Powering a future-ready data strategy through automated ETL conversion to Azure Data Factory

Find out how FulkrumCloud helped a global research and analytics leader modernize its data estate with automated ETL conversion to Azure Data Factory, strengthening the foundation for downstream analytics and AI.

3-min read

Overview

As a global research and advisory leader, the organization relies on vast volumes of published data to power its business. However, much of this data flowed through hundreds of legacy Informatica jobs in the form of complex, fragmented pipelines built over years of iteration. To modernize its data estate and strengthen the foundation for downstream analytics and AI, the company needed to migrate these pipelines to Azure Data Factory (ADF) at scale.

By automating ETL conversion and validation, FulkrumCloud enabled the company to transition to ADF faster, streamline data operations, and deliver trusted, consistent insights to clients.

Key Challenges

  • Large legacy estate with inconsistent transformation patterns and duplicated data.
  • High operational cost from manual discovery, cleansing, and maintenance of brittle Informatica jobs.
  • Inability to confidently pursue advanced analytics and AI because of unreliable upstream pipelines.
  • Verifying parity between legacy Informatica outputs and new ADF pipelines was labor-intensive and error-prone

Solution

For the global research and analytics organization, data quality defines brand credibility. So, speed alone wasn’t enough. They needed a way to accelerate ADF migration while preserving the integrity of every record. Achieving this balance of agility and assurance demanded an automated, governed modernization approach.

Here’s how FulkrumCloud made it possible:

  • Prioritize what matters (CloudSense):: Assessed the 800+ Informatica job inventory, identified reusable transformation patterns, and prioritized workloads to create a value-first modernization roadmap.
  • Automate the heavy lifting (CloudForge): Reduced repeat engineering by reusing existing logic instead of rebuilding transformations from scratch, and minimized human error through automated Informatica-to-ADF mapping for pipelines and data flows.
  • Validate outcomes (CloudSure):: Ran automated row-by-row and column-by-column comparisons with continuous reconciliation to confirm data parity, capture exceptions, and embed governance, ensuring accuracy and confidence in the new ADF outputs.

Benefits

40% reusable logic:

Accelerated migration timelines and reduced costs by reusing existing transformation patterns instead of rebuilding from scratch.

Reduced manual discovery time

Freed data teams from repetitive discovery tasks to focus on higher-value analytics and innovation.

70% reduction in rewrite effort

Minimized engineering overhead, cutting modernization effort and total cost of ownership.

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Informatica ETL Conversion to Azure Data Factory | FulkrumCloud Case Study