Why hire Jade Global as a Workday Partner?

A certified Workday Partner, like Jade Global, brings proven implementation expertise, best practices, and ongoing support to help reduce project risk, accelerate deployment, and maximize the value of your Workday investment.

Why should organizations work with a certified Workday Partner?

A certified Workday Partner has validated product expertise and access to the latest Workday capabilities. Jade Global combines certified consultants, proven delivery, and Workday consulting services to help organizations implement, optimize, and scale Workday with confidence.

What Workday services does Jade Global provide?

Jade Global offers end-to-end Workday services, including Workday implementation, Workday consulting and advisory, Workday Application Managed Services (AMS), Workday shared services, Workday integrations, optimization, and support for HR and Finance transformations.

How does Jade Global improve Workday adoption?

Jade Global improves Workday adoption through business process optimization, user training, automation, change management, and continuous Workday managed services, helping teams get more value from their Workday investment.

How does Jade Global modernize HR and Finance using Workday AI?

Jade Global helps organizations modernize HR and Finance with Workday AI services, automation, AI advisory, and intelligent workflows that improve productivity, simplify processes, and support better business decisions.

Does Jade Global support Workday AI and Workday Extend Services?

Yes. Jade Global provides Workday AI services and Workday Extend consulting to help organizations automate workflows, build custom applications, and extend Workday capabilities while maintaining a unified user experience.

Does Jade Global integrate Workday with enterprise systems?

Yes. Jade Global delivers Workday integration services that connect Workday with ERP, CRM, payroll, finance, benefits, and other enterprise applications, enabling secure data exchange and streamlined business operations.

A Leading Professional Services Firm Achieved 100% Data Fidelity with a Scalable Workday API Integration

Client Name: A leading professional services and business consulting firm
Industry: Professional services / Business consulting
Key Technologies / Platforms: Workday API, Databricks, Azure Data Factory (ADF), SQL Server, Apache Spark

About the Client

The client is a top independent professional services and business consulting firm serving private companies, nonprofit organizations, and public-sector entities. They offer a wide range of services, including assurance, tax, consulting, and business management, and operate in locations worldwide.

Business Requirements

Near-Real-Time Workday Data Integration: Make it easy to keep Workday data in sync so users and downstream applications can always access the most up-to-date information.
Reliable Data Ingestion With No Data Loss: Guarantee that every single record is captured, even those tucked away in complex lists, arrays, and nested objects.
Scalable Processing For Large Data Volumes: Handle increasing Workday data volumes effortlessly, avoiding the performance hiccups that often come with traditional ETL processing.
Accelerated Development & Minimal Maintenance: Build a reusable framework that accommodates various Workday objects, reducing repetitive development and maintenance tasks.

Business Challenges

Scalability Bottlenecks & High Latency: Traditional ETL processing often struggled to keep up with increasing volumes of Workday data and workload complexity. This led to longer execution times, resource constraints, and operational delays.
Complex & Nested Workday API Data: Workday API responses were filled with nested JSON structures, arrays, and intricate objects. Parsing these through standard ETL methods added processing complexity and increased the risk of missing crucial data during extraction.
Delayed Data Availability: Full-load batch processing took a long time, making it tough for downstream systems and business users to access the latest Workday data.
Data Refresh Disruptions: Updating target tables directly during refreshes posed risks like table locking, query timeouts, and interruptions for users who needed access to essential business data.

Our Approach and Solutions

Workday API Integration With Databricks & ADF: Earlier ADF was struggled to efficiently handle massive data volumes and complex processing, resulting in long execution times, frequent resource constraints, and operational delays. So to overcome this Apache Spark's distributed architecture was leveraged to handled heavy transformation workloads with ease. So you can remove this bullet as you have considered in the next one.
Scalable Databricks Compute: By utilizing Databricks and Apache Spark, we can handle large Workday datasets simultaneously. This approach not only boosts scalability but also overcomes the computing limitations often seen in traditional ETL processes.
Native Processing Of Complex Data Structures: Thanks to Databricks' built-in support for semi-structured data, we can efficiently parse, flatten, and transform nested JSON payloads and array objects. This capability is crucial to maintaining data integrity.
Incremental Workday Data Integration: Our incremental pipelines process only newly created or modified records, avoiding the hassle of reloading the entire dataset. This not only reduces processing needs but also improves data availability.

Impact and Business Benefits

100% Data Fidelity: Our robust approach to handling nested structures, arrays, and controlled API extraction ensured that we didn’t miss any records or lose any data along the way.
Scalable Workday Data Integration: With Databricks, we efficiently processed large, ever-expanding Workday datasets.
Near-Real-Time Data Availability: Incremental pipelines significantly increased the speed and frequency of Workday data updates.
Reduced API Timeout Risk: By using metadata-driven data chunking, we managed large-dataset extraction, improving pipeline reliability
Zero-Downtime Data Access: We implemented staging and atomic table swaps to ensure no disruptions during target data refreshes.