--- title: "Databricks - Lakehouse, Data & AI Engineering | Cheesecake Labs" description: "Pipelines, ML workloads, and governed data engineered on Databricks and run in production. Built for companies whose data outgrew the warehouse and whose AI plans need a real foundation." url: "https://cheesecakelabs.com/partners/databricks" locale: "en" updatedAt: "2026-08-05T18:10:11.301Z" --- # Databricks - Lakehouse, Data & AI Engineering | Cheesecake Labs > Pipelines, ML workloads, and governed data engineered on Databricks and run in production. Built for companies whose data outgrew the warehouse and whose AI plans need a real foundation. *Databricks* ## The Databricks partner that takes the lakehouse into production. We modernize data platforms, build governed pipelines, and put analytics and machine learning to work on Databricks, from architecture through operations. ## Talk with our team › ### Official Databricks Partner We define workloads, data contracts, access, latency, cost, and ownership before choosing the architecture. ### Modern data stack delivery Catalog, lineage, access, quality, and environment boundaries are built in from day one. ### Production data engineering We build tested, documented, observable pipelines and own reliability after launch. ### One foundation for analytics and AI Data engineering, analytics, ML, and production AI run on the same governed foundation. ## What the Databricks partnership means for your company Databricks expertise connected to the systems around it. ## Schedule a call › ### Lakehouse architecture & migration Target architecture, workload sequencing, storage and compute design, and phased migration from legacy or fragmented data systems. ### Batch and streaming pipelines Reliable pipelines with orchestration, testing, monitoring, and recovery built in. ### ML & AI workloads Feature pipelines, experimentation, model operations, retrieval, and AI apps grounded in governed data. ### Governance and data quality Catalog, lineage, access controls, data contracts, and quality checks for trusted human and AI use. ## What we build on Databricks Databricks engineering across the lakehouse: ### Make streaming data usable Turn high-volume events into reliable operational and analytical data with clear latency, recovery, and observability targets. ### Replace a fragmented data estate Consolidate duplicated pipelines, inconsistent data models, and disconnected analytics into a governed lakehouse architecture. ### Prepare governed data for AI Create the access controls, quality rules, lineage, and retrieval patterns required for production AI workloads. ## Where teams start ## Databricks Data Platform Assessment In 2 to 4 weeks, we assess the current estate, target workloads, governance gaps, cost drivers, migration risks, and operating model. Best for: Teams planning a Databricks migration, consolidating data platforms, improving reliability, or preparing governed data for AI ## Assess your Databricks platform › ### Why choose Cheesecake Labs for Databricks work? Because configuring Databricks and shipping products on it are different jobs, and we do the second one. Our teams build the full system around the lakehouse: the pipelines, the governance, and the applications people actually open every morning. We have senior engineers on your time zone, inside the conversation instead of behind a ticket queue. ### Can Databricks feed our AI initiative? That is its strong suit. We turn the lakehouse into AI-ready foundations — feature pipelines, retrieval-ready data, and governance — so your models train and run on data the business can trust. ### Can our teams query data in plain English? Yes. We wire natural-language querying over governed Databricks data, so analysts and business teams get answers directly, with permissions and lineage intact behind the scenes. ### Should we use Databricks or Snowflake? It depends on your workloads, team, and existing stack — both are excellent. We help you decide on the evidence, and we deliver production systems on either (or on both, side by side). ### What should you look for in a Databricks partner? Look for engineers who ship products on the lakehouse, not just configure it: real pipelines, governance, and applications in production — with senior people in your time zone, in the conversation rather than behind a ticket queue. ## FAQ ## Structured data ```json [{"@context":"https://schema.org","@type":"BreadcrumbList","@id":"https://cheesecakelabs.com/partners/databricks#breadcrumb","inLanguage":"en","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://cheesecakelabs.com/"},{"@type":"ListItem","position":2,"name":"Partners"},{"@type":"ListItem","position":3,"name":"Databricks"}]},{"@context":"https://schema.org","@type":"FAQPage","@id":"https://cheesecakelabs.com/partners/databricks#faq","inLanguage":"en","mainEntity":[{"@type":"Question","name":"Why choose Cheesecake Labs for Databricks work?","acceptedAnswer":{"@type":"Answer","text":"Because configuring Databricks and shipping products on it are different jobs, and we do the second one. 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