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@databricks-industry-solutions

Databricks Industry Solutions

Databricks Solution Accelerators are fully functional notebooks that tackle the most common and high-impact use cases that you face every day.

Databricks Solution Accelerators are fully functional notebooks that tackle the most common and high-impact use cases that you face every day. Databricks customers utilize Solution Accelerators as a starting-point for new data use-cases and product development. Solution Accelerators are vetted and built by industry experts at Databricks.

By Industry

Getting started

Although specific solutions can be downloaded as .dbc archives from our websites, we recommend cloning these repositories onto your databricks environment. Not only will you get access to latest code, but you will be part of a community of experts driving industry best practices and re-usable solutions, influencing our respective industries.

add_repo

To start using a solution accelerator in Databricks simply follow these steps:

  1. Clone solution accelerator repository in Databricks using Databricks Repos
  2. Attach the RUNME notebook to any cluster and execute the notebook via Run-All. A multi-step-job describing the accelerator pipeline will be created, and the link will be provided. The job configuration is written in the RUNME notebook in json format.
  3. Execute the multi-step-job to see how the pipeline runs.
  4. You might want to modify the samples in the solution accelerator to your need, collaborate with other users and run the code samples against your own data. To do so start by changing the Git remote of your repository to your organization’s repository vs using our samples repository (learn more). You can now commit and push code, collaborate with other user’s via Git and follow your organization’s processes for code development.

The cost associated with running the accelerator is the user's responsibility.

Project support

Please note the code in this project is 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. The source in this project is provided subject to the Databricks License. All included or referenced third party libraries are subject to the licenses set forth below.

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.

Popular repositories Loading

  1. security-analysis-tool security-analysis-tool Public

    Security Analysis Tool (SAT) analyzes customer's Databricks account and workspace security configurations and provides recommendations that help them follow Databrick's security best practices. Whe…

    Python 91 41

  2. diy-llm-qa-bot diy-llm-qa-bot Public

    Driving a Large Language Model Revolution in Customer Service and Support

    Python 69 24

  3. esg-scoring esg-scoring Public

    In this solution, we offer a novel approach to sustainable finance by combining NLP techniques and news analytics to extract key strategic ESG initiatives and learn companies' commitments to corpor…

    Python 50 26

  4. hls-llm-doc-qa hls-llm-doc-qa Public

    Build a question answering system based on a given collection of documents with open-source LLMs

    Python 47 23

  5. auto-data-linkage auto-data-linkage Public

    Low effort linking and easy de-duplication. Databricks ARC provides a simple, automated, lakehouse integrated entity resolution solution for intra and inter data linking.

    Python 46 20

  6. many-model-forecasting many-model-forecasting Public

    Bootstrap your large scale forecasting solution on Databricks with Many Models Forecasting (MMF) Project.

    Python 44 17

Repositories

Showing 10 of 153 repositories
  • security-analysis-tool Public

    Security Analysis Tool (SAT) analyzes customer's Databricks account and workspace security configurations and provides recommendations that help them follow Databrick's security best practices. When a customer runs SAT, it will compare their workspace configurations against a set of security best practices and delivers a report.

    databricks-industry-solutions/security-analysis-tool’s past year of commit activity
    Python 91 41 5 0 Updated Nov 8, 2024
  • pixels Public

    Facilitates simple large scale processing of HLS Medical images, documents, zip files. Previously at https://github.com/dmoore247/pixels

    databricks-industry-solutions/pixels’s past year of commit activity
    JavaScript 24 15 18 (4 issues need help) 1 Updated Nov 7, 2024
  • dbignite-forked Public Forked from databrickslabs/dbignite

    Forking for internal development

    databricks-industry-solutions/dbignite-forked’s past year of commit activity
    Python 1 10 0 0 Updated Nov 7, 2024
  • x12-edi-parser Public

    Used for reading & writing X12 messages

    databricks-industry-solutions/x12-edi-parser’s past year of commit activity
    Python 7 1 2 1 Updated Nov 6, 2024
  • databricks-industry-solutions/CareCost-Compass’s past year of commit activity
    Python 1 1 0 0 Updated Nov 5, 2024
  • databricks-industry-solutions/transformer_forecasting’s past year of commit activity
    Python 7 1 0 0 Updated Nov 4, 2024
  • digital-pathology Public

    Help augment diagnostic workflows with this Databricks Solution Accelerator for pathology image analysis. Now you can rapidly process thousands of whole slide images in minutes and use machine learning to automate the detection of metastasis.

    databricks-industry-solutions/digital-pathology’s past year of commit activity
    Python 15 12 0 0 Updated Nov 4, 2024
  • semantic-caching Public

    This project implements a caching system for Databricks, designed to improve response times and reduce cost for frequently asked questions or similar queries.

    databricks-industry-solutions/semantic-caching’s past year of commit activity
    Python 7 3 0 2 Updated Nov 4, 2024
  • csrd_assistant Public

    In this solution accelerator, we demonstrate how generative AI, retrieval augmented generation (RAG) and multi stage reasoning can be used to better navigate through the complexities of regulatory filings, bringing more transparency for companies to disclose their societal and environmental impacts.

    databricks-industry-solutions/csrd_assistant’s past year of commit activity
    Python 4 1 1 2 Updated Nov 4, 2024
  • context-graph-analytics Public

    Time series knowledge graphs for cybersecurity

    databricks-industry-solutions/context-graph-analytics’s past year of commit activity
    Python 18 6 1 0 Updated Oct 29, 2024