It’s been one year since Yahoo open-sourced CaffeOnSpark so the tech giant has found a way to celebrate it — by open-sourcing TensorFlowOnSpark, its latest open source framework for distributed deep learning on big data clusters.
Reactive programming is gaining momentum but people are still reluctant to jump on the bandwagon. To help you overcome the fear of the unknown, we decided to ask Scala, Lagom, Spark, Akka and Play experts to explain how these elements coexist and work together to create a reactive universe. This JAX Magazine issue is packed with goodies — it’s our treat!
Each Monday we take a step back and analyze what has happened in the previous week. Last week we witnessed the launch of Prometheus 1.0, Mesos 1.0 and Spark 2.0, we discovered why Go is a beloved programming language and we dived deep into machine learning.
It’s been two years since the release of Apache Spark 1.0, and Databricks is now busy preparing Spark 2.0. To get a taste of what’s coming, the team announced the technical preview of Spark 2.0. Although it is not ready for production, its goal is to receive feedback from the community in time for the general availability release.
Yahoo is the latest tech giant to create a deep learning system for developing predictive applications such as image or speech recognition. CaffeOnSpark is able to perform ‘deep learning’ on the massive amount of data kept in the company’s Hadoop file system. The new internally-built software is now available on GitHub.
The fourth release for the 1.X line of Spark is here, with Apache Spark 1.3 introducing usability improvements alongside a new DataFrame API that is sure to provide powerful and convenient operators.
Typesafe found what they like to call a ‘golden nugget’ in some of their latest developer survey results and wanted to investigate the unexpected uptake of Apache Spark. Their responses paint a promising and exciting picture for Spark adoption.
In a pairing described as ‘magical’, Apache Spark and Cassandra have pooled their resources to deliver analytics up to “100 times” swifter in-memory, and 10 times speedier on disk.