Anastasiya Lavrenchuk

Genie Code adds tremendous value for data engineers - unifying and automating much of the ingest, ETL and streaming process. Hear from Bilal Aslam (Senior Director of Product Management) and our customer SEGA on how to use Genie Code for agentic data engineering, automating much of the process of building intelligent data pipelines. It integrates into the broader Databricks environment, and with Lakeflow and Spark Declarative Pipelines is a fully governed way to prepare and analyze data directly in Databricks.

At SEGA Europe, we process billions of game telemetry events daily to power real-time analytics for live ops, player experience optimisation, and business intelligence. We have rebuilt our streaming platform on Lakeflow Spark Declarative Pipelines, moving from fragile, manually managed jobs to a resilient, self-healing lakehouse architecture. This approach was battle-tested during the launch of Football Manager 2026, where scale and reliability were critical. This session shares our production journey with Lakeflow—the wins, lessons, and unexpected challenges of operating at gaming scale. We’ll cover our migration from legacy streaming infrastructure, achieving sub-minute latency, and handling evolving event schemas. Topics include stream fan-out from Kinesis to Bronze, schema evolution, late and out-of-order events, data quality checks, performance tuning, and cost control. We’ll also share quantified results from FM26 and practical patterns for teams evaluating Lakeflow at scale.