Hadoop requires specialized skills. It is relative of course, but in the last couple of years, as many companies have deployed significant Hadoop infrastructure, there has been a lot said and written on this point. It takes substantial time and technical resources to get Hadoop up and running at most companies. An article in the Wall Street Journal, citing a 2015 Gartner analyst survey on Hadoop adoption and use, reported that “implementation and deployment hurdles [of Hadoop] inside big companies aren’t unheard of, and some CIOs have noted they are taking a cautious approach to Hadoop adoption.” Around the same time, Fortune.com made a similar point on a shortcoming of skills and early successes.
Still, a lot of work has been done to ease deployment. The Hadoop vendors have done their part to make the platform easier to administer. And, we at Teradata have offered a Hadoop appliance, preconfigured and optimized specifically with the goal of running enterprise class big data workloads easier. So, with Hadoop adoption on a good trajectory, a lot of attention turned to the tools to extract insights from data in Hadoop. From the early days of MapReduce and Hive, to newer SQL-on-Hadoop tools like Presto, to the rise of Apache Spark, the community, including our developers and engineers at Teradata, has taken some good, iterative steps to make it easier to analyze Hadoop data.
Today, we at Teradata are taking the next iterative step by announcing Teradata Aster Analytics on Hadoop 7.0 [link to press release] will execute natively inside the Hadoop cluster, now integrated fully with YARN and managed by the YARN resource manager. This is an important step in raising the use and value of Hadoop for companies that have invested so much in the infrastructure. With Aster Analytics on Hadoop, companies can deploy the best of Aster with pre-packaged and powerful analytics on any new and existing Hadoop cluster. It gives our customers even more choice and lets them realize ROI from Hadoop data that they might not be fully realizing today.
Aster Analytics on Hadoop is all about connecting analysts with big data at scale. As mentioned in the open, most Hadoop deployments today are way too technical, making it very hard for the standard business analyst to access the data, beyond simple BI and reporting use cases. Advanced and algorithmic analytics to find new insights and predict future outcomes requires hard-to-find data science skills. Aster Analytics’ simplified approach to advanced analytics, now running natively in Hadoop, gives analysts the expanded SQL repertoire to do path analysis for understanding the customer journey to conversion, graph analytics to find influencer networks, machine learning on sensor data to predict part failures and many other advanced analytic use cases directly against Hadoop data.
Hadoop can be tricky. But, with Teradata Aster Analytics on Hadoop 7.0 we are taking an important step in helping our customers more easily unlock value from big data.