Big Data Terminology: 16 Key Concepts Everyone Should Understand (Part I)

These definitions are for anyone who wants to know more about Big Data and of which they should have a general understanding. As-a-Service Infrastructure Data-as-a-service, software-as-a-service, platform-as-a-service, these all refer to the idea that rather than selling data, licences to use data, or platforms for running Big Data technology, it can be provided “as-a-service,” rather than as a distinct product.

Learning from Your Data: Essential Considerations

For any organization undergoing digital transformation, a primary consideration is how to find, capture, manage and analyze big data. They are looking to big data and data science to facilitate the discovery of analytics that will enable informed decision-making. CIOs have a responsibility to provide expertise in the area of analytics, as well as an understanding of how to provide

Anatomy of a Hadoop Project Failure

Several years ago, the educational technology company Blackboard selected Apache Hadoop to run a new data analytics application designed to turn data exhaust into actionable insight. Months later, the failed project was cancelled, and Blackboard implemented a hosted relational data warehousing product instead. The reasons behind Blackboard‘s initial selection of Hadoop for this project will sound familiar: a desire to

Meet Ray, the Real-Time Machine-Learning Replacement for Spark

Researchers at UC Berkeley’s RISELab have developed a new distributed framework designed to enable Python-based machine learning and deep learning workloads to execute in real-time with MPI-like power and granularity. Called Ray, the framework is ostensibly a replacement for Spark, which is seen as too slow for some real-world AI applications, and should be ready for production use in less

Hadoop Has Failed Us, Tech Experts Say 

The Hadoop dream of unifying data and compute in a distributed manner has all but failed in a smoking heap of cost and complexity, according to technology experts and executives who spoke to Datanami. “I can’t find a happy Hadoop customer. It’s sort of as simple as that,” says Bob Muglia, CEO of Snowflake Computing, which develops and runs a

Big Data Platforms in 2017: Leveraging Hybrid Clouds for Intelligent Operations

According to a recent Gartner survey, big data investments reached a possible peak in 2016. But these investments show signs of contracting, with 48 percent of companies having invested in big data in 2016 – only three percent more than 2015. This trend signals that organizations have delivered significant big data insights and will now look to operationalize in the

5 Hadoop Trends to Watch in 2017

Hadoop is synonymous with big data, providing both storage and processing resources for large and disparate data sources—not to mention a platform for third-party software vendors to build upon. Where will the distributed computing system go in 2017? Here are five macro trends impacting Hadoop to keep an eye out this year. Later this month, Hadoop will turn 11 years