Get Your Free Copy of Delta Lake: The Definitive Guide (Early Release)
At the Data + AI Summit, we were thrilled to announce the early release of Delta Lake: The Definitive Guide, published by O’Reilly. The guide teaches how to build a modern lakehouse architecture that combines the performance, reliability and data integrity of a warehouse with the flexibility, scale and support for unstructured data available in...
Introducing Delta Sharing: An Open Protocol for Secure Data Sharing
Data sharing has become critical in the modern economy as enterprises look to securely exchange data with their customers, suppliers and partners. For example, a retailer may want to publish sales data to its suppliers in real time, or a supplier may want to share real-time inventory. But so far, data sharing has been severely...
How We Launched a Podcast: Lessons, (Minor) Mishaps & Key Takeaways
After six episodes featuring amazing leaders and practitioners in the data and AI community, we wrapped up season 1 of Data Brew by Databricks, our homegrown podcast hosted by us two - Denny and Brooke. This season focused on all things lakehouses - combining the key features of data warehouses, such as ACID transactions, with...
Attack of the Delta Clones (Against Disaster Recovery Availability Complexity)
Notebook: Using Deep Clone for Disaster Recovery with Delta Lake on Databricks For most businesses, the creation of a business continuity plan is crucial to ensure vital services, such as data stores, remain online in the event of a disaster, emergency or other issue. For many, it is mission critical that data teams can still...
Automatically Evolve Your Nested Column Schema, Stream From a Delta Table Version, and Check Your Constraints
We recently announced the release of Delta Lake 0.8.0, which introduces schema evolution and performance improvements in merge and operational metrics in table history. The key features in this release are: Unlimited MATCHED and NOT MATCHED clauses for merge operations in Scala, Java, and Python. Merge operations now support any number of whenMatched and whenNotMatched...
Natively Query Your Delta Lake With Scala, Java, and Python
Today, we’re happy to announce that you can natively query your Delta Lake with Scala and Java (via the Delta Standalone Reader) and Python (via the Delta Rust API). Delta Lake is an open-source storage layer that brings reliability to data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch...
Diving Into Delta Lake: DML Internals (Update, Delete, Merge)
In previous blogs Diving Into Delta Lake: Unpacking The Transaction Log and Diving Into Delta Lake: Schema Enforcement & Evolution, we described how the Delta Lake transaction log works and the internals of schema enforcement and evolution. Delta Lake supports DML (data manipulation language) commands including DELETE, UPDATE, and MERGE. These commands simplify change data...
An Update on Project Zen: Improving Apache Spark for Python Users
Apache Spark™ has reached its 10th anniversary with Apache Spark 3.0 which has many significant improvements and new features including but not limited to type hint support in pandas UDF, better error handling in UDFs, and Spark SQL adaptive query execution. It has grown to be one of the most successful open-source projects as the...
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...
