Fast-growing fintechs outgrow their app database. A lakehouse with real-time pipelines keeps analytics and risk on one platform.
Fintechs can run real-time payments analytics and fraud detection on the Databricks lakehouse without a second streaming stack, using real-time pipelines, governed features and model serving on one platform. This suits small teams that cannot run several platforms, and it keeps fraud features and models governed in one place.
Fintechs grow fast. Transaction volumes rise, new products launch, and regulators ask harder questions. The app database that worked at launch starts to slow down.
Teams add a streaming tool for fraud, a warehouse for reporting and a separate store for ML features. Each works. Together they are hard to govern and expensive to run.
Compliance teams see prioritized alerts with context. Product teams answer funnel and retention questions themselves. Engineers maintain one platform instead of four.
Fintechs rarely have large data teams. A dedicated pod that owns the platform end to end often costs less than hiring each specialist separately.
NeuralShore Engineering, Databricks delivery team
Databricks architects, data engineers and AI engineers at NeuralShore, DLytica’s Databricks delivery company. Leadership comes from IBM data and AI architecture.
Yes. Real-time pipelines, governed features and model serving on one platform score transactions close to the moment of payment.
Fewer systems to run means less infrastructure work and faster delivery of new use cases.
What Canadian banks, insurers and enterprises need to plan for when they build on Databricks: Canadian regions, OSFI guidelines, PIPEDA and Québec Law 25.
Read the article: Databricks in Canada: data residency, OSFI and Québec Law 25 →Genie One is now generally available for business users, and Genie Code brings an AI coding agent to data engineering and ML.
Read the article: Genie One and Genie Code: what they mean for data teams →
