Databricks lakehouse engineering
Databricks Lakehouse Engineering
Build governed lakehouse pipelines, analytics and machine learning on Databricks.
Databricks is a data and AI platform built on Apache Spark and Delta Lake, available on Azure, AWS and Google Cloud. We design lakehouse architectures, build pipelines and jobs, set up Unity Catalog governance, and keep compute costs under control.
What we cover
- Lakehouse architecture
- Delta Lake tables
- Unity Catalog governance
- Lakeflow pipelines and jobs
- MLflow and machine learning
- Cluster and cost management
Requirement-led advice · Documented handover
- Advice based on your requirements
- Works with your existing stack
- Documented, maintainable delivery
What Is Databricks?
Databricks is a lakehouse platform: it stores data in open Delta Lake tables on cloud object storage and lets engineers, analysts and data scientists work on the same data with Spark, SQL and Python. Databricks SQL provides warehouses for BI tools, notebooks and jobs handle data engineering, and MLflow tracks machine learning experiments and models.
Unity Catalog adds central governance: a single place for permissions, lineage, auditing and data discovery across workspaces. Lakeflow covers ingestion, declarative pipelines (formerly Delta Live Tables) and job orchestration, and serverless compute reduces the need to manage clusters. Databricks Asset Bundles let teams define jobs and pipelines as code for CI/CD.
When Databricks is a good fit
- You process large data volumes or streaming data with Spark.
- You work across more than one cloud, or mainly on AWS.
- Data engineering and machine learning teams need to share one platform.
- You want open table formats and strong engineering tooling.
What Teams Build on Databricks
Typical Databricks workloads.
Medallion lakehouse
Bronze, silver and gold Delta tables with quality checks.
Streaming pipelines
Near real-time processing with Structured Streaming.
BI on the lakehouse
Databricks SQL warehouses for Power BI and other tools.
Machine learning
Feature engineering, training and tracking with MLflow.
Governed data sharing
Unity Catalog permissions and Delta Sharing.
Hadoop and warehouse migration
Move legacy Spark, Hive or warehouse workloads.
Cost optimisation
Right-size compute and tune job performance.
Data quality
Expectations and monitoring on critical tables.
Databricks Services We Deliver
Each piece of work has a clear output, so you know what you will receive.
Architecture and roadmap
Design workspaces, catalogs, environments and data layers.
Deliverables: architecture, roadmap, cost estimate
Unity Catalog setup
Catalogs, schemas, permissions and lineage.
Deliverables: governance model, configured catalog
Pipeline development
Lakeflow pipelines, jobs and notebooks with tests.
Deliverables: pipelines, jobs, documentation
DevOps for Databricks
Asset Bundles, repositories and CI/CD.
Deliverables: bundle definitions, release pipeline
Performance and cost
Tune jobs, clusters and storage layout.
Deliverables: tuning report, cost savings plan
Migration
Move from Hadoop, older Spark or warehouses.
Deliverables: migration plan, migrated workloads
Databricks Platform Components
Parts of Databricks we work with.
Storage and tables
- Delta Lake
- Unity Catalog volumes
- Liquid clustering
- Delta Sharing
Engineering
- Lakeflow Declarative Pipelines
- Lakeflow Jobs
- Notebooks
- Structured Streaming
Analytics and AI
- Databricks SQL
- MLflow
- Model serving
- Feature engineering
Operations
- Unity Catalog permissions and lineage
- Serverless compute
- Databricks Asset Bundles
- System tables for cost and usage
Listing a feature here does not mean every project uses it. We recommend what fits your requirements, skills, licensing and running costs.
How Databricks Fits Your Stack
Engineering on Databricks is mostly PySpark and SQL. Data arrives through Lakeflow Connect, Azure Data Factory or other tools, and results feed Power BI and applications.
Databricks runs on Azure and AWS. Many Microsoft-centred organisations combine it with Microsoft Fabric, for example by sharing Delta tables. For dedicated engineers, see Hire Data Engineers.
Implementation Considerations
- Set up Unity Catalog before teams create many tables.
- Use job and serverless compute instead of always-on interactive clusters.
- Define jobs and pipelines as code for repeatable releases.
- Agree layer and naming conventions across teams.
- Monitor costs with system tables and tags.
- Plan storage layout and clustering for the queries you actually run.
How We Work With Databricks
The same four steps we use across our technology work, adapted to your scope.
Discover
Goals, users, constraints and the systems Databricks must work with.
Select
Confirm Databricks is the right fit and agree the architecture and scope.
Build and integrate
Deliver in short iterations with testing, reviews and documentation.
Operate and improve
Monitor, support and improve the solution as your needs change.
Databricks Questions, Answered
What is Databricks?
A data and AI platform based on Apache Spark and Delta Lake, available on Azure, AWS and Google Cloud.
What is Unity Catalog?
Databricks' central governance layer for permissions, lineage, auditing and discovery across workspaces.
What happened to Delta Live Tables?
Delta Live Tables is now part of Lakeflow as Lakeflow Declarative Pipelines.
Databricks or Microsoft Fabric?
Fabric suits Microsoft-centred teams wanting SaaS simplicity. Databricks suits heavy engineering, ML and multi-cloud. Some organisations use both.
How do you control Databricks costs?
Job and serverless compute, auto-termination, cluster policies, tagging and cost monitoring.
Can you migrate from Hadoop?
Yes. We assess Hive, Spark and job workloads and move them in stages.
Planning a Databricks Platform?
Tell us about your data volumes, cloud and goals, and we will suggest a practical design.
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