Data Engineering Case Study

Client is a diversified financial services company with approximately $70 billion assets under management, administration and servicing. Its business spans corporate lending, private equity, venture capital, commercial real estate and municipal finance.
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Business Need
  • Client needed to build a BI and Analytics platform in the cloud, which served as the single source of truth. Their environment had 30 disparate application systems storing data in their individual data silos, which was not meeting business needs. There was a need to create a data lake aggregated from source systems, with all reporting leveraging centralized data in the Azure cloud.
Our Solution
  • Engaged with client stakeholders to assess needs, develop migration roadmap, and implementation plan. Solution architecture we developed required ingesting data from various source systems(on -prem, databases, salesforce, email) via Azure Data Factory into data lake. We also used Databricks, Azure functions and Logic apps in this implementation. We transitioned BI platform from Spotfire to Power BI while minimizing business disruption. All data, reports and dashboards were successfully migrated.
Business Benefit
  • A primary challenge we solved with our solution was one of data integration. With all the data residing in performance-optimized data lake, the solution has enabled the ability to create machine learning, and artificial intelligence workloads as needed. Our solution also enabled the scenario of self-service BI to data analysts and data citizens.
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