ADP accelerates Amazon EMR workload modernization with LeapLogic™

308 jobs re-engineered and migrated through automation on Databricks in just three months, laying the foundation for faster reporting and broader data access.

Challenge

As one of the world’s largest providers of human capital management solutions, ADP manages payroll, workforce, and compliance services for clients globally.
While its Amazon EMR environment was stable, it lacked the agility to support ADP’s vision for OneData—a centralized enterprise data platform strategy. The system had grown increasingly complex, costly to maintain, and difficult 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 ADP leadership to move workloads into a unified, future-ready Databricks environment.

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

ADP 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 ADP’s centralized governance

308 legacy jobs modernized in just 3 months with 40% automated code conversion using LeapLogic™

Outcomes

In just three months, ADP modernized its EMR workloads with speed and precision:

308 jobs and scripts migrated, covering Hive, Pig, Oozie, Shell, and Java
Up to 40% 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

❝ 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 ADP 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, ADP 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.