Certified Databricks engineers building production lakehouses, streaming pipelines, and ML platforms. Company-wide Databricks Partner status with certified engineers on every engagement — not just the credential on a website.
★★★★★ 5.0 on Clutch/Databricks Partner/Certified engineers on every project
From lakehouse design to production ML — the full data and AI stack inside a single Databricks environment.
Design medallion architectures (bronze, silver, gold) on Delta Lake that handle batch and streaming in one place. We architect for reliability, schema evolution, and time-travel from day one.
Build production-grade batch and streaming pipelines using PySpark, Structured Streaming, Delta Live Tables, and Auto Loader. Designed for operational reliability, not just throughput benchmarks.
Stand up end-to-end ML platforms on Databricks — experiment tracking with MLflow, feature engineering and serving with Feature Store, and model deployment via Databricks Model Serving.
Implement Unity Catalog for centralised access control, data lineage, and auditing across all Databricks workspaces. Column masking, row filters, and tag-based governance built in.
Configure Databricks SQL warehouses and serverless endpoints for BI workloads. Query optimisation, Photon acceleration, and caching strategies that make dashboards responsive at data warehouse scale.
Databricks spans data engineering, ML, and analytics. Most vendors cover one layer. We engineer the full lakehouse.
Every Databricks engagement is staffed with certified engineers — Databricks Certified Data Engineer or ML Professional. Not one specialist on the pitch deck and generalists on the ground.
Most shops treat pipelines and ML as separate workstreams. We build them together — Feature Store integration, lineage, and model training pipelines designed as one system.
We build modular, tested, CI/CD-deployed pipelines — not notebooks that work once in a sandbox. Production reliability is the design constraint, not an afterthought.
Whether you're migrating from legacy ETL, consolidating warehouses into a lakehouse, or building net-new — we've done both and can advise on which path fits your situation.
Lakehouses and ML platforms built for teams that rely on them every day.
Built a HIPAA-compliant Delta Lake lakehouse consolidating HL7 FHIR feeds, HL7 v2 messages, and device telemetry from five hospital sites. A medallion architecture with PII masking at the silver layer powers compliance reporting and clinical ML models.
Replaced a batch ETL process that fed recommendation models with a Structured Streaming pipeline updating features in near real-time. Model latency dropped from hours to under 90 seconds, improving personalisation hit rate measurably within weeks of launch.
★★★★★They designed a lakehouse architecture that finally unified our fragmented data sources. Their MLflow setup gave our data science team the experiment tracking they'd been asking for for two years.
Head of Data Science, Healthtech
★★★★★The streaming pipeline they built replaced a batch job that was already causing model staleness complaints. The improvement in recommendation quality was visible in the metrics within the first sprint post-launch.
Director of Engineering, eCommerce
Flexible models whether you need a dedicated lakehouse team, engineers embedded in yours, or a fixed-scope Databricks build.
A cross-functional team of Databricks data engineers, ML engineers, and platform architects working exclusively on your lakehouse or ML platform. Best for companies building core data infrastructure from the ground up.
Embed certified Databricks engineers into your existing data or ML team. They join your workspace, standups, and sprint ceremonies and contribute from day one. Best for scaling capacity without a hiring cycle.
Fixed-scope engagement for a defined lakehouse migration, pipeline rebuild, or ML platform setup. We own the delivery end to end and hand over documented, production-ready code. Best for scoped initiatives.
Structured delivery from architecture design to production handoff.
Audit data sources, current pipeline architecture, ML maturity, and governance requirements.
Agree medallion architecture, Unity Catalog structure, streaming vs batch strategy, and ML platform design before build begins.
Pipeline development, Delta Lake setup, ML platform engineering, and incremental data onboarding.
Data quality checks, schema evolution testing, ML model pipeline validation, and load testing.
Documented pipelines, Unity Catalog setup, runbooks, and knowledge transfer. Your team owns what we built.
Databricks delivers the most value where data volumes are high, ML matters, or batch ETL has become a bottleneck.
Whether you're migrating legacy ETL, building a net-new lakehouse, or standing up a production ML platform — we'll help you design the right architecture before any code is written.
Hello, we are BRILLIAN’S. Trying to make an effort to put the right people for you to get the best results. Just insight !
