MongoDB announces the availability of MongoDB Atlas, the company's new database-as-a-service (DBaaS) offering.
The company wants to see if it can use its massive database technology to compete better with Amazon Web Services and Microsoft Azure, according to The Information news site.
Medical imaging leaders team up with IBM and its Watson cognitive capability to tackle cancer, diabetes, eye health, brain disease and heart disease.
The company's best news, as it has been for a while, was in the cloud sales revenue category, which was up 68 percent to $859 million in fiscal Q4.
LinkedIn continues its strategy of developing hot technology and open-sourcing it, this time a machine learning library for Spark called Photon ML.
At the Spark Summit, Databricks announced a new enterprise security framework for Apache Spark and made its data platform generally available.
Splice Machine is seeking contributors, mentors and sponsors to help support its move to take its dual-engine RDBMS open source.
Aimed at making the cloud more data-friendly, IBM's new Data Science Experience is a native Apache Spark platform for data scientists and developers.
In a move to advance data science in the enterprise, IBM has joined the R Consortium to better support the R programming language.
IBM and Cisco join forces to empower users to draw instant insights from their Internet of things devices at the edge of the network.
The financial manager accuses upper management of pushing her to "fit square data into round holes" to make Oracle Cloud Services' results look better.
The wait's over. Microsoft's cloud-enabled, analytics-friendly database is now generally available.
Datameer 6 provides a new user experience for iterative analytics and a re-architected, future-proof back end supporting Apache Spark.
Before big data and fast data, the challenge of data in motion was simple: move fields from fairly static databases to an appropriate home in a data warehouse, or move data between databases and apps in a standardized fashion. The process resembled a factory assembly line. In today's world, consuming applications and routes and rules for moving data constantly change. Big data processing operations are more like a city traffic grid than the linear path taken by traditional data. The emerging world is many-to-many, with streaming or micro-batched data coming from numerous sources and being consumed by numerous applications. Because modern data is so dynamic, dealing with data in motion requires a full lifecycle perspective including day-to-day operations and agility over time. Organizations must tune the performance of their data movement system as both data infrastructure and business requirements for the use of data evolve. Based on interviews with and white papers from big data infrastructure software provider StreamSets, this eWEEK slide show offers the following 10 best practices formanaging the performance of data movement as a system and eliciting maximum value.