65 lines
4.0 KiB
Markdown
65 lines
4.0 KiB
Markdown
---
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title: 'Some of You Will "Git" This'
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description: Pull requests, diffs, hooks, tests and pipelines - for your production data. Data Controller brings the git workflow to master data management in SAS.
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date: '2026-08-13 14:00:00'
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author: 'Data Controller'
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authorLink: https://www.linkedin.com/showcase/data_controller
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tags:
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- Announcements
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previewImg: './gitflow.png'
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---
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# Some of You Will "Git" This
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If you could make a "pull request" for production data, how might that look?
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Perhaps:
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- ✅ A [before / after comparison](https://docs.datacontroller.io/dc-userguide/) of the values that had changed
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- ✅ [Security](https://docs.datacontroller.io/dcc-security/) (who can submit / approve in each "repo")
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- ✅ Only store the changed data (not full copies)
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- ✅ A new "diff" when the base table changes
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- ✅ Ability to choose [one or more approvers](https://docs.datacontroller.io/dcc-tables/)
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- ✅ [HOOK scripts](https://docs.datacontroller.io/dcc-tables/) before / after a commit
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- ✅ Tests ([DQ checks](https://docs.datacontroller.io/dcc-validations/)) prior to merge
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- ✅ A [pipeline](https://docs.datacontroller.io/dcc-tables/) following a merge
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- ✅ [Emails](https://docs.datacontroller.io/emails/) for "status" changes
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- ✅ Comments by reviewers
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- ✅ Full [history](https://docs.datacontroller.io/dc-userguide/) of changes
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Sound familiar? This is exactly how developers have managed *code* for decades - and Data Controller brings the same discipline to *data*. Every edit to a table in SAS® goes through an edit - stage - approve workflow: changes are staged as a diff, checked by validation rules and hook scripts, reviewed and approved by the people you choose, then loaded. The audit trail captures who changed what, when and why - the `git blame` of master data.
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If you're ready to "commit" to Data Control, we invite you to "clone" the latest version of [Data Controller for SAS®](https://datacontroller.io) - which has ALL these features - and "push" to your "main" environment for some "master" data management!
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<!-- Source LinkedIn post:
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Some of you will "git" this..
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If you could make a "pull request" for production data, how might that look?
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Perhaps:
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✅ A before / after comparison of the values that had changed
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✅ Security (who can submit / approve in each "repo")
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✅ Only store the changed data (not full copies)
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✅ A new "diff" when the base table changes
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✅ Ability to choose one or more approvers
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✅ HOOK scripts before / after a commit
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✅ Tests (DQ checks) prior to merge
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✅ A pipeline following a merge
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✅ Emails for "status" changes
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✅ Comments by reviewers
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✅ Full history of changes
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Sound familiar? This is exactly how developers have managed CODE for decades - and Data Controller brings the same discipline to DATA. Every edit to a table in SAS® goes through an edit - stage - approve workflow: changes are staged as a diff, checked by validation rules and hook scripts, reviewed and approved by the people you choose, then loaded. The audit trail captures who changed what, when and why - the "git blame" of master data.
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If you're ready to "commit" to Data Control, we invite you to "clone" the latest version of Data Controller for SAS - which has ALL these features - and "push" to your "main" environment for some "master" data management!
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Link in the comments below 👇
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#dataops #mdm #masterdata #dataquality #sas #sasjs #datagovernance
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-->
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<!-- Image prompt:
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A clean, flat-design illustration in the Data Controller brand style showing a database table flowing through a git-style pull-request workflow: rows of a data table on the left passing through a "stage" step shown as a highlighted before/after diff of changed cells, then an approval checkpoint with a tick and reviewer avatars, then merging into a production database cylinder on the right. Git-flavoured touches: a branch line diagram arcing from "staging" branch into "main", small commit dots along the line. Dark navy background, teal/green accents, orange highlights on the changed cells. Minimal text: "stage -> approve -> merge". 16:9 landscape, suitable as a blog/feed cover image.
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-->
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