TL Consulting Group

Data & Analytics Transformation For A Global Retailer to Enable ML/AI capabilities

What We Achieved

Reduced Time-to-Insight

By designing and building a unified data platform that is able to manage data from multiple sources we have enabled faster and more comprehensive decision making processes with better collaboration between business domains. 

Scalable Data Ecosystem

Established a scalable and reusable medallion architecture for data ingestion, enhancing the retailer’s capability to seamlessly integrate diverse data sources. This framework supports scalable growth and adaptability, facilitating efficient data management and analytics as business needs evolve.

Increased Operational Efficiency

Streamlined operations by implementing Delta Load Management and a CI/CD pipelines using Azure DevOps, which automates data changes deployed into the data platform environment. This reduces manual efforts, enhances data reliability, and accelerates the deployment of new features, directly lowering operational costs and accelerating time-to-market.

Industry

  • Global Retailer

Technologies Used

  • Azure Data Factory
  • Azure Databricks
  • Unity Catalog
  • Delta Lake
  • Azure Blob Storage
  • Azure Key Vault
  • Azure DevOps (CI/CD)
  • Unity Catalog (With Databricks)
  • Great Expectations (Data Quality)
  • Tableau Server

Timeframe

  • Project Delivered in 12 weeks.
TL Consulting's Professional Services team were highly professional in their engagement and provided strong expertise and capability that we needed. They have a strong delivery focus and have provided the right guidance & expertise to our team and we look forward to working with them in the future.
Global Retailer
Executive IT Director

The Challenge

TL Consulting were engaged with a Global Retailer to design, build, and operationalise a Modern Data Analytics platform using Data Lakehouse technology with Databricks on the Azure platform.

One of the key visions for the platform was to enable open-source tools/platforms to be integrated that enable data quality and security, while providing enrichment on all data sources driven by robust data governance & metadata management.

The customer at the time were facing several business challenges & pain-points which are summarised below: 

  • Slow time-to-insight with existing datasets and complex data ingestion processes, leading to inefficient and poor decision making to drive business value.
  • Lack of data unification with several data sources each with its own complexity & heterogenous structures which were not ingested, standardised and enriched to derive information and drive valuable insights & analytics into the respective business domains (i.e., Finance & Supply Chain Management).
  • Lack of capability across data platforming & engineering particularly with Microsoft Azure to drive cloud-scale analytics.  

The Data platform needed to support various datasets ingested using various ingestion patterns from multiple source systems including material master source, distribution centre data, customer payment transactional and financial data, with a required capability for data science teams to bring their own data (BYOD) to leverage self-service analytics.

The Solution

The solution delivered was meta-data driven using ELT design methodology. One of the key capabilities for the platform was to enable open-source tools/platforms to be integrated that enable data quality and test automation frameworks to enable these capabilities. TL delivered this engagement following a top-down strategic approach with the solution underpinned by industry best practices and architecture principles.

The Outcomes

  • Data Platform Solution Design & Build – In alignment to the business goals & objectives with clear definition of each technology component and architectural design patterns
  • Delivered re-usable data ingestion patterns – including a medallion architecture to ingest, transform and enrich historical and delta loads (supporting files, database and REST API), this supports pattern reusability and scalability for future data sources.
  • Configured Delta Load Management using Autoloader within Databricks as a control framework to handle delta ingestion loads
  • Unified Metadata Management – Encompassing Automated Data Quality & Test Automation workflows integrated with Unity Catalog
  • Implemented Data Vault Modelling – to build a data warehouse for enterprise scale analytics.
  • Designed the CI/CD workflow – using Azure DevOps to automate code integration, testing & deployment to ensure rapid and reliable delivery
  • Implemented Microservice-orientated design patterns – enabling cloud-native, modular architecture to handle specific functions and therefore enhancing agility and resilience, enabling services to run autonomously and evolve independently when necessary.

Ready to enhance decision-making and operational efficiency with advanced data solutions? Contact us to see how we can build, transform and enrich your data using Microsoft Azure and Databricks, just as we did for this global retailer.

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