Clear, owned outputs.
- Databricks platform design
- Unity Catalog set-up
- Production pipelines
- CI/CD for Databricks assets
- Cost dashboard and policies
Databricks combines data engineering, analytics and machine learning on a lakehouse built on open formats. Its flexibility means design choices matter: workspace and catalogue structure, cluster policies, pipeline frameworks and cost controls. We set Databricks up so it's governed, efficient and productive for every team using it.
The problems Databricks Solutions solves.
Lakehouse design, Unity Catalog, pipelines, cost control and ML on Databricks.
Clusters run idle and costs are unpredictable.
Notebooks are used in production with no testing or version control.
Access to data is granted inconsistently across workspaces.
Engineering and data science teams work in separate silos.
What Databricks Solutions covers.
Platform set-up
Workspaces, networking, identity and cluster policies.
Unity Catalog
Central governance, permissions and lineage.
Pipelines
Delta Live Tables, jobs and workflows for reliable processing.
Engineering practices
Repos, testing and CI/CD for notebooks and code.
Cost management
Cluster sizing, autoscaling, serverless and usage monitoring.
ML and AI
MLflow, feature engineering and model serving.
The Databricks pieces that matter.
Databricks is a managed Spark platform built around the lakehouse. Most of the value comes from a few features used consistently.
- Delta Lake
- Open table format that adds ACID transactions, schema enforcement, time travel and MERGE to files in cloud storage.
- Unity Catalog
- One place for permissions, lineage and data discovery across workspaces, down to column level.
- Workflows
- Job orchestration for notebooks, Python, SQL and dbt tasks, with retries, alerts and job clusters sized per task.
- Delta Live Tables
- Declarative pipelines with built-in data quality expectations, useful for bronze-silver-gold processing.
- Databricks SQL
- Serverless SQL warehouses for BI tools, so analysts query the lakehouse without managing clusters.
- Cost governance
- Cluster policies, auto-termination, Photon where it pays off and tagging so spend can be traced to teams.
Why it's worth it.
Unified work
Engineers, analysts and data scientists share one governed platform.
Controlled cost
Policies and monitoring keep compute spend predictable.
Production quality
Version control and tests make notebooks dependable.
When Databricks Solutions is the right move.
- A new lakehouse on Azure, AWS or Google Cloud.
- Migrating Spark workloads from Hadoop to Databricks.
- Governing an existing Databricks estate with Unity Catalog.
Technologies & platforms
- Databricks
- Delta Lake
- Unity Catalog
- Delta Live Tables
- MLflow
- Databricks Asset Bundles
- Apache Spark
- Terraform
How Databricks Solutions work runs.
- 01
Discover
Sources, consumers, data quality, volumes and the decisions the data must support.
- 02
Design
Architecture, data model, pipelines and governance agreed in writing.
- 03
Build
Pipelines, models and tests delivered in increments.
- 04
Validate
Reconciliation against source systems and sign-off by data owners.
- 05
Operate
Monitoring, data quality alerts, documentation and handover.
Databricks Solutions questions.
Databricks or Snowflake?
Both are strong. Databricks suits engineering-heavy and ML workloads on open formats; Snowflake suits SQL-centric analytics with minimal administration. Many organizations use both. We recommend based on your workloads.
How do you control Databricks costs?
With cluster policies, autoscaling, job clusters instead of always-on clusters, serverless where it helps, and regular usage reviews.
Can you migrate us from Hadoop?
Yes. Spark workloads often move to Databricks with modest changes; we assess each job and plan the migration.
Talk to us about databricks solutions.
Describe the environment and what needs to change. We'll reply with the questions we would answer first and a sensible first step.