Lakehouse architecture
We design Lakehouse architecture based on Delta Lake, Delta Live Tables, Auto Loader and the medallion architecture model - bronze, silver, gold. The goal is a scalable data layer built for analytics, machine learning and AI.
Databricks - Lakehouse Platform and ML/AI
We deploy the Databricks Lakehouse Platform as a shared environment for data, machine learning and AI. We work with Unity Catalog, Delta Lake, MLflow and Mosaic AI to bring data engineering, analytics and AI models together on one platform.
What your organisation gains
One platform for data and ML
Databricks reduces silos between data engineering, data science, analytics and AI teams. Data, models, experiments, notebooks and production processes can run on a shared platform with a unified governance model.
Open standards and lower vendor lock-in risk
Delta Lake, Apache Iceberg, Parquet and MLflow support working on open standards. This reduces the organisation's dependency on a single vendor and preserves greater architectural flexibility over the long term.
Scaling ML in production
MLflow, Model Registry, Model Serving and Feature Store support the full model lifecycle - from experimentation, through validation and registration, to deployment and monitoring in the production environment.
Mosaic AI and AI agents
Mosaic AI enables the build of AI solutions and AI agents with native access to the organisation's data. Vector Search, Agent Framework and AI Guardrails support building RAG solutions, assistants and agents that operate on a controlled data layer.
What we deliver on this project
Unity Catalog - governance
We implement Unity Catalog as a central governance layer for data, ML models and notebooks. Scope may include row- and column-level access control, lineage, audit trail and rules for how different teams work with data.
ML/AI pipeline in MLflow
We configure ML processes in MLflow: experiments, Model Registry, Model Serving, A/B testing and model monitoring. This lets the organisation manage the full model lifecycle - from experiment to production deployment.
Mosaic AI - AI agents and RAG
We implement Mosaic AI components such as Vector Search, Agent Framework and AI Guardrails. Scope may include building RAG solutions, assistants and AI agents integrated with Unity Catalog and a controlled data layer.
Data engineering - Delta Live Tables
We design declarative ELT pipelines using Delta Live Tables, data quality rules and incremental processing. The goal is less manual code and greater control over data flow quality and reliability.
SAP / SaaS / on-premise integrations
We integrate Databricks with SAP Datasphere, SAP BW, SAP S/4HANA, Salesforce, Workday, Kafka, message queues and on-premise systems. Where needed, we also build custom connectors.
How we deliver projects in this area
A Databricks project begins with architectural decisions: workspace topology, security model, Unity Catalog configuration, naming standards, and working practices for data engineering, data science and AI teams. We then launch a data engineering MVP for one or two data domains, typically using medallion architecture. At this stage we configure the initial data flows, quality rules, monitoring and governance. Next we prepare ML/AI enablement: an example end-to-end model in MLflow, a model registry, a deployment process and rules for monitoring models in production. A full Databricks rollout across the organisation typically takes 6-12 months, depending on the number of data domains, governance requirements, integrations and the maturity of data engineering and AI teams.
Technology stack
DatabricksAzure DatabricksDelta LakeDelta Live TablesUnity CatalogMLflowMosaic AIApache SparkApache IcebergdbtPhotonDatabricks SQLGenieLakeflow ConnectVector Search
The team's certifications in Databricks, data engineering, machine learning, AI and enterprise systems confirm SNOK's readiness to deliver Databricks projects end to end.
Where we have delivered similar solutions
Financial sector company
Databricks Lakehouse for risk analytics: Delta Lake and MLflow as the foundation for credit models.
Industrial manufacturer
IoT analytics on Databricks: SCADA data, predictive maintenance and integration with SAP.
Technology company
Mosaic AI for customer agents: Vector Search and Agent Framework with access to product data.
FAQ - Databricks
Databricks or Snowflake? +
Databricks is usually the better choice for ML-first organisations with significant needs in data engineering, ETL, machine learning and large-scale data analytics. Snowflake more often suits SQL-first organisations focused on BI and self-service analytics. In practice, some organisations use both - Databricks for data engineering and ML, Snowflake for BI and self-service analytics.
Do we need Apache Spark skills? +
Yes, Apache Spark expertise remains important for advanced data engineering. At the same time, Databricks SQL and Delta Live Tables allow some scenarios to be handled without deep Spark knowledge, particularly for analytics and BI teams.
Is Databricks cheaper than Snowflake? +
This depends on the workload profile. Databricks tends to be more cost-effective where compute, ETL, model training and big-data processing dominate. Snowflake tends to be more cost-effective for SQL-heavy, BI and self-service analytics workloads. SNOK helps compare TCO for the client's specific scenario.
What about Unity Catalog vs Snowflake Horizon? +
Both solutions support data governance. Unity Catalog is deeply integrated with ML/AI processes in Databricks and covers data, models and notebooks. Snowflake Horizon is firmly embedded in classic governance for SQL and analytical data environments. The choice depends on the organisation's dominant way of working: ML-first, SQL-first or hybrid.