Databricks SQL opens up potentialities for nearly every thing we wish to do. It’s an all-in-one platform with full information intelligence. It’s largely computerized below the hood so that you don’t have to fret – you may simply construct.— Tamas Bacskai, Head of Knowledge, Fizz.hu
Fizz.hu is a fast-growing ecommerce market backed by OTP Group. Launched simply two years in the past as a part of OTP’s “past banking” technique, Fizz hosts greater than 500 retailers providing over 1.5 million lively product presents throughout electronics, family items, and extra.
From the start, information was a precedence. However the firm began with a easy basis: Microsoft SQL Server and Energy BI, operating every day batch masses for reporting. As product catalogs expanded and new use instances emerged, that setup started to point out its limits.
Fizz wanted greater than a conventional information warehouse. It wanted an all-in-one platform that would help SQL, Python, and future AI initiatives with out including operational complexity. The staff discovered that in Databricks SQL and determined emigrate to a lakehouse structure constructed to scale with the enterprise.
A realistic migration, delivered in three months
When Tamas Bacskai joined as Head of Knowledge, his mandate was clear: construct a data-oriented staff and outline a scalable path ahead. The present SQL Server surroundings functioned as a fundamental warehouse, however Python workloads ran on a separate digital machine, governance was restricted, and scaling meant rising infrastructure spend.
The staff evaluated three choices: proceed focusing solely on warehousing, cut up superior workloads to a different growth staff, or undertake a lakehouse structure that would unify SQL and Python. The lakehouse mannequin “ticked all of the packing containers,” Bacskai mentioned — together with future growth into machine studying and AI.
Quite than aiming for an ideal redesign, Fizz took an MVP-first strategy. With help from an exterior associate, they migrated roughly 50 tables and a number of other saved procedures, recreating core views in Databricks SQL. The objective was easy: maintain reviews operating, however level them to a brand new engine.
“It was unorthodox,” Bacskai mentioned. “We didn’t need an ideal migration the place every thing is rewritten. We wished to maneuver as quick as doable and refine and modernize after. It’s a lot simpler to do as soon as the info is in Databricks.”
In three months, the legacy SQL Server was switched off utterly. Energy BI reviews continued seamlessly, now powered by Databricks. “It was not unattainable, solely formidable,” Bacskai mentioned, “however predictable and achievable.”
Quicker reporting and higher service ranges
The rapid affect was on efficiency. Beforehand, every day ETL cycles might take three to 4 hours, and reporting was not reliably accessible till 7:00 or 8:00 a.m. That created friction with enterprise customers who started their day earlier.
With Databricks SQL, Fizz diminished its end-to-end nightly processing window to roughly 90 minutes. Stories are actually persistently prepared by 4:30 a.m., even on weekends and holidays. Energy BI refresh cycles had been lower by roughly 50%, and gigabyte-scale exports now full in minutes.
The good points weren’t the results of overprovisioned infrastructure. Fizz runs comparatively reasonable workloads — about 10 TB complete throughout bronze and silver layers — however the brand new SQL engine and auto-optimization capabilities delivered measurable enhancements with out fixed tuning.
“It’s not that we simply threw more cash or larger clusters at it,” Bacskai clarified. “The SQL execution engine is just sooner. It auto-optimizes and every thing is there for us.”
Equally vital, Databricks eradicated the necessity for separate environments to run Python. All jobs now run natively inside the platform, simplifying operations and making a cleaner basis for future machine studying initiatives.
Increasing capabilities with AI and self-service
From the outset, Fizz wished a platform that may not restrict its AI ambitions. Even throughout migration, the staff anticipated rising demand for machine studying, generative AI, and extra superior information governance.
At this time, Databricks can help SQL, Python, and machine studying workloads in a single surroundings. The staff is exploring masking insurance policies and governance controls to strengthen GDPR and EU AI Act readiness. AI-powered SQL features will assist clear and standardize product names, decreasing reliance on advanced common expressions and accelerating information preparation.
Self-service analytics can also be increasing by way of Databricks Genie. Enterprise customers can ask natural-language questions, in Hungarian, with out writing SQL. About 20 lively customers depend on Genie as we speak, reclaiming roughly 20% of an analyst’s time beforehand spent answering advert hoc requests – liberating the staff up for extra value-add efforts.
“Our Genie set-up just isn’t full but,” Bacskai famous, “but it surely means we don’t need to study SQL to ask a query. You’ll be able to simply chat along with your information.”
For a rising ecommerce firm, the worth extends past pace. Databricks gives a unified, AI-ready basis that scales with new use instances from advertising information integration to mannequin serving endpoints with out requiring a bigger staff to handle it.
“Databricks SQL was a lot better than what we anticipated,” Bacskai mentioned. “It’s one thing we like to work with. It could actually do every thing we would like, so we are able to simply construct and create what we would like.”