How to Manage Python Dependencies in PySpark
Controlling the environment of an application is often challenging in a distributed computing environment - it is difficult to ensure all nodes have the desired environment to execute, it may be tricky to know where the user’s code is actually running, and so on. Apache Spark™ provides several standard ways to manage dependencies across the...
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...
How to Train XGBoost With Spark
XGBoost is currently one of the most popular machine learning libraries and distributed training is becoming more frequently required to accommodate the rapidly increasing size of datasets. To utilize distributed training on a Spark cluster, the XGBoost4J-Spark package can be used in Scala pipelines but presents issues with Python pipelines. This article will go over...
Improving the Spark Exclusion Mechanism in Databricks
Ed Note: This article contains references to the term blacklist, a term that the Spark community is actively working to remove from Spark. The feature name will be changed in the upcoming Spark 3.1 release to be more inclusive, and we look forward to this new release. Why Exclusion? The exclusion mechanism was introduced for...
Faster SQL: Adaptive Query Execution in Databricks
Earlier this year, Databricks wrote a blog on the whole new Adaptive Query Execution framework in Spark 3.0 and Databricks Runtime 7.0. The blog has sparked a great amount of interest and discussions from tech enthusiasts. Today, we are happy to announce that Adaptive Query Execution (AQE) has been enabled by default in our latest...
Analyzing Algorand Blockchain Data with Databricks Delta
Algorand is a public, decentralized blockchain system that uses a proof of stake consensus protocol. It is fast and energy-efficient, with a transaction commit time under 5 seconds and throughput of one thousand transactions per second. The Algorand system is composed of a network of distributed nodes that work collaboratively to process transactions and add...
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...
It’s an ESG World and We’re Just Living in it
The future of finance goes hand in hand with socially responsible investing, environmental stewardship, and corporate ethics. In order to stay competitive, Financial Services Institutions (FSI) are increasingly disclosing more information about their environmental, social, and corporate governance (ESG) performance. Hence the increasing importance of ESG ratings and ESG scores to investment managers and institutional...
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...
