7-Eleven’s Knowledge Documentation Dilemma
7-Eleven’s information ecosystem is huge and complicated, housing 1000’s of tables with a whole lot of columns throughout our Databricks atmosphere. This information types 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 staff members who would manually doc desk and column definitions.
We confronted a crucial roadblock as we started exploring the AI-powered options on the Databricks Knowledge Intelligence Platform, together with AI/BI Genie, clever dashboards and different functions. These superior instruments rely closely on desk metadata and feedback embedded straight 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 had been primarily leaving highly effective AI capabilities on the desk. For instance, when Genie lacks column definitions, it will possibly 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 correct tables in response to pure language queries, and producing much 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-about migrating our documentation from Confluence to Databricks, the dimensions of the problem grew to become instantly obvious. With 1000’s of tables containing a whole lot of columns every, a guide migration would require:
- Time-intensive labor: A whole bunch 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
- Challenge oversight: Implementing a monitoring system to make sure all tables had 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 methods in sync
Human error can be unavoidable even when we devoted important assets to this effort. Some tables can be missed, feedback can be incorrectly formatted, and the method would possible should be repeated as documentation advanced. Furthermore, the tedious nature of the work possible results in inconsistent high quality throughout the documentation.
Most regarding was the chance value. Whereas our information staff targeted on this migration, they couldn’t work on higher-value initiatives. Day-after-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 classy agentic AI workflow powered by Llama 4 Maverick, deployed by Mosaic AI Mannequin Serving, that automated the complete documentation migration course of by 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 probably the most related documentation for every desk, filtering out noise and irrelevant content material. This crucial 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, by MLflow monitoring and analysis, verifies that metadata was utilized appropriately, logs outcomes, and surfaces any points for human overview.
- Monitoring and insights: The staff 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 remodeled months of tedious, error-prone work into an automatic course of that accomplished the preliminary migration in days. The system’s means 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 frivolously used AI assistant has advanced into an on a regular basis software in our information workflows.. Genie’s means to grasp context now mirrors how a human would interpret the info, because of the column-level metadata we injected. Our information scientists and analysts can now use pure language queries by 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 adjustments are mirrored in our Databricks atmosphere. This challenge demonstrated how thoughtfully utilized AI brokers can remedy advanced information governance challenges at enterprise scale, turning what appeared like an insurmountable documentation process into a sublime automated resolution.
Wish to study extra about AI/BI and the way it may also help unlock worth out of your information? Study extra right here.
