Databricks Labs are projects created by the field
to help customers get their use cases into production faster!
You can use the Databricks Terraform provider to manage your Databricks workspaces and the associated cloud infrastructure using a flexible, powerful tool. Databricks customers are using the Databricks Terraform provider to deploy and manage clusters and jobs, provision Databricks workspaces, and configure data access.
This tool simplifies jobs launch and deployment process across multiple environments. It also helps to package your project and deliver it to your Databricks environment in a versioned fashion. Designed in a CLI-first manner, it is built to be actively used both inside CI/CD pipelines and as a part of local tooling for fast prototyping.
The purpose of this project is to provide an API for manipulating time series on top of Apache Spark. Functionality includes featurization using lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, and downsampling & interpolation. This has been tested on TB-scale of historical data.
Analyze all of your jobs and clusters across all of your workspaces to quickly identify where you can make the biggest adjustments for performance gains and cost savings.
This package allows to connect to a remote Databricks cluster from a locally running JupyterLab.
Smolder provides an Apache Spark™ SQL data source for loading EHR data from HL7v2 message formats. Additionally, Smolder provides helper functions that can be used on a Spark SQL DataFrame to parse HL7 message text, and to extract segments, fields, and subfields, from a message.
Apache Spark ML Estimator for Density-based spatial clustering based on Hexagonal Hierarchical Spatial Indices.
Toolkit for Apache Spark ML for Feature clean-up, feature Importance calculation suite, Information Gain selection, Distributed SMOTE, Model selection and training, Hyper parameter optimization and selection, Model interprability.
An accelerator providing APIs built on top of PySpark with optimization, validation, and deduplication in mind to simplify and unify feature engineering workflow.
Dataframe Rules Engine
Scala Dataframe data quality expectation validation library.
A tool used to synchronize source Databricks deployment with a target Databricks deployment.
cookiecutter project template for automated Databricks CI/CD pipeline creation and deployment.
Please note that all projects in the http://github.com/databrickslabs account are provided for your exploration only, and are not formally supported by Databricks with Service Level Agreements (SLAs). They are provided AS-IS and we do not make any guarantees of any kind. Please do not submit a support ticket relating to any issues arising from the use of these projects. Any issues discovered through the use of this project should be filed as GitHub Issues on the Repo. They will be reviewed as time permits, but there are no formal SLAs for support.