Data Engineering

Lakehouse platforms and pipelines on Databricks and Apache Spark, built by certified Databricks engineers.

Data Engineering

Lakehouse platforms and pipelines on Databricks and Apache Spark, built by certified Databricks engineers.

Data Pipeline Implementation

Reliable batch and streaming pipelines that move data from your source systems into one place, with data quality rules written into the code so bad records are caught at ingestion rather than discovered in a board report.

Tooling:

Lakeflow Declarative Pipelines (formerly Delta Live Tables) and Auto Loader
Spark Structured Streaming for near-real-time feeds
Orchestration with Lakeflow Jobs, Airflow, Azure Data Factory or AWS Glue
API ingestion from third-party platforms

python

1import dlt
2from pyspark.sql.functions import col
3
4@dlt.table(comment="Loan-level records, cleaned and typed")
5@dlt.expect_or_drop("has_loan_id", "loan_id IS NOT NULL")
6@dlt.expect_or_drop("valid_balance", "current_balance >= 0")
7def silver_loans():
8    return (
9        dlt.read_stream("bronze_loan_tape")
10        .withColumn("current_balance",
11                    col("current_balance").cast("decimal(18,2)"))
12    )

Data Lake & Warehouse Implementation

A central store for all of your data, raw and modelled, so analytics, reporting and AI work from the same numbers. We set up a new data lake or warehouse, or combine both as a lakehouse, on the cloud you already use.

Built on:

Databricks lakehouse with Delta Lake storage
Snowflake, Synapse, BigQuery or Redshift where they fit or are already in place
Azure, AWS or GCP object storage as the data lake
Unity Catalog for permissions, lineage and discovery

Agentifying Data Engineering

AI agents that take on the repetitive parts of data engineering, with engineers reviewing and approving what they produce.

Where agents help:

Generating pipeline code and SQL transformations from plain-language requirements
Mapping and documenting new source schemas
Suggesting and writing data quality rules
Investigating failed jobs and proposing fixes
Answering questions about your data through a governed natural-language interface

Medallion Architecture

Data is organised into three layers so every table has a clear owner and a known level of quality.

The layers:

Bronze: raw data exactly as it arrived from the source, kept for replay and audit
Silver: cleaned, typed, deduplicated and joined records
Gold: business-ready tables and metrics for reporting, dashboards and AI

Data Architecture Built to Your Requirements

There is no single right architecture. We design around your data volumes, latency needs, budget, existing tools and compliance obligations, not around a vendor's reference diagram.

What we look at:

Batch, streaming or a mix of both
Cloud, on-premises or hybrid
Which tools to keep, replace or consolidate
Security, data residency and regulatory requirements
Cost at today's volumes and at the scale you expect

Platform Migrations

Moving off Hadoop, legacy SQL Server or Oracle warehouses, or scattered scripts on a VM. Migrations fail when nobody can prove the new numbers match the old ones, so we prove it before cutover.

Method:

Inventory of every job, table and downstream consumer
Old and new pipelines run in parallel
Row counts and key aggregates reconciled table by table
Cutover one domain at a time, with a rollback plan

Governance & Security

Sensitive data such as borrower records, tenant files or patient data needs controls enforced by the platform, not by a policy document.

Controls:

Unity Catalog permissions and lineage
Row filters and column masks for PII
Encryption at rest and in transit, plus audit logs
Designed to support HIPAA, SOC 2 and data residency requirements

sql

1-- Only the pii_readers group sees full tax IDs
2CREATE FUNCTION mask_tax_id(tax_id STRING)
3RETURN CASE
4  WHEN is_account_group_member('pii_readers') THEN tax_id
5  ELSE concat('***-***-', right(tax_id, 3))
6END;
7
8ALTER TABLE silver.borrowers
9  ALTER COLUMN tax_id SET MASK mask_tax_id;

Cost & Performance

Most Databricks bills we review have avoidable spend. We find it and fix it without slowing anything down.

Where the savings come from:

Liquid clustering and file-size tuning
Photon and serverless compute where they're cheaper for the workload
Right-sized job clusters and cluster policies
Idle all-purpose clusters replaced with jobs compute

Data Engineering

Data Pipeline Implementation

Data Lake & Warehouse Implementation

Agentifying Data Engineering

Medallion Architecture

Data Architecture Built to Your Requirements

Platform Migrations

Governance & Security

Cost & Performance

Related capabilities

Data Analytics & BI

Data Science & Machine Learning

Databricks Training