Databricks consulting & development
We build Databricks lakehouses that turn messy data from your apps, files and devices into clean, governed tables — ready for reporting, machine learning and generative AI, on AWS, Azure or Google Cloud.
TECHTARN is a Databricks consulting and development partner for companies in the USA, UK, UAE, India, Canada, Australia and Singapore. Our Databricks developers design lakehouses, build Spark pipelines, set up governance with Unity Catalog and put machine learning into production.
Databricks combines data engineering, SQL analytics and AI in one platform, with data stored as open Delta tables in your own cloud account. We pair it with Python, PyTorch and MLOps practices, and with Snowflake where that fits better.
Most Databricks projects refine data in three layers. Step through a tiny order feed and see what each layer does.
| order | country | date | amount | |
|---|---|---|---|---|
| 1001 | ··priya@shop.com | IN | 2026-03-02 | 120.00 |
| 1002 | LIAM@SHOP.COM | UK | 02/03/2026 | 75.5 |
| 1002 | LIAM@SHOP.COM | UK | 02/03/2026 | 75.5 |
| 1003 | null | US | 2026-03-03 | 210.00 |
| 1004 | sara@shop.com | in | 2026-03-03 | 60 |
| 1005 | omar@shop.com | AE | 2026-03-04 | -5 |
| order | country | date | amount | |
|---|---|---|---|---|
| 1001 | priya@shop.com | IN | 2026-03-02 | 120.00 |
| 1002 | liam@shop.com | UK | 2026-03-02 | 75.50 |
| 1004 | sara@shop.com | IN | 2026-03-03 | 60.00 |
2 rows quarantined (blank email, negative amount) · 1 duplicate dropped
| country | orders | revenue |
|---|---|---|
| IN | 2 | 180.00 |
| UK | 1 | 75.50 |
Raw data lands exactly as it arrived — duplicates, blank emails, mixed date formats and all. Nothing is lost, so you can always reprocess.
Cleaned and conformed: duplicates removed, emails lower-cased and trimmed, dates standardised, invalid rows quarantined with a reason.
Business-ready: aggregated into the tables that dashboards, reports and ML models read.
Every change to a Delta Lake table is saved as a new version. You can query an older version, audit who changed what, or restore a table in seconds after a bad update.
Pick a version to see the table as it was.
SELECT * FROM stock VERSION AS OF 4
| product | units |
|---|
Spark splits a big job into small tasks and runs them in parallel across worker machines. Add workers and watch the waves of work shrink.
Simplified: 48 tasks, 4 cores per worker, each core runs one task at a time. Real jobs also depend on data skew, shuffles and I/O.
Many models never leave a notebook. On Databricks, MLflow and Unity Catalog connect every step — from the first experiment to a monitored endpoint — so models can be retrained and redeployed safely.
Data scientists try features and algorithms in notebooks.
MLflow logs parameters, metrics and artefacts for every run.
The best model is versioned in the registry inside Unity Catalog.
Model Serving exposes it as a scalable REST endpoint.
Lakehouse Monitoring watches drift and quality, triggering retraining.
More on MLOps and AI development.
| customer_id | |
|---|---|
| country | |
| lifetime_value |
Unity Catalog organises data as catalog, schema and table, with one place to grant access. Column masks hide personal data, row filters limit regions, and lineage shows where every number came from.
Switch roles to see the same customer table through different eyes.
Both are excellent. Answer five questions to see which way your needs lean — a starting point for a conversation, not a verdict.
Do you plan to build ML or AI models on the same data?
Is most of your work SQL dashboards and reporting?
Do you process streaming or very large semi-structured data?
Do you want a mostly hands-off, SQL-first warehouse?
Do you want open formats (Delta, Parquet) in your own cloud storage?
Answer the questions to tip the scale.
From a first workspace to a governed, cost-controlled data and AI platform.
Delta Lake on your cloud storage, workspaces, networking and a medallion design that fits your data.
Batch and streaming pipelines in PySpark, SQL and Lakeflow Declarative Pipelines (formerly Delta Live Tables).
Catalogs, permissions, row filters, column masks, lineage and audit across workspaces.
SQL warehouses and dashboards, plus connections to Power BI, Tableau and Looker.
Feature engineering, experiment tracking, model registry, serving and monitoring.
Retrieval-augmented apps and agents with Vector Search, built on governed data.
From Hadoop, Informatica, SSIS, Talend or hand-written ETL to Databricks — in planned phases.
Cluster policies, autoscaling, serverless, Photon and job tuning to keep spend predictable.
See also Kubernetes, TensorFlow, custom software and our full technology stack.
Run a cell to read the answer.
Tell us about your data sources, users and goals. We'll reply with honest advice and suggested next steps.