Introducing Click: The Command Line Interactive Controller for Kubernetes
Click is an open-source tool that lets you quickly and easily run commands against Kubernetes resources, without copy/pasting all the time, and that easily integrates into your existing command line workflows. At Databricks we use Kubernetes, a lot. We deploy our services (of which there are many) in unique namespaces, across multiple clouds, in multiple...
Introducing Low-latency Continuous Processing Mode in Structured Streaming in Apache Spark 2.3
Structured Streaming in Apache Spark 2.0 decoupled micro-batch processing from its high-level APIs for a couple of reasons. First, it made developer’s experience with the APIs simpler: the APIs did not have to account for micro-batches. Second, it allowed developers to treat a stream as an infinite table to which they could issue queries as...
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,...
