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Talk 1: Real-Time Data Streams and Accurate SQL Automation The Challenge: Democratizing Data Access One of the key hurdles in data-driven organizations is making data accessible to non-technical users. Many employees and even customers struggle with writing complex SQL queries. Incorporate rigorous prompt testing to eliminate errors.
In this post, we discuss a Q&A bot use case that Q4 has implemented, the challenges that numerical and structured datasets presented, and how Q4 concluded that using SQL may be a viable solution. RAG with semantic search – Conventional RAG with semantic search was the last step before moving to SQL generation.
Solution overview The following figure illustrates our systemarchitecture for CreditAI on AWS, with two key paths: the document ingestion and content extraction workflow, and the Q&A workflow for live user query response. This event-driven architecture provides immediate processing of new documents.
Cache Layer Nearly all of our teams are well-versed with the BigQuery interface because of its ease of use and powerful performance when executing SQL queries. We would have had to manage the changelog table ourselves, as the CDC (Change Data Capture) stream emits an event for every cell change.
The tool converts the templated configuration into a set of SQL commands that are executed against the target Snowflake environment. Manually converting this code to work in Snowflake can be very challenging with differences in data processing paradigms, query languages, and overall systemarchitecture.
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