Data platformsCase study
Azure Data Warehouse and DataOps
A centralised Azure data warehouse with DataOps practices, bringing iLA's SaaS and business systems together for consistent, self-service reporting.
Before
- Reports built by hand in a mix of tools
- Data spread across SaaS applications, including Employment Hero
- Inconsistent organisation-wide reporting
- Hard to establish consistent data management
After
- A centralised Azure data warehouse
- Automated, metadata-driven ingestion
- Curated, reporting-ready data for self-service
- DataOps for reliable, repeatable operation
01 · The challenge
Where they started
iLA is a not-for-profit organisation serving individuals seeking to live more independent lives. Without a central data platform, it was hard to get consistent insight from data spread across many systems.
Manual reporting
Reports were developed by hand in a range of reporting tools and the native reporting of multiple SaaS applications.
Data spread across systems
With data distributed across different systems, including Employment Hero, consistent organisation-wide reporting was difficult.
Limited self-service
Enabling self-service analytics was increasingly challenging without a trusted, central source.
Governance gaps
The lack of a central platform made it hard to establish consistent data management and improve governance.
02 · The solution
What we built
DATA LEAGUE implemented an Azure data warehouse to establish a central foundation for reporting and analytics.
How it fits together
- SaaS and business systemsIncluding Employment Hero
- Azure Data FactoryMetadata-driven ELT
- Medallion layersIngest, transform, curate
- Data warehouseReporting-ready data
- Self-service reportingPower BI
Metadata-driven ELT with Azure Data Factory
Automates and standardises data ingestion and transformation.
Medallion architecture
Ingestion, transformation and curated layers, ending in reporting-ready data for self-service analytics.
SaaS integration
Brings together data from multiple SaaS and business systems, including Employment Hero.
DataOps practices
Supports reliable, repeatable and maintainable operation of the platform.
03 · Delivery
How we delivered it
- 1
Understand
Mapped the source systems, reports and data management practices.
- 2
Build
Implemented the warehouse, the medallion layers and the metadata-driven ELT framework.
- 3
Integrate
Connected SaaS and business systems, including Employment Hero.
- 4
Operate
Introduced DataOps practices for reliable, repeatable releases and operation.
04 · Results
What changed
One consistent foundation
A central data platform for organisation-wide reporting.
Less manual effort
Far less work extracting and consolidating data from multiple systems.
Reusable framework
The metadata-driven framework supports new data sources and reporting needs.
Better self-service
An improved foundation for self-service reporting and analytics.
Maintainable operations
DataOps practices make platform operations more consistent.
Clearer governance picture
A clearer understanding of governance maturity and where to improve.
In the client's words
Vilko Poznovia
Manager DS & ICT
Indigo
- Engagement
- Data warehouse setup with a full DataOps implementation
- Outcome
- A markedly more secure environment
- Delivery
- A streamlined deployment pipeline
We engaged DATA LEAGUE across two significant projects, and the experience has been outstanding from start to finish. …
For our iLA platform, DATA LEAGUE and the team delivered a comprehensive data warehouse setup with a full DataOps implementation. The result was a markedly more secure environment and a streamlined deployment pipeline that has improved how we manage and release data infrastructure. The consultants brought deep technical expertise, clear communication, and a pragmatic approach to both engagements. We would not hesitate to recommend DATA LEAGUE to any organisation looking for a trusted data engineering partner.
- Full DataOps
- More secure
- Faster releases
- Clear and pragmatic
05 · Technology
Built with
- Azure Data Factory
- Azure data warehouse
- Medallion architecture
- DataOps
- Employment Hero
- Power BI
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