Diving Into Delta Lake: Unpacking The Transaction Log
The transaction log is key to understanding Delta Lake because it is the common thread that runs through many of its most important features, including ACID transactions, scalable metadata handling, time travel, and more. In this article, we’ll explore what the transaction log is, how it works at the file level, and how it offers...
AutoML on Databricks: Augmenting Data Science from Data Prep to Operationalization
Thousands of data science jobs are going unfilled today as global demand for the talent greatly outstrips supply. Every day, businesses pay the price of the data scientist shortage in missed opportunities and slow innovation. For organizations to realize the full potential of machine learning, data teams have to build hundreds of predictive models a...
MLflow, TensorFlow, and an Open Source Show
This summer, I interned on the ML Platform team. I worked on MLflow, an open-source machine learning management framework. This blog post details the projects I worked on, and my experience at Databricks overall. The automatic logging feature I developed makes it easier for data scientists to track their training sessions, without having to change...
How (Not) To Scale Deep Learning in 6 Easy Steps
Introduction: The Problem Deep learning sometimes seems like sorcery. Its state-of-the-art applications are at times delightful and at times disturbing. The tools that achieve these results are, amazingly, mostly open source, and can work their magic on powerful hardware available to rent by the hour in the cloud. It's no wonder that companies are eager...
Productionizing Machine Learning with Delta Lake
Try out this notebook series in Databricks - part 1 (Delta Lake), part 2 (Delta Lake + ML) For many data scientists, the process of building and tuning machine learning models is only a small portion of the work they do every day. The vast majority of their time is spent doing the less-than-glamorous (but...
Deep Learning on Medical Images at Population Scale: On-Demand Webinar and FAQ Now Available!
On June 26th, we hosted a live webinar — Deep Learning on Medical Images at Population-scale— with members of the data science and engineering teams from Human Longevity Inc (HLI), a leader in medical imaging and genomics. During the webinar, HLI shared how they use MRI images, whole-genome sequencing data, and other clinical data sets...
Announcing the Delta Lake 0.3.0 Release
We are excited to announce the release of Delta Lake 0.3.0 which introduces new programmatic APIs for manipulating and managing data in Delta tables. The key features in this release are: Scala/Java APIs for DML commands - You can now modify data in Delta tables using programmatic APIs for Delete (#44), Update (#43) and Merge...
Network performance regressions from TCP SACK vulnerability fixes
Update on Aug 2, 2019: Added to the end explaining our kernel patch and additional details we found. On June 17, three vulnerabilities in Linux’s networking stack were published. The most severe one could allow remote attackers to impact the system’s availability. We believe in offering the most secure image available to our customers, so...
Announcing Databricks Runtime 5.5 with Conda (Beta)
Databricks is pleased to announce the release of Databricks Runtime 5.5 with Conda (Beta). We introduced Databricks Runtime 5.4 with Conda (Beta), with the goal of making Python library and environment management very easy. This release includes several important improvements and bug fixes as noted in the latest release notes [Azure|AWS]. We recommend all users...
Fast Parallel Testing at Databricks with Bazel
The Databricks Developer Tools team recently completed a project to greatly speed up the pull-request (PR) validation workflows for many of our engineers: by massively parallelizing our tests, validation runs that previously took ~3 hours now complete in ~40 minutes. This blog post will dive into how we leveraged the Bazel build tool to achieve...
