An international digital Banking platform needed to accelerate its onboarding to Databricks and faced specific and challenging regulatory and data segregation requirements. They required an end-to-end review of their approach to Machine Learning and an efficient data pipeline build approach that could scale to thousands of data sources.
Our cross-functional team of Data Engineers and Data Scientists performed a strategic evaluation of the client’s Databricks implementation to provide actionable recommendations on best practices and feature functionality to improve their data pipeline and ML model deployment workflows. To upskill Data Engineering staff, we were tasked with building an end-to-end data pipeline, including unit testing and data quality validation, as an enablement device to templatize subsequent pipelines.
1. Improved Pipelines:
Key recommendations were accepted into the approach, resulting in more fault-tolerant and scalable ML pipelines.
2. Standardized Processes:
Guidelines were created outlining relevant best practices to be followed by their data engineering team.
3. Increased Efficiency:
An existing Autoloader pipeline was refactored to distribute processing more efficiently, resulting in a 10x improvement in run time.
Adding new systems to your business’s ecosystem isn’t enough; they must be properly implemented and scaled. This is why a trusted partner is the key to any successful project.
The Aimpoint Difference
As a Unified Analytics Consultancy, we comprehensively understand the Databricks Lakehouse ecosystem.
Our ability to provide practical solutions within a condensed timeframe sets us apart. In just four weeks, we can implement a range of value accelerator use cases tailored to your specific needs, meaning you’ll see tangible improvements in your Databricks operations in a remarkably short period.
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