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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…

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    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…

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Repositories

Showing 10 of 148 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.

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    Python 6 2 1 1 Updated Jul 15, 2024
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    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
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    databricks-industry-solutions/many-model-forecasting’s past year of commit activity
    Python 25 10 0 0 Updated Jul 8, 2024
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    databricks-industry-solutions/causal-incentive’s past year of commit activity
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    databricks-industry-solutions/dbignite-forked’s past year of commit activity
    Python 1 11 0 1 Updated Jun 30, 2024
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    0 0 0 0 Updated Jun 12, 2024
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    Use generative AI to create product copy

    databricks-industry-solutions/product_copy_genai’s past year of commit activity
    Python 0 1 0 0 Updated Jun 12, 2024
  • segmentation Public

    Create advanced customer segments to drive better purchasing predictions based on behaviors. Using sales data, campaigns and promotions systems, this solution helps derive a number of features that capture the behavior of various households. Build useful customer clusters to target with different promos and offers.

    databricks-industry-solutions/segmentation’s past year of commit activity
    Python 6 4 0 1 Updated Jun 7, 2024

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