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Complete the form below to get instant access to the Modernization Checklist
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Rationalizing legacy sprawl means knowing what to keep, what to retire, and what to rebuild. This checklist walks the three phases that get you there — map the estate, define your data products, then build them. Any source, any target.
Before rationalization, the entire estate is unified into one contextual intelligence layer: ETL transforms mapped to fields, semantic models and lineage, access and usage logs, and documents and SOPs connected to code. Redundancy detection identifies duplicate workloads and reduces costs before migration.
Usage logs identify workloads for rationalization into modular data products. Redundant and dead workloads are flagged for retirement, while active workloads are scored by usage, schema, business rules, and consumption. Clear retirement criteria prevent scope creep, and each product serves multiple consumers.
Our SLMs generate cloud execution code, run automated testing to confirm analytical parity, and pass each product through engineering review before CI/CD. Data quality and governance criteria are defined upfront, with post-launch usage tracking to confirm adoption.
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Enterprise modernizations completed
Faster time-to-market for AI-ready infrastructure
A multinational insurance and financial services provider accelerated legacy application modernization with Databricks and Delta Lake.
Read the case study to see how Next Pathway delivered:
A leading insurance and financial services provider modernized legacy data workloads with Databricks automation.
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ENDERLE GROUP
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THE BLOOR GROUP
Global Financial Firm Modernizes with Snowflake Migration.
Thinking beyond migration?
We help you modernize your data platform, end to end.
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