Best Data Management Platforms (DMP) for GitHub
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Compare the Top Data Management Platforms (DMP) that integrate with GitHub as of October 2025
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This a list of Data Management Platforms (DMP) that integrate with GitHub. Use the filters on the left to add additional filters for products that have integrations with GitHub. View the products that work with GitHub in the table below.
What are Data Management Platforms (DMP) for GitHub?
Data management platforms (DMP) store audience and customer data for an organization to enable it to run more effective advertising and marketing campaigns using first, second, and third party audience data. Compare and read user reviews of the best Data Management Platforms (DMP) for GitHub currently available using the table below. This list is updated regularly.
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Tugger
Tugger
Tugger swiftly and securely copies your data out of your business system(s) and into data analytics tools Microsoft Power BI or Tableau for first-rate business reporting. Once your data is transferred, Tugger also gets you set up with key business reports for a complete end-to-end solution, no other ETL tool offers this complete package. Tugger makes your life easier by removing the need for any manual API integrations and reduces the risk of skewed data. No technical knowledge is required and all users get access to Tugger's popular support. Data Sources that Tugger integrates with include: HubSpot, Harvest, Microsoft Teams, JIRA, GitHub and more.Starting Price: £75 per month -
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IRI Voracity
IRI, The CoSort Company
Voracity is the only high-performance, all-in-one data management platform accelerating AND consolidating the key activities of data discovery, integration, migration, governance, and analytics. Voracity helps you control your data in every stage of the lifecycle, and extract maximum value from it. Only in Voracity can you: 1) CLASSIFY, profile and diagram enterprise data sources 2) Speed or LEAVE legacy sort and ETL tools 3) MIGRATE data to modernize and WRANGLE data to analyze 4) FIND PII everywhere and consistently MASK it for referential integrity 5) Score re-ID risk and ANONYMIZE quasi-identifiers 6) Create and manage DB subsets or intelligently synthesize TEST data 7) Package, protect and provision BIG data 8) Validate, scrub, enrich and unify data to improve its QUALITY 9) Manage metadata and MASTER data. Use Voracity to comply with data privacy laws, de-muck and govern the data lake, improve the reliability of your analytics, and create safe, smart test data
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