Map every source
Each source listed with its owner, fields, volume, refresh rate and how it identifies customers, before any pipeline is designed.
4.9 / 5Databricks data pipelines that run without a babysitter.
Databricks data pipelines that move customer data from source to analysis on schedule, with every source mapped and every table checked.
Selected work, in motion.
The digital experiences that connect to a wider business system.Reports built on hope.
When data arrives late or wrong, every report downstream inherits the problem.
BeforeAfter
Full reloads every night that take hoursIncremental loads that process only what changed
Sources nobody documentedA source map with owners, fields and refresh times
Schema changes breaking pipelines silentlySchema evolution handled, with alerts on breaking changes
Failed runs found days laterExpectations, retries and run alerts on every pipeline
What's included
Source to table, reliably.
We design pipelines for the data your website, product and marketing teams depend on.
Design the pipeline
Declarative pipelines or Jobs chosen per source, with incremental loads, medallion layers and schemas planned for change.
Check data quality
Expectations for nulls, duplicates, ranges and freshness, with failing rows quarantined and a clear record of what was dropped.
Orchestrate the runs
Dependencies, schedules and retries set in Databricks Jobs, with alerts to Slack or email when a run fails or runs late.
Security, by consideration.
Trust is part of the build.
For your Databricks project, we consider data sources, workspace permissions, pipelines and downstream consumers as part of one connected build.
See our approachConnected systems. Considered safeguards.
Changes you can trace.
GitHub workflows, peer review and dependency checks bring decisions into the release process.
Protected in transit.
Certificate configuration, HTTPS and secure headers help protect the path between your users and your systems.
Intelligence with limits.
Scoped tool access, validated inputs and human approval where automated actions carry consequences.
Map, build, prove.
A pipeline is finished when its numbers match the source, not when it runs.
- 01
Map the sources
Sources, owners, volumes and refresh needs documented and agreed.
- Source map
- Refresh requirements
- Table design
- 02
Build the pipelines
Ingestion, transformations and expectations built and tested on real data.
- Pipeline code
- Delta tables
- Expectation rules
- 03
Reconcile and schedule
Counts matched against sources, then pipelines scheduled and monitored.
- Reconciliation report
- Job schedule
- Run alerts
Built for what’s next.
Built to driveMore sign-ups.
Built to driveMore memberships.
Built to driveBetter-qualified leads.
Built to driveMore qualified candidates.
Built to driveMore enquiries.
Built to driveMore qualified demos.
Built to driveMore workplace demos.
Built to driveMore business accounts.
Built to driveMore free trials.
Built to driveMore building owners.
Built to driveMore free sign-ups.
Private Villas MexicoBuilt to driveMore direct bookings.
Built to driveMore committed players.
Built to driveMore returning customers.
Built to driveMore meaningful demos.
Built to driveBetter talent connections.
Built to driveMore course creators.
Built to driveMore product discovery.
Trusted across our practice
Working together. In their words.
Independent reviews. Long-term working relationships.
“We’ve been working with Stega & Gabriel for the last two years. Cannot recommend them enough. They’re responsive, professional & an asset.”
“After a website (re)launch, they are also always accessible and happy to help. I highly recommend everyone who is looking for a new and more stable website in Webflow, to work with Stega!”
“Stega Creative team not only understood our needs and vision for the websites, but they also came up with improvements.”
Databricks pipeline questions.
A few things worth discussing before your Databricks data pipelines takes shape.
What is the difference between Delta Live Tables and Databricks Jobs?
Delta Live Tables, now called Lakeflow Declarative Pipelines, lets you declare tables and quality rules while Databricks manages dependencies and incremental processing. Databricks Jobs orchestrate any task, including notebooks, scripts and those pipelines, on a schedule. We often use declarative pipelines for transformations and Jobs to run everything around them.
How do you handle data quality in Databricks pipelines?
Data quality in Databricks pipelines is handled with expectations that check each row against rules, such as no missing IDs or valid dates. Failing rows can be dropped, quarantined or allowed to stop the pipeline. We pick the action per rule, based on how much damage a bad row would do downstream.
Can Databricks pipelines load data incrementally?
Databricks pipelines load data incrementally with Auto Loader for files and change data capture for databases, processing only new or changed records. Incremental loads cut compute cost and run time compared with full nightly reloads, which matters once event data grows into millions of rows.
What do Databricks data pipelines cost to run?
Cost depends on data volume, refresh frequency and compute type. Incremental loads and serverless jobs keep it down, and we estimate compute before the build starts.
Revamp today.The 
way.
Your ambition. Our collective craft.
Strategy, design and technology, working as one.