7-Eleven’s Information Documentation Dilemma
7-Eleven’s information ecosystem is very large and sophisticated, housing 1000’s of tables with a whole lot of columns throughout our Databricks atmosphere. This information varieties the spine of our operations, analytics and decision-making processes. Historically, 7-Eleven’s information dictionary and documentation lived in Confluence pages, meticulously maintained by our information workforce members who would manually doc desk and column definitions.
We confronted a essential roadblock as we started exploring the AI-powered options on the Databricks Information Intelligence Platform, together with AI/BI Genie, clever dashboards and different purposes. These superior instruments rely closely on desk metadata and feedback embedded instantly inside Databricks to generate insights, reply questions on our information, and construct automated visualizations. With out correct desk and column feedback in Databricks itself, we have been primarily leaving highly effective AI capabilities on the desk. For instance, when Genie lacks column definitions, it may well misread the that means of bespoke columns, requiring finish customers to make clear. As soon as we enriched our metadata, Genie’s contextual understanding improved dramatically—precisely figuring out column functions, surfacing the suitable tables in response to pure language queries, and producing way more related and actionable insights. Merely put, Genie, like all AI brokers, will get extra considerate and extra useful when it has higher metadata to work with.
The hole between our well-documented Confluence pages and our “metadata-light” Databricks atmosphere was stopping us from realizing the total potential of our information platform funding.
Guide Migration’s Unimaginable Scale
After we initially thought of migrating our documentation from Confluence to Databricks, the size of the problem grew to become instantly obvious. With 1000’s of tables containing a whole lot of columns every, a handbook migration would require:
- Time-intensive labor: A whole lot of person-hours to repeat and paste documentation
- Guide metadata updates: Crafting 1000’s of particular person SQL statements to replace metadata or going to every desk UI
- Mission oversight: Implementing a monitoring system to make sure all tables have been correctly up to date
- High quality assurance: Making a validation course of to catch inevitable human errors
- Ongoing maintenance: Establishing an ongoing upkeep protocol to maintain each techniques in sync
Human error can be unavoidable even when we devoted vital assets to this effort. Some tables can be missed, feedback can be incorrectly formatted, and the method would probably must be repeated as documentation advanced. Furthermore, the tedious nature of the work probably results in inconsistent high quality throughout the documentation.
Most regarding was the chance price. Whereas our information workforce targeted on this migration, they couldn’t work on higher-value initiatives. Day by day, we confronted delays in strengthening our Databricks metadata, leaving untapped potential within the AI/BI capabilities already at our fingertips.
The Clever Doc Processing Pipeline
To resolve this problem, 7-Eleven developed a complicated agentic AI workflow powered by Llama 4 Maverick, deployed via Mosaic AI Mannequin Serving, that automated the whole documentation migration course of via an clever multistage pipeline:
- Discovery section: The agent makes use of Databricks APIs to get all tables, desk names and column buildings.
- Doc retrieval: The agent pulls all related information dictionary paperwork from Confluence, making a corpus of potential documentation sources.
- Reranking and filtering: Implementing superior reranking algorithms, the system prioritizes essentially the most related documentation for every desk, filtering out noise and irrelevant content material. This essential step ensures we match tables with their correct documentation even when naming conventions aren’t completely constant.
- Clever matching: For every Databricks desk, the AI agent analyzes potential documentation matches, utilizing contextual understanding to find out the right Confluence web page even when names don’t match precisely.
- Focused extraction: As soon as the right documentation is recognized, the agent intelligently extracts related descriptions for each tables and their columns, preserving the unique that means whereas formatting appropriately for Databricks metadata.
- SQL technology: The system mechanically generates correctly formatted SQL statements to replace the Databricks desk and column feedback, dealing with particular characters and formatting necessities.
- Execution and verification: The agent runs the SQL updates and, via MLflow monitoring and analysis, verifies that metadata was utilized accurately, logs outcomes, and surfaces any points for human evaluation.
- Monitoring and insights: The workforce additionally makes use of the AI/BI Genie Dashboard to trace challenge metrics in actual time, guaranteeing transparency, high quality management, and steady enchancment.
This clever pipeline reworked months of tedious, error-prone work into an automatic course of that accomplished the preliminary migration in days. The system’s capability to grasp context and make clever matches between in another way named or structured assets was key to attaining excessive accuracy.
Since implementing this resolution, we plan emigrate documentation for over 90% of our tables, unlocking the total potential of Databricks’ AI/BI options. What started as a evenly used AI assistant has advanced into an on a regular basis software in our information workflows.. Genie’s capability to grasp context now mirrors how a human would interpret the information, because of the column-level metadata we injected. Our information scientists and analysts can now use pure language queries via AI/BI Genie to discover information, and our dashboards leverage the wealthy metadata to supply extra significant visualizations and insights.
The answer continues to supply worth as an ongoing synchronization software, guaranteeing that as our documentation evolves in Confluence, these modifications are mirrored in our Databricks atmosphere. This challenge demonstrated how thoughtfully utilized AI brokers can resolve complicated information governance challenges at enterprise scale, turning what appeared like an insurmountable documentation process into a chic automated resolution.
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