supports people’s natural  ability  to  think visually. Shift fluidly between views, following your natural train of thought. You’re not stuck in wizards or bogged down writing scripts. You just create beautiful, rich data visualizations.  It's so easy to use that any Excel user can learn it. Get more results for less effort. And it’s 10 –100x faster than existing solutions.

Tableau server
Tableau  Server  is  a  business  intelligence  application  that  provides  browser-based  analytics anyone can use. It’s a rapid-fire alternative to th…

How to use Machine learning skills for Internet of Things IoT

How to use Machine learning skills for Internet of Things IoT
#How to use Machine learning skills for Internet of Things IoT:
Big data relates to extremely large and complex data sets which are difficult to process using traditional means.

Machine Learning comprises algorithms which learn from data, make predictions based on their learning, and have the ability to improve their outcomes with experience. Due to the enormity of data involved with Machine Learning, various technologies and frameworks have been developed to address the same. Hadoop is an open-source framework targeted for commodity hardware to address big data scale.

The distributed design of the Hadoop framework makes it an excellent fit to crunch data and draw insights from it by unleashing Machine Learning algorithms on it.
So, the true value of IoT comes from ubiquitous sensors’ relaying of data in real-time, getting that data over to Hadoop clusters in a central processing unit, absorbing the same, and performing Machine Learning on data to draw insights; all at petabyte scale or more.

In reviewing the use cases and challenges from preceding sections, one thing is very clear. That is to do with quickness with which certain analytics must be performed. Imagine sending a critical alert late because computing could not be done any faster. Two key gaps here include absorbing incoming data at such a high rate reliably and in observing that Hadoop was not created for real time streaming data.
It was originally envisaged as a framework for batch processing. Innovators have responded to those challenges well. Let us review some of those technologies now.

SAP HANA with internet of things came into picture with real time processing of data compared to Hadoop which is only batch processing.

Iot Basics Part-1


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