Challenge
Challenge
A global payroll leader in human capital management (HCM) services needed to modernize its Amazon EMR environment to align with its vision for a centralized enterprise data platform. While the system was stable, it had become complex, costly to maintain, and lacked the agility to scale efficiently.
Key challenges included:
- Performance bottlenecks: Separate data processing and warehousing pipelines caused latency. Outputs stored in Redshift with Type 2 schema and CSV formats further slowed performance.
- High maintenance effort: The multi-technology stack (Hive, Pig, Oozie, SQL, Java, Shell) involved denormalized jobs and code duplication, driving up operational complexity.
- Scaling limitations: Persistent EMR clusters relied heavily on Ops support and lacked elasticity for varying workloads.
Modernization wasn’t optional—it was a strategic directive from leadership to move workloads into a unified, future-ready Databricks environment.
Legacy Landscape (Before migration)
| Attribute | Details |
|---|---|
| Data volume | ~2 TB historical |
| Processing cadence | ~20 GB/week (daily + weekly jobs) |
| Users impacted | 20+ users across 2 business units |
| Tech stack | Hive, Pig, Oozie, SQL, Java, Shell |
| Infra setup | Persistent Amazon EMR cluster |
| Governance | Managed by ADP’s centralized governance team |
Solution
The global payroll leader partnered with Impetus to migrate EMR workloads into the Databricks Lakehouse using a mix of automation and re-engineering. The modernization approach included:
- Automated code conversion with Impetus LeapLogicTM
- Custom workload re-engineering: 9 Shell scripts and 4 Java jobs re-engineered as Databricks Notebooks
- Optimized orchestration: Legacy Oozie flows redesigned in Databricks Workflows, with ingestion filters embedded directly into transformation pipelines
- Elastic cluster execution: Shift from a single EMR cluster to Databricks job clusters, tuned for each workload
- Governance alignment: Hive Metastore integration with group-level access controls and standardized views, consistent with centralized governance policies
308 legacy jobs modernized in just 3 months with up to 40-85% automated code conversion using Impetus LeapLogic™
Outcomes
In just three months, the payroll leader modernized its EMR workloads with speed and precision:
- 308 jobs and scripts migrated, covering Hive, Pig, Oozie, Shell, and Java
- Up to 85% of code converted automatically with LeapLogic
- Legacy Oozie flows re-engineered into Databricks Workflows
- Automated validation framework ensured cell-by-cell accuracy across all workloads
Customer Quote
“Flawless execution, high quality, and early delivery by highly talented and committed team at Impetus.”
– Hemlata Rawal, Sr. Director, ADP
Business impact
The transformation has already simplified how teams access and use data. Elastic compute has reduced reliance on Ops, performance has improved with Parquet-based storage, and business units now have wider access to richer datasets.
Key benefits observed so far:
- Expanded data democratization across technical and business teams
- Faster, elastic scaling of workloads without Ops intervention
- Broader data availability and span, strengthening reporting and analytics
While adoption is still in progress, the move to Databricks has laid a strong foundation for faster reporting cycles, leaner infrastructure, and more agile decision-making across payroll and HR functions. With a modernized platform now in place, the payroll leader is well positioned to expand its transformation and gradually explore new opportunities for analytics and innovation—turning this migration into the first step of a longer modernization journey.
