Providing a quantum leap in financial services
Financial services companies spend thousands of dollars on simple data engineering tasks such as setting up new pipelines, adding new data sources or upgrading databases. This means deploying new data science solutions and proof of concepts can be time consuming and expensive.
Case Study
Launching custom data engineering & cloud architecture in a matter of minutes.
Problem: Our client, a $4B hedge fund in New York & London, was in the process of building out a new quantitative research arm. The team consisted of code-based analysts with Python as their primary language. The client had their broader business technology infrastructure in the private cloud with no public cloud footprint.
Solution: We onboarded the client onto our Ingenii Data Engineering Platform & Guided Setup:
Deployment of a powerful Azure Lakehouse Architecture through Ingenii.
Ingestion & orchestration of 3rd party ESG data.
Databricks dashboard development.
Ongoing updates and maintenance.
Access to Ingenii data engineering solution library for future projects.
Results:
Fully automated and cloud based architecture deployed to the client in under 20 minutes (compared to 6-9 months).
Delivered $500k in value for a fraction of the cost and now lives as repeatable code.
Achieved a robust in-house data ecosystem for the client to help them maintain their competitive edge.
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