In the latest of a string of cloud upgrades, Google on Thursday revealed automated data-processing features that the company hopes will encourage users to adopt its cloud for their big data and ...

Google today formally took the wraps off Cloud Dataflow, the hosted offering designed to allow developers with average Java and Python skills to build sophisticated analytic “pipelines” that process ...

But Dataflow is a little different in that Google is offering it solely as a cloud service, something that anyone can access over the internet.

With Cloudera DataFlow for the Public Cloud, customers can automate complex data flow operations, improve the operational efficiency of streaming data flows with auto-scaling capabilities, and ...

The company on Dec. 18 released a Java software development kit for Cloud Dataflow into the open-source community as part of what it described as an effort to spur application development around ...

In a simple batch processing test, Google Cloud Dataflow beat Apache Spark by a factor of two or more, depending on cluster size ...

With Cloud Dataflow — in its ideal state — data analysts will be able use the same system for creating their pipelines, no matter the underlying architecture they want to run them on.

Google announced Cloud Dataflow last June as a managed service designed to help companies ingest and analyze huge data sets both in batch processing and in real-time streaming mode.

Google is launching a couple of updates to its cloud-based big data products at the Hadoop Summit in Brussels today. These include the launch of the open beta of Cloud Dataflow, Google's new ...

Mark Pollack introduces Spring Cloud Data Flow enabling one to create pipelines for data ingestion, real-time analytics and data import/export, demoing apps that are deployed onto multiple runtimes.

Significantly, Google Cloud Dataflow is meant to replace MapReduce, the software at the heart of Hadoop and other big data processing systems. MapReduce was originally developed by Google and ...