エンジニアリングのブログ

Ray is an open-source project first developed at RISELab that makes it simple to scale any compute-intensive Python workload. With a rich set of libraries and integrations built on a flexible distributed execution framework, Ray brings new use cases and simplifies the development of custom distributed Python functions that would normally be complicated to create....

Genomic technologies are driving the creation of new therapeutics, from RNA vaccines to gene editing and diagnostics. Progress in these areas motivated us to build Glow, an open-source toolkit for genomics machine learning and data analytics. The toolkit is natively built on Apache Spark™, the leading engine for big data processing, enabling population-scale genomics. The...

Hassle Free Data IngestionDiscover how Databricks simplifies semi-structured data ingestion into Delta Lake with detailed use cases, a demo, and live Q&A. Register for the Webinar Ingesting and querying JSON with semi-structured data can be tedious and time-consuming, but Auto Loader and Delta Lake make it easy. JSON data is very flexible, which makes it...

This is a guest authored post by Stephanie Mak, Senior Data Engineer, formerly at Intelematics.   This blog post offers my experience of contributing to the open source community with Bricklayer, which I'd started during my time at Intelematics. Bricklayer is a utility for data engineers whose job is to farm jobs, build map layers...

The Oakland Athletics baseball team in 2002 used data analysis and quantitative modeling to identify undervalued players and create a competitive lineup on a limited budget. The book Moneyball, written by Michael Lewis, highlighted the A’s ‘02 season and gave an inside glimpse into how unique the team’s strategic data modeling was, for its time....

Sentiment analysis is commonly used to analyze the sentiment present within a body of text, which could range from a review, an email or a tweet. Deep learning-based techniques are one of the most popular ways to perform such an analysis. However, these techniques tend to be very computationally intensive and often require the use...

We are excited to announce the availability of Apache Spark™ 3.2 on Databricks as part of Databricks Runtime 10.0. We want to thank the Apache Spark community for their valuable contributions to the Spark 3.2 release. The number of monthly maven downloads of Spark has rapidly increased to 20 million. The year-over-year growth rate represents...

This post is the third in a series  on Bayesian inference ([1], [2] ). Here we will illustrate how to use managed MLflow on Databricks to perform and track Bayesian experiments using the Python package PyMC3. This results in systematic and reproducible experimentation ML pipelines that can be shared across data science teams due to...

When working with cyber security data, one thing is for sure: there is no shortage of available data sources. If anything, there are too many data sources with overlapping data. Your traditional SIEM (security information and event management) tool is not really fit to handle the complex transformation and stitching of multiple data sources in...

Runbot is a bespoke continuous integration (CI) solution developed specifically for Databricks' needs. Originally developed in 2019, Runbot incrementally replaces our aging Jenkins infrastructure with something more performant, scalable, and user friendly for both users and maintainers of the service. This blog post will explore the motivations behind developing Runbot, the core design decisions that...