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[ DATA ENGINEERING ]

FROM RAW DATA, A PIPELINE.
FROM PIPELINES, CLARITY.

Pipelines, lakehouses and streaming systems on Azure and Databricks that make data reliable enough to build on.

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01 / Approach

Data you can trust.

Most organizations don't lack data. They lack data that is consistent, documented and available when it's needed. We build the pipelines and platforms that move data out of operational systems, clean and model it, and deliver it to analysts, applications and machine learning models. Every dataset has an owner, a definition and quality checks.

02 / What we build

Six capabilities, one data platform.

01

Data Platform Architecture

Lakehouse and warehouse architectures designed around how your data is produced and used.

  • Lakehouse
  • Warehouse
  • Medallion layers
02

Data Pipelines & ELT

Batch pipelines that ingest, clean and transform data from operational systems on a reliable schedule.

  • Ingestion
  • ELT
  • Orchestration
03

Real-Time Streaming

Event streams processed as they arrive, for dashboards, alerts and applications that can't wait for the nightly batch.

  • Streaming
  • Events
  • CDC
04

Data Modeling & Analytics

Clean, documented data models and semantic layers that give everyone the same numbers.

  • Dimensional models
  • Semantic layer
  • BI
05

Data Quality & Governance

Automated quality checks, lineage, access control and cataloging, so problems are caught before they reach a report.

  • Quality checks
  • Lineage
  • Catalog
06

ML-Ready Data

Feature pipelines and curated datasets that your data scientists and our Applied AI / ML & Mathematics practice can build on.

  • Feature store
  • Training data

03 / Capability matrix

The technical detail.

Capability Typical question Core techniques Tooling Deliverable
Platform Architecture Where should our data live, and in what shape? Lakehouse design, medallion layers, storage formats Databricks, Delta Lake, Azure Data Lake Storage Target architecture and platform setup
Pipelines & ELT Why does the report break every Monday? Incremental loads, orchestration, error handling, backfills Azure Data Factory, Databricks Workflows, Spark Scheduled, monitored pipelines
Real-Time Streaming Can we react to events as they happen? Event ingestion, stream processing, change data capture Azure Event Hubs, Kafka, Spark Structured Streaming Real-time data streams
Modeling & Analytics Why do two dashboards show different numbers? Dimensional modeling, semantic layers, metric definitions Databricks SQL, Power BI Documented data models and dashboards
Quality & Governance Who changed this table, and can we trust it? Quality rules, lineage, access policies, cataloging Unity Catalog, automated quality checks Governed, cataloged datasets
ML-Ready Data Is our data ready for machine learning? Feature engineering, training datasets, versioning Databricks Feature Store, MLflow Feature pipelines and training data

04 / Method

One use case at a time.

  1. 01

    Assess

    Inventory data sources, consumers and pain points, and agree on the first use case.

  2. 02

    Design

    Define the architecture, data models and quality rules for that use case.

  3. 03

    Build

    Implement pipelines in code, with tests and monitoring from the start.

  4. 04

    Validate

    Reconcile outputs against source systems with the people who use the data.

  5. 05

    Operate

    Monitor freshness and quality, and extend the platform one use case at a time.

05 / Principles

How we build data platforms.

The practices that keep a data platform trustworthy after the first pipeline goes live.

  1. Pipelines as code

    Versioned, reviewed and deployed through CI/CD like any other software.

  2. Quality checks at every layer

    Data is validated on the way in, during transformation and before it is published.

  3. Clear ownership

    Every dataset has a named owner and a written definition.

  4. Lineage you can follow

    Any number in a report can be traced back to its source.

  5. Controlled access

    Sensitive data is classified and access is granted by role.

  6. Cost-aware processing

    Compute is sized to the workload, so you don't pay for idle clusters.

Is your data in too many places?

Tell us where your data lives and who needs it. We'll suggest where to start.

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