Transcript

In this demo, we begin with the ingestion layer where all our data sources are managed in one place. Users can simply click New Connection to choose from supported connectors such as MySQL, PostgreSQL, or SQL Server. After selecting the source, a configuration panel slides out where users only need to fill in basic information like host, port, database and sync frequency. Once saved, the connection automatically syncs data into Databricks, giving us. Live and reliable pipeline for our downstream semantic layer and Chappie eye. Step one. I select the tables I'll likely need customers and orders. Then I open Fine Table relations and simply describe my goal in natural language. Create a table that summarizes each customer's total spending in order count. The AI detects how these tables relate. If I want to change, I can just ask the AI assistant on the right side. Like I'll add an average order value column. The editor updates instantly. Click the Run Query button at the top, then check if the table displays the expected data. I can also do targeted inline edits. I highlight the average column and asked to limit it to US customers. We can see the code changes in place. Now I run the query and preview the results immediately. Notice the US only average is null for EU customers. That's expected. With the help of AITL, a standard medallion model is automatically generated across bronze, silver and gold layers. Here we add a new transformation step. The AI automatically generates A transformation note for us. At each stage, the user can interact with the nodes using natural language, refining SQL, validating outputs and correcting results through a human in the loop process. Once the data is validated, the node configuration is saved, ensuring a clean and reusable data pipeline. For notebook scripts, such as the SQL query, the user preview the data directly below the code editor. A search bar is available for filtering the data. The user can also view a data lineage graph which shows how the data flows and transforms from layer to layer. When I'm happy with the query, I save it as an ETL node so it can be reused across jobs. Each ETL node can be defined as an ETL job. To create a job, the user simply provides a title and description for scheduling. The user can choose the schedule type, run once, run daily, etc. And specify the start date and time for execution. To automate it, I dragged the customer under score orders node and link it to a Report under score export node. I schedule it to run every morning at 9:00. Alternatively, I can just say what I want, like create a job that runs daily at 9:00 AM, first run customer under score orders, then export the report. The job scheduling are generated for me. When the user clicks Run Job, the entire ETL job is triggered and executed. The running status and logs are displayed in the panel below. Once completed, the results and detailed job metrics are shown. Additionally, the Run History tab provides access to past execution logs. Assume the gold layer data is ready. Users can configure their semantic layer from business logic via YAML or a graph editor to define relationships for AI degenerate. In this demo, the user begins by asking why has our monthly active users dropped this quarter. The AI first recognizes the intent the user wants to understand the reasons behind the MU decline, with analysis focused on quarterly trends, user segments, device types, and acquisition channels. Next, the Query Computation agent automatically generates an SQL query to calculate monthly active users, their changes over time, and breakdowns by device, user type and region. Based on the results, the AI detects a 15% overall drop in Q3, with mobile users declining by 23% as the main driver and free user showing the sharpest decline. When the user requests A deeper root cause analysis, the AI launches its root cause analysis agent. It examines over 450,000 users and more than two million events. Comparing Q2 with Q3, the results reveal that 67% of the decline comes from critical technical issues. Mobile performance degradation, App Store rating drops and push notification failures. Secondary factors include pricing changes and seasonal effects. While new competitors contributed to a smaller share. Drill down tables show mobile iOS MQ dropping over 30%, app crash rates increasing by 340% and iOS ratings falling from 4.2 to 2.8 stars, leading to a major drop in downloads. A correlation matrix highlights the strong link between. Performance and user sentiment. The AI concludes that the EMU decline is primarily driven by execution failures rather than market strategy. The causal chain is clear. Poor app performance leads to bad reviews, reduced acquisition, and higher churn. Recommended actions include rolling back the app to a stable version, fixing the notification system, launching a recovery campaign for App Store ratings, and adjusting the free tier strategy. With these insights, the AI projects a six to eight week recovery timeline. The immediate actions are taken.