Databricks SQL opens up prospects for nearly every part we need to do. It’s an all-in-one platform with full knowledge intelligence. It’s largely computerized beneath the hood so that you don’t have to fret – you may simply construct.— Tamas Bacskai, Head of Information, 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 energetic product provides throughout electronics, family items, and extra.
From the start, knowledge was a precedence. However the firm began with a easy basis: Microsoft SQL Server and Energy BI, operating each day batch hundreds 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 knowledge warehouse. It wanted an all-in-one platform that would help SQL, Python, and future AI initiatives with out including operational complexity. The group 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 Information, his mandate was clear: construct a data-oriented group and outline a scalable path ahead. The prevailing SQL Server atmosphere functioned as a fundamental warehouse, however Python workloads ran on a separate digital machine, governance was restricted, and scaling meant growing infrastructure spend.
The group evaluated three choices: proceed focusing solely on warehousing, cut up superior workloads to a different growth group, or undertake a lakehouse structure that would unify SQL and Python. The lakehouse mannequin “ticked all of the containers,” Bacskai stated — together with future enlargement into machine studying and AI.
Reasonably than aiming for an ideal redesign, Fizz took an MVP-first method. With help from an exterior associate, they migrated roughly 50 tables and several other saved procedures, recreating core views in Databricks SQL. The aim was easy: preserve reviews operating, however level them to a brand new engine.
“It was unorthodox,” Bacskai stated. “We didn’t need an ideal migration the place every part is rewritten. We needed to maneuver as quick as potential and refine and modernize after. It’s a lot simpler to do as soon as the information is in Databricks.”
In three months, the legacy SQL Server was switched off fully. Energy BI reviews continued seamlessly, now powered by Databricks. “It was not unattainable, solely formidable,” Bacskai stated, “however predictable and achievable.”
Sooner reporting and higher service ranges
The speedy affect was on efficiency. Beforehand, each day ETL cycles may take three to 4 hours, and reporting was not reliably out there till 7:00 or 8:00 a.m. That created friction with enterprise customers who started their day earlier.
With Databricks SQL, Fizz lowered its end-to-end nightly processing window to roughly 90 minutes. Stories are actually constantly prepared by 4:30 a.m., even on weekends and holidays. Energy BI refresh cycles have been minimize by roughly 50%, and gigabyte-scale exports now full in minutes.
The good points weren’t the results of overprovisioned infrastructure. Fizz runs comparatively average 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 extra money or greater clusters at it,” Bacskai clarified. “The SQL execution engine is just sooner. It auto-optimizes and every part is there for us.”
Equally vital, Databricks eradicated the necessity for separate environments to run Python. All jobs now run natively throughout 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 needed a platform that will not restrict its AI ambitions. Even throughout migration, the group anticipated rising demand for machine studying, generative AI, and extra superior knowledge governance.
At this time, Databricks can help SQL, Python, and machine studying workloads in a single atmosphere. The group is exploring masking insurance policies and governance controls to strengthen GDPR and EU AI Act readiness. AI-powered SQL capabilities will assist clear and standardize product names, decreasing reliance on advanced common expressions and accelerating knowledge preparation.
Self-service analytics can be increasing by Databricks Genie. Enterprise customers can ask natural-language questions, in Hungarian, with out writing SQL. About 20 energetic customers depend on Genie at this time, reclaiming roughly 20% of an analyst’s time beforehand spent answering advert hoc requests – liberating the group up for extra value-add efforts.
“Our Genie set-up just isn’t full but,” Bacskai famous, “but it surely means we don’t must be taught SQL to ask a query. You possibly can simply chat together with your knowledge.”
For a rising ecommerce firm, the worth extends past velocity. Databricks gives a unified, AI-ready basis that scales with new use instances from advertising knowledge integration to mannequin serving endpoints with out requiring a bigger group to handle it.
“Databricks SQL was a lot better than what we anticipated,” Bacskai stated. “It’s one thing we like to work with. It could actually do every part we would like, so we are able to simply construct and create what we would like.”