Data Engineering

Data engineering focuses on building the infrastructure — pipelines, warehouses, and platforms — that collect, transform, and deliver data. Data science focuses on analysing that data to extract insights and build predictive models. Think of data engineering as building the roads and data science as driving on them.

It depends on your use cases. Data warehouses excel at structured, SQL-based analytics, while data lakes handle unstructured data and ML workloads. Many organisations benefit from a lakehouse architecture that combines both. We help you evaluate and choose the right approach.

We implement automated quality checks at every stage — schema validation on ingestion, row-count and freshness assertions after transformations, and anomaly detection on key metrics. Failed checks trigger alerts and can halt downstream processing to prevent bad data from spreading.

Absolutely. We work with all major cloud data platforms (Snowflake, BigQuery, Redshift, Databricks) and integrate with your existing BI tools, orchestrators, and monitoring systems to build on what you already have.

A single end-to-end pipeline typically takes 2–4 weeks from discovery to production. A full data platform with multiple domains, quality frameworks, and governance can take 8–16 weeks depending on complexity and data volume.