Selected Sessions to Watch for at Spark + AI Summit 2018
Early last month, we announced our agenda for Spark + AI Summit 2018, with over 180 selected talks with 11 tracks and training courses. For this summit, we have added four new tracks to expand its scope to include Deep Learning Frameworks, AI, Productionizing Machine Learning, Hardware in the Cloud, and Python and Advanced Analytics....
Introducing Stream-Stream Joins in Apache Spark 2.3
Since we introduced Structured Streaming in Apache Spark 2.0, it has supported joins (inner join and some type of outer joins) between a streaming and a static DataFrame/Dataset. With the release of Apache Spark 2.3.0, now available in Databricks Runtime 4.0 as part of Databricks Unified Analytics Platform, we now support stream-stream joins. In this...
Announcing Machine Learning Model Export in Databricks
In recent years, machine learning has become ubiquitous in industry and production environments. Both academic and industry institutions had previously focused on training and producing models, but the focus has shifted to productionizing the trained models. Now we hear more and more machine learning practitioners really trying to find the right model deployment options. In...
Apache Spark 2.3 with Native Kubernetes Support
This is a community blog from Anirudh Ramanathan and Palak Bhatia, software engineer and product manager respectively at Google, working in the Kubernetes team. They are part of the group of companies that contributed to native Kubernetes support for the Apache Spark 2.3. This post is cross-posted on blog.kubernetes.io Kubernetes and Big Data The open...
Introducing Apache Spark 2.3
Today we are happy to announce the availability of Apache Spark 2.3.0 on Databricks as part of its Databricks Runtime 4.0. We want to thank the Apache Spark community for all their valuable contributions to Spark 2.3 release. Continuing with the objectives to make Spark faster, easier, and smarter, Spark 2.3 marks a major milestone...
Accelerate Innovation with Microsoft Azure Databricks
It’s hard to believe that we are already three weeks into 2018. If you’re still struggling to get valuable insights from your data, now is the perfect time to try something new! We recently announced Azure Databricks, a fast, easy and collaborative Apache® Spark™ based analytics platform optimized for Azure. With Azure Databricks, you can help your...
Matei Zaharia’s 5 predictions about big data and AI in 2018
Over the past few years, the demand for artificial intelligence (AI) and machine learning capabilities has surged with innovations in natural language processing, task automation, and predictions. From autonomous cars to a more personalized shopping experience, big data and artificial intelligence is at the forefront of new solutions that are delighting customers, improving business operations...
Meltdown and Spectre: Exploits and Mitigation Strategies
In an earlier blog post, we analyzed the performance impact of Meltdown and Spectre on big data workloads in the cloud. In this blog post, we explain these exploits, their mitigation strategies and how they impact Databricks from a security and performance perspective. Meltdown Meltdown breaks a fundamental assumption in operating system security: an...
Meltdown and Spectre’s Performance Impact on Big Data Workloads in the Cloud
Last week, the details of two industry-wide security vulnerabilities, known as Meltdown and Spectre, were released. These exploits enable cross-VM and cross-process attacks by allowing untrusted programs to scan other programs’ memory. On Databricks, the only place where users can execute arbitrary code is in the virtual machines that run Apache Spark clusters. There,...
Databricks Cache Boosts Apache Spark Performance
We are excited to announce the general availability of Databricks Cache, a Databricks Runtime feature as part of the Unified Analytics Platform that can improve the scan speed of your Apache Spark workloads up to 10x, without any application code change. In this blog, we introduce the two primary focuses of this new feature: ease-of-use...
