At the 10th birthday of Hadoop, which is fast becoming everyone’s favorite big data technology – is gearing up for enterprise wide adoption. According to Forrester Wave: Big Data Hadoop Distributions, Q1 2016, within a span of 2 years, 100% of the large enterprises will adopt Hadoop or other Hadoop technologies like Apache Spark.
Hadoop is open source, but there are several Hadoop vendors in the market, who create enterprise relevant extension son Hadoop to make it more compatible for enterprise adoption. MapR’s Sr. Vice President of product management, Anil Gadre, said that there is a lot of scope for foundational innovation in any open source framework. This helps customers gain a competitive edge.
When Apache Hive 1.2 was launched last May, it supported the SQL Union functionality that is important for querying in Hadoop. After that every new version that was released like Spark MLib, Impala Project all supported the Hadoop SQL dialects. Looker allows for larger dialects in the Hadoop ecosystem to support business intelligence.
The popular Hadoop vendor – Hortonworks has reported an annual increase in revenue of up to 165%. While this is all good, their expenses still outweigh the sales revenue. The Hadoop vendor is still young and is rapidly growing. While their costs are more extensive than their revenue, their executive estimate that in the Q4 of 2016, after adjusting EBITDA, they will break even.
In the 10 years that Hadoop has been a part of the Big Data community, it has undergone several changes and modifications to suit the current Big Data need. Currently Hadoop is in the phase of Hadoop enterprise adoption where it is accessible to all business units.
A Research and Markets report – “World Hadoop Market, Opportunities and Forecasts, 2014 – 2021” – states that not only is the Big Data and Hadoop market on the rise, but it is actually getting deeply entrenched into critical industries like banking and the government. This report also predicts that by 2021, the global Hadoop market’s revenue will rise to $84.6 bn.
Springone2GX is designed to keep the developers up-to-date on the latest big data technologies and upgrades. With the adoption of Spring cloud technology for Apache Hadoop, workflows with MapReduce, Hive and Pig is set to become much easier and Spring will provide portability across Cloudera, Hortonworks and MapR distributions.
Yahoo has open sourced some of its key Artificial Intelligence technologies. Last year, Yahoo built a library called CaffeOnSpark which is to perform the popular AI of deep learning on the vast data present in their Hadoop file systems. CaffeOnSpark is written primarily in C++ and was developed to take advantage of Apache Spark which can perform certain computations faster than Hadoop.
For the complete list of big data companies and their salaries- CLICK HERE