The last decade saw the massive growth of big data. During that time, all the technologies did not change but there have been a lot of transformations. Cloud analytics, uses a range of analytical tools to help companies extract information from a massive amount of data and present it in a form that is readily available via web browser, has become popular among the companies with the emerging new data sources. With the need to store and process big data, a whole constellation of open source software such as Hadoop emerged, which is used to store and do a basic processing on big data and is also cheaper than a data warehouse for similar volumes of data. Scripting languages like Hive, Pig, and Python along with many open source tools like Spark are gaining much popularity. Read more at: https://hbr.org/2017/06/how-analytics-has-changed-in-the-last-10-years-and-how-its-stayed-the-same

One can use the big data, available in hand, in a right or a wrong way. Here is the list of top 10 worst big data practices which one should try to avoid. First, though MongoDB has an aggregation platform, it is not good as an analytical system and thus should not be used as big data platform. Second, RDBMS schema is used as files by many which should be avoided too. Third, creating a series of data points. Fourth, failing to develop use cases. Fifth, over-dependence on Hive should be reduced as the whole point of big data is to expand beyond what one could do with one technology. Sixth, it's not right to treat HBase like an RDBMS. Seventh, trying to install Hadoop and all its moving parts on 100 nodes by hands is also a worst practice. Eighth, one should also avoid RAID/LVM/SAN/VM-ing one's data nodes. Ninth, instead of treating HDFS as just a file system one needs to think about how one is going to secure all of this and for whom. Finally, everyone is free but each one should have a plan. Read more at:http://analytics.theiegroup.com/article/53c925453723a81857000073/The-10-Worst-Big-Data-Practices-

Horton Networks is giving an effort to fit Hive to work with the component that frees Hadoop from the tyranny of batch processing in order to bring Hadoop more into enterprise mainstream. With the release of Horton Networks Data platform 2.1, the company has completed that Hive interactive query capability, known as Stinger Phase 3 - and is simultaneously releasing it for both the Windows and Linux platforms. To know more about this big release, follow the article by Andrew J. Brust, a developer, consultant and entrepreneur in the software industry:

http://www.zdnet.com/hortonworks-announces-data-platform-2-1-7000027949/