Lakehouse pipelines
Bronze, silver and gold Delta tables built with Spark and Delta Live Tables, with data quality expectations.
Data and DevOps
Databricks development.
Databricks combines a data lake and warehouse in one platform, with Spark, Delta Lake and managed machine learning tools. We use it for large-scale data engineering and ML where many teams share governed data.
A short call. Your first build, a fixed quote, and the timeline. No obligation.
What we build with Databricks
Where it fits.
Governed data access
Unity Catalog setups controlling who can see which tables, columns and models.
ML workflows
Feature engineering, MLflow tracking and model serving in one workspace.
Streaming ingestion
Structured Streaming jobs and Auto Loader bringing in files and events continuously.
When it is the right choice
Choose Databricks when
- →Your data is large and Spark-scale processing is needed.
- →Data engineers and data scientists share the same platform.
- →You need table-level governance and lineage.
Worth knowing
Honest trade-offs.
- →Clusters left running or oversized drive up usage fast, so we set policies and auto-termination.
- →It can be more than a small team needs when a warehouse alone would do.
Often paired with
Apache SparkDelta LakeMLflowPythondbtAzure
Proof
Work built by the same team.
Closeum
13+ connected modules from calls through commissions
13+connected modules
5role types
32completed transactions
40+leads managed
SIIMS
Orders, production, warehouse, and dispatch on the same records
4operational roles
37backend tests passed
17hosted workflow tests passed
Ledgerline
603 tests passed in a dated financial-control verification
SAR 760Kcash reconciled end to end
603tests passed
14control findings fixed
3finance sources in one ledger
Common questions
Before you start.
Databricks or Snowflake?
Databricks is stronger for heavy data engineering and ML on open formats. Snowflake is often simpler for SQL analytics, and some companies use both.
Is it only for big companies?
No, but its value shows when data volume or team count grows. For small data, a simpler stack may be better.
Which cloud does it run on?
It runs on AWS, Azure and Google Cloud. We build it in the cloud you already use.
What do you build with Databricks?
Data lakehouses, ML pipelines, Large-scale analytics, Feature stores, and related work. We choose Databricks when it is the right fit for the product, the team and the budget.
Can you work with our existing Databricks code?
Yes. We can review what you have, fix what is fragile, and extend it, or build alongside your team.
Keep exploring
More than Databricks.
Other data and devops
Where we apply it
The Audit
Get my free AuditKnow what to build first.
Book a short call and show us one workflow. You leave with what we would build first, a fixed quote, and what should wait.