Modernizing a Leading Pension Fund’s Member Services Platform: From MSSQL & Talend to Snowflake

Delivering a two-phase modernization of a leading pension fund's Member Services platform from MSSQL, Talend and UC4 to Snowflake, Python and Apache Airflow.

Next Pathway is an Elite Snowflake Partner, trusted by global enterprises to deliver end-to-end modernization, from deep discovery to production cutover.

Case study summary: MSSQL and Talend to Snowflake in 8 months

A leading Canadian pension fund modernized its Member Services platform, moving from a fragmented ecosystem of MSSQL, Talend, Python on Kubernetes and UC4 scheduling to a scalable, cloud-native architecture on Snowflake in 8 months. Across two phases, Next Pathway migrated 5,000+ MSSQL database objects (tables, views and procedures) to Snowflake, converted 184 Talend jobs to Python with Next Pathway's NEXA Platform, repointed 155+ Python ETL jobs and 1,500+ SQL files to Snowflake, and replaced UC4 scheduling with Apache Airflow DAGs.

Results: Unified architecture on Snowflake; legacy Talend and UC4 tooling fully retired; improved performance and scalability of data pipelines; simplified orchestration and ETL management; faster turnaround for development and defect fixes.

AI readiness: A standardized Snowflake, Python and Airflow platform ready for faster analytics delivery and future AI initiatives.

Download your free case study

Get the full story of how a leading pension fund moved its Member Services platform from MSSQL and Talend to Snowflake in 8 months.

By providing information in this form, you agree to Next Pathway’s Privacy Policy

What Next Pathway's Pension Fund Member Services Case Study Covers

A leading pension fund modernized its Member Services platform by migrating from MSSQL, Talend and UC4 to Snowflake, Python and Apache Airflow. Next Pathway's NEXA Platform automated code translation, standardized ETL, and built a scalable cloud-native platform for analytics and AI.

Member Services Platform Modernization

Consolidated a fragmented legacy ecosystem across MSSQL, Talend, Python on Kubernetes and UC4 scheduling into a single, standardized architecture on Snowflake, without disrupting live member services operations.

Automated Talend to Python Migration

Converted 184 Talend jobs to Python with Next Pathway's NEXA Platform, migrated 5,000+ MSSQL database objects, and repointed 155+ Python ETL jobs and 1,500+ SQL files to Snowflake across two delivery phases.

Cloud-Native Foundation for Analytics and AI

Replaced UC4 with Apache Airflow DAGs and standardized orchestration and ETL management, creating a foundation for faster analytics delivery, improved data consistency, and future AI initiatives.

1 Billion+

Lines of code translated automatically

160+

Enterprise modernizations completed

80%

Faster time-to-market for AI-ready infrastructure

MSSQL and Talend to Snowflake Migration: frequently asked questions

Answers drawn from the pension fund Member Services case study.

How long did the MSSQL and Talend to Snowflake migration take?

8 months, delivered in two phases. The pension fund moved its Member Services platform from MSSQL, Talend, Python on Kubernetes and UC4 scheduling to Snowflake, Python and Apache Airflow without disrupting live member services operations.

What was migrated and converted?

5,000+ MSSQL database objects, including tables, views and procedures, were migrated to Snowflake. 184 Talend jobs were converted to Python with Next Pathway's NEXA Platform, and 155+ Python ETL jobs and 1,500+ SQL files were repointed to Snowflake.

How was the work split across the two phases?

Phase 1 covered repointing, database migration and an MVP conversion of 25 Talend jobs to validate the approach. Phase 2 delivered full-scale Talend conversion and Apache Airflow DAG development to replace UC4 orchestration.

How was UC4 scheduling replaced?

UC4 orchestration was fully replaced with Apache Airflow DAGs, giving the platform simplified orchestration and ETL management through unified frameworks on Snowflake, Python and Airflow.

How was data integrity maintained during the migration?

Data integrity and consistency were ensured across parallel development and testing cycles, with unit testing and SIT Lite across migration phases, and defect remediation and QA support through SIT and UAT.

What Industry Analysts Say

Rob Enderle

ENDERLE GROUP

"Next Pathway is providing a solution that accelerates the migration process, giving organizations a faster route to realizing value from their data."

Eric Kavanagh

THE BLOOR GROUP

"SHIFT Cloud enables a remarkably swift and robust transition from traditional data warehousing to the cloud."

What a structured Snowflake migration covers

A successful migration to Snowflake requires a clear plan across four disciplines.

Discovery and dependency mapping

Every code object, ETL pipeline, and data dependency needs to be identified and documented before migration begins. Hidden complexity discovered mid-migration creates delays and risk.

Code translation and modernization

Legacy SQL, stored procedures, and ETL pipelines must be converted into optimized, Snowflake-native workloads. Automated translation eliminates manual rework and ensures full coverage.

Validation and functional parity

Every migrated workload must be validated against the legacy source to confirm data accuracy and functional equivalence before cutover. 100% parity is not optional.

Production cutover planning

Cutover requires a defined plan covering resource allocation, rollback decisions, and business sign-off. Teams that plan cutover from the start move faster and with greater confidence.