Introducing Built-in Image Data Source in Apache Spark 2.4
Introduction With recent advances in deep learning frameworks for image classification and object detection, the demand for standard image processing in Apache Spark has never been greater. Image handling and preprocessing have their specific challenges - for example, images come in different formats (eg., jpeg, png, etc.), sizes, and color schemes, and there is no...
Apache Avro as a Built-in Data Source in Apache Spark 2.4
Apache Avro is a popular data serialization format. It is widely used in the Apache Spark and Apache Hadoop ecosystem, especially for Kafka-based data pipelines. Starting from Apache Spark 2.4 release, Spark provides built-in support for reading and writing Avro data. The new built-in spark-avro module is originally from Databricks’ open source project Avro Data...
Introducing Databricks Runtime 5.0 for Machine Learning
Six months ago we introduced the Databricks Runtime for Machine Learning with the goal of making machine learning performant and easy on the Databricks Unified Analytics Platform. The Databricks Runtime for ML comes pre-packaged with many ML frameworks and enables distributed training and inference. Today we are excited to release the second iteration including Conda...
MLflow v0.8.0 Features Improved Experiment UI and Deployment Tools
Last week we released MLflow v0.8.0 with multiple new features, including improved UI experience and support for deploying models directly via Docker containers to the Azure Machine Learning Service Workspace. Now available on PyPI and with docs online, you can install this new release with pip install mlflow as described in the MLflow quickstart guide....
Introducing HorovodRunner for Distributed Deep Learning Training
Today, we are excited to introduce HorovodRunner in our Databricks Runtime 5.0 ML! HorovodRunner provides a simple way to scale up your deep learning training workloads from a single machine to large clusters, reducing overall training time. Motivated by the needs of many of our users who want to train deep learning models on datasets...
Announcing Databricks Runtime 5.0
We’re excited to announce the general availability of Databricks Runtime 5.0. Included in this release is Spark 2.4. This release offers substantial performance increases within key areas of the platform. Benchmarking workloads have shown a 16% improvement in total execution time and Databricks Delta benefits from substantial improvements to metadata caching, improving query latency by...
Introducing New Built-in and Higher-Order Functions for Complex Data Types in Apache Spark 2.4
Apache Spark 2.4 introduces 29 new built-in functions for manipulating complex types (for example, array type), including higher-order functions. Before Spark 2.4, for manipulating the complex types directly, there were two typical solutions: 1) Exploding the nested structure into individual rows, and applying some functions, and then creating the structure again 2) Building a User...
Applying your Convolutional Neural Network: On-Demand Webinar and FAQ Now Available!
On October 25th, we hosted a live webinar—Applying your Convolutional Neural Network—with Denny Lee, Technical Product Marketing Manager at Databricks. This is the third webinar of a free deep learning fundamental series from Databricks. In this webinar, we dived deeper into Convolutional Neural Networks (CNNs), a particular type of neural networks that assume that inputs...
Open Sourcing Databricks Integration Tools at Edmunds
This is a guest post from Shaun Elliott, Data Engineering Tech Lead and Sam Shuster, Staff Engineer at Edmunds. What is Databricks and How is it Useful for Edmunds? Databricks is a cloud-based, fully managed, big data and analytics processing platform that leverages Apache SparkTM and the JVM. The big selling point of the Databricks Unified...
Introducing Apache Spark 2.4
UPDATED: 11/19/2018 We are excited to announce the availability of Apache Spark 2.4 on Databricks as part of the Databricks Runtime 5.0. We want to thank the Apache Spark community for all their valuable contributions to the Spark 2.4 release. Continuing with the objectives to make Spark faster, easier, and smarter, Spark 2.4 extends its...
