Easily Clone your Delta Lake for Testing, Sharing, and ML Reproducibility
Introducing Clones An efficient way to make copies of large datasets for testing, sharing and reproducing ML experiments We are excited to introduce a new capability in Databricks Delta Lake - table cloning. Creating copies of tables in a data lake or data warehouse has several practical uses. However, given the volume of data in...
Enabling Spark SQL DDL and DML in Delta Lake on Apache Spark 3.0
Last week, we had a fun Delta Lake 0.7.0 + Apache Spark 3.0 AMA where Burak Yavuz, Tathagata Das, and Denny Lee provided a recap of Delta Lake 0.7.0 and answered your Delta Lake questions. The theme for this AMA was the release of Delta Lake 0.7.0 coincided with the release of Apache Spark 3.0...
Time Traveling with Delta Lake: A Retrospective of the Last Year
Try out Delta Lake 0.7.0 with Spark 3.0 today! It has been a little more than a year since Delta Lake became an open-source project as a Linux Foundation project. While a lot has changed over the last year, the challenges for most data lakes remain stubbornly the same - the inherent unreliability of data...
Diving Into Delta Lake: Schema Enforcement & Evolution
Data, like our experiences, is always evolving and accumulating. To keep up, our mental models of the world must adapt to new data, some of which contains new dimensions - new ways of seeing things we had no conception of before. These mental models are not unlike a table's schema, defining how we categorize and...
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 Delta Lake transaction log is, how it works at the file level, and how...
Introducing Delta Time Travel for Large Scale Data Lakes
Data versioning for reproducing experiments, rolling back, and auditing data We are thrilled to introduce time travel capabilities in Databricks Delta, the next-gen unified analytics engine built on top of Apache Spark, for all of our users. With this new feature, Delta automatically versions the big data that you store in your data lake, and...
Benchmarking Structured Streaming on Databricks Runtime Against State-of-the-Art Streaming Systems
Update Dec 14, 2017: As a result of a fix in the toolkit’s data generator, Apache Flink's performance on a cluster of 10 nodes, multiple core cluster went from 6x slower than Apache Spark to 3x. This configuration is not examined in Apache Flink's post. In most uses cases, you will want to run streaming...
Running Streaming Jobs Once a Day For 10x Cost Savings
This is the sixth post in a multi-part series about how you can perform complex streaming analytics using Apache Spark. Traditionally, when people think about streaming, terms such as “real-time,” “24/7,” or “always on” come to mind. You may have cases where data only arrives at fixed intervals. That is, data appears every hour or...
Working with Complex Data Formats with Structured Streaming in Apache Spark 2.1
In part 1 of this series on Structured Streaming blog posts, we demonstrated how easy it is to write an end-to-end streaming ETL pipeline using Structured Streaming that converts JSON CloudTrail logs into a Parquet table. The blog highlighted that one of the major challenges in building such pipelines is to read and transform data...
New Features in Machine Learning Pipelines in Apache Spark 1.4
Apache Spark 1.2 introduced Machine Learning (ML) Pipelines to facilitate the creation, tuning, and inspection of practical ML workflows. Spark’s latest release, Spark 1.4, significantly extends the ML library. In this post, we highlight several new features in the ML Pipelines API, including: A stable API — Pipelines have graduated from Alpha! New feature transformers...
