Burning Through Electronic Health Records in Real Time With Smolder
In previous blogs, we looked at two separate workflows for working with patient data coming out of an electronic health record (EHR). In those workflows, we focused on a historical batch extract of EHR data. However, in the real world, data is continuously inputted into an EHR. For many of the important predictive healthcare analytics...
ルールベースと AI モデルの組み合わせによる金融詐欺対策
The financial services industry (FSI) is rushing towards transformational change, delivering transactional features and facilitating payments through new digital channels to remain competitive. Unfortunately, the speed and convenience that these capabilities afford also benefit fraudsters. Fraud in financial services still remains the number one threat to organizations’ bottom line given the record-high increase in overall...
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...
ACID Transactions on Data Lakes Tech Talks: Getting Started with Delta Lake
As part of our Data + AI Online Meetup, we’ve explored topics ranging from genomics (with guests from Regeneron) to machine learning pipelines and GPU-accelerated ML to Tableau performance optimization. One key topic area has been an exploration of the Lakehouse. The rise of the Lakehouse architectural pattern is built upon tech innovations enabling the...
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...
Integrating large-scale Genomic Variation and Annotation Data with Glow
Genomic annotations augment variant data by providing context for each change in the genome. For example, annotations help answer questions like does this mutation cause a change in the protein-coding sequence of a gene? If so, how does it change the protein? Or is the mutation in a low information part of the genome, also...
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...
Announcing Databricks Labs Terraform integration on AWS and Azure
We are pleased to announce integration for deploying and managing Databricks environments on Microsoft Azure and Amazon Web Services (AWS) with HashiCorp Terraform. It is a popular open source tool for creating safe and predictable cloud infrastructure across several cloud providers. With this release, our customers can manage their entire Databricks workspaces along with the...
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...
