The Challenge
The client’s data sat in Amazon S3 and needed to be transformed into consistent, analytics-ready datasets that business teams could use without duplicated logic or conflicting definitions.
At the same time, governance had to be robust. They needed clear control over who could access which data, including fine-grained policies that could evolve as teams and use cases expanded.
The Solution
We implemented a Databricks environment on AWS, including external S3 storage connectivity and the required IAM roles, then created the catalog structure to support governed access from the start.
1) A a secure Databricks foundation on AWS
We implemented the Databricks environment on AWS, including external S3 storage connectivity and the IAM roles needed to operate securely at scale. We also created the catalog structure to support governed access from the start.
2) Automated ingestion and curation with Delta Live Tables
To accelerate reliable delivery, we built reusable Delta Live Tables pipelines for Bronze and Silver layers, including schema application, parameterisation, and data quality checks. We then migrated the required entities into Silver, and into Gold where needed for downstream consumption.
3) Embedded governance with Unity Catalog and fine-grained controls
Governance was implemented using Unity Catalog, with a persona-based security access model and fine-grained enforcement via dynamic row filters and dynamic column masks. This enabled secure access across teams without duplicating datasets or creating brittle workarounds.
4) Business value with AI/BI and Genie experiences
To make the platform immediately useful for data consumers, we delivered an AI/BI dashboard for call-volume insight and set up a Genie space so stakeholders could explore the governed data using natural language.
Tools & Technologies
This solution was Delivered on Databricks on AWS, combining S3-based ingestion with Delta Live Tables, Unity Catalog governance, and end-user analytics via AI/BI Dashboards and Genie.
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Databricks on AWS with external Amazon S3 storage and IAM roles for secure access.
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Delta Live Tables (DLT) pipelines with schema application, parameterisation, and data quality checks, curating data into Silver and Gold layers
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Unity Catalog with a persona-based access model, including dynamic row filters and column masks, plus AI/BI Dashboards and Genie for governed self-serve insight.
The Results
The client gained a governed Databricks platform on AWS with clear, repeatable pipeline patterns for ingesting and curating data, reducing operational risk and improving trust in downstream reporting.
- Unity Catalog provided centralised governance, with dynamic row and column controls enabling secure access for different personas, while keeping a single, consistent version of the data.
- On top of the curated datasets, the AI/BI dashboard and Genie space provided an adoption-friendly way for teams to explore call-volume insight, backed by the same governed foundation.
In telecoms, data only creates value when people can use it quickly and safely. This delivery proved that Databricks can support both sides of the equation: scalable engineering patterns (DLT, curated layers, quality checks) and enterprise-grade governance (Unity Catalog, fine-grained access), while also supporting modern analytics experiences like AI/BI and Genie that accelerate adoption.
If your teams are sitting on valuable operational data but struggling with trust, governance, or adoption, we can help you build a Databricks foundation that gets you to reliable insight quickly, without cutting corners on security.
