Biography
Raghav Matta is a Specialist Solutions Architect at Databricks, where he works with organisations on real-time analytics, data engineering and applied AI on the lakehouse. His work centres on turning unstructured, high-volume data into governed, queryable assets that business teams can actually use.
He brings a hands-on, practitioner perspective on modern data architecture: streaming ingestion, medallion-layer design, SQL-based AI functions for sentiment analysis, classification, entity extraction and PII masking, and governance applied to both data and models. He also focuses on making these workflows reproducible with freely available tooling, so that analysts and students can rebuild them without enterprise infrastructure.
His perspective is especially relevant for audiences interested in real-time data pipelines, applied AI for business users, and data and model governance at scale.
Session
- AI For TechSpeaker
Demo 7 - Sentiment Analysis using Databricks AI Functions
Tuesday 22 September, 13:00 – 13:25Demo RoomThe Business Challenge: Exploring how modern organizations effectively monitor live brand sentiment, emerging trends, and customer reactions across high-velocity social platforms. Streaming Architecture: A comprehensive walkthrough of a robust, cloud-native architecture using Azure Databricks, Event Hubs, and Unity Catalog for secure and scalable data ingestion. LLM-Powered Enrichment: Demonstrating how to democratize AI by using built-in Databricks SQL AI functions (such as ai_analyze_sentiment, ai_extract, and ai_classify) to process unstructured text without complex Python ML frameworks. End-to-End Execution: Showcasing the full data lifecycle—from ingesting live social content and storing it efficiently in Delta Lake, to visualizing the enriched results through Databricks AI/BI dashboards. Conversational Analytics: Highlighting Databricks Genie to show how business users can interact with the analyzed data using natural language to uncover deeper, on-the-fly insights.