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What Happened to Hadoop? And Where Do We Go from Here?

Apache Hadoop emerged on the IT scene in 2006 with the promise to provide organizations with the capability to store an unprecedented volume of data using cheap, commodity hardware. Hadoop facilitated data lakes were accompanied by a number of independent open source compute engines – and on top of that, “open source” meant free! What could go wrong?

How Hadoop Can Help Your Business Manage Big Data

Hadoop. Once largely unknown, hit the scene in part due to the explosion of unstructured data. Download the new white paper, “Making the Most of Your Investment in Hadoop,” through which SQREAM explores an approach to Hadoop that aims to help businesses reduce time-to-insight, increase productivity, empower data teams for better decision making, and increase revenue.

Making the Most of Your Investment in Hadoop

Hadoop is a popular enabler for big data. But with data volumes growing exponentially, analytics have become restricted and painfully slow, requiring arduous data preparation. Often, querying weeks, months, or years of data is simply infeasible. Download the whitepaper to learn how to deal with the exponential growth of data, reduce time spent on data preparation, and generate insights faster from ad-hoc queries of raw data.

The Future of Open Source Big Data Platforms

Three well-funded startups – Cloudera Inc., Hortonworks Inc., and MapR Technologies Inc. — emerged a decade ago to commercialize products and services in the open-source ecosystem around Hadoop, a popular software framework for processing huge amounts of data. The hype peaked in early 2014 when Cloudera raised a massive $900 million funding round, valuing it […]

Cloudera + Hortonworks: A Marriage from the Edge to AI

Did you hear the clap of thunder in the big data ecosystem today? If so, it was only just Cloudera, Inc. and Hortonworks, Inc. jointly announcing that they have entered into a definitive agreement under which the companies will combine in an all-stock merger of equals. The transaction, which has been unanimously approved by the Boards of Directors of both companies, will create the world’s leading next generation data platform provider.

Hadoop-as-a-Service: The Need Of The Hour For Superior Business Solutions

In this contributed article, content writer Swamini Kulkarni discusses how the launch of new platforms based on HaaS solutions demonstrate that Hadoop-as-a-Service (HaaS) is a promising solution for building and managing the big data cluster, which will compel organizations to consider Hadoop as a potential solution for big data challenges.

Interview: Jamie Engesser, VP Product Mangement at Hortonworks

I recently caught up with Jamie Engesser, VP Product Mangement at Hortonworks during the company’s DataWorks Summit 2018 conference in San Jose, California, to get an update on his company’s direction and his sense for the pulse of the big data industry.

Hortonworks Data Platform 3.0 Enables Containerization and Deep Learning Workloads

Hortonworks, Inc.® (NASDAQ: HDP), a leading provider of global data management solutions, today announced Hortonworks Data Platform (HDP) 3.0, which delivers significant new enterprise features including containerization for faster and easier deployment of applications, and increased developer productivity. The new version of HDP enables customers to more quickly, reliably and securely get value from their data at scale to drive business transformation.

Hadoop 3.0 Perspectives by Hortonwork’s Hadoop YARN & MapReduce Development Lead, Vinod Kumar Vavilapalli

In the Q&A below, , Hortonwork’s Hadoop YARN & MapReduce Development Lead, Vinod Kumar Vavilapalli, offers his perspectives on the recent release of Hadoop 3.0, the latest version of the Open Source software framework for reliable, scalable, distributed computing.

AtScale Brings its Universal Semantic Layer to the AWS Cloud

AtScale announced the preview availability of its universal semantic platform for business intelligence (BI) on Amazon Redshift. With this offer, enterprises will gain faster time to insight by deploying Big Data Analytics on the Amazon Cloud and benefit from an enhanced ROI by running production-ready workloads on the cost-effective Amazon cloud platform.