The Opportunity: A Data Environment Outgrown by Growth
As the client’s business and operational complexity grew, it was outgrowing its data infrastructure. Data was increasingly distributed across multiple systems.
The company needed a more reliable operating model for its data environment, one that could reduce manual effort, improve support efficiency, and prevent pipeline issues from contributing to a growing ticket backlog.
Data Spread Across Multiple Systems: As the client’s business and operational complexity grew, data was increasingly distributed across multiple systems. The company was outgrowing its data infrastructure as it scaled.
Too Much Manual Effort: The client needed a more reliable operating model for its data environment, one that could reduce manual effort across data operations, including the manual checks used to validate data loads.
Pipeline Issues Adding to the Backlog: As operational complexity increased, the client needed to improve support efficiency and prevent pipeline issues from contributing to a growing ticket backlog.
The Approach: An End-to-End Databricks Data Platform
The client partnered with KMS to build an end-to-end Databricks data platform unifying data from Salesforce, BambooHR, and MongoDB. The platform combines automated ingestion, a medallion architecture organized with Unity Catalog, automated testing, and self-healing operations to keep pipelines resilient and operating near real time.
Unify: Automate Ingestion from Three Systems
KMS implemented automated ingestion pipelines that unify data from Salesforce, BambooHR, and MongoDB on Databricks. Change data capture (CDC) streaming and custom PySpark connectors bring data into the platform near real time, with ingestion latency of about six seconds and throughput of roughly 1,000 rows per second.
Key deliverables included:
Automated ingestion pipelines across three systems
Change data capture (CDC) streaming
Custom PySpark connectors
Salesforce, BambooHR, and MongoDB data unified
Organize: Organize Data into Secure, Business-Aligned Domains
Data from Salesforce, BambooHR, and MongoDB is processed through a medallion architecture, moving through Bronze, Silver, and Gold layers. Unity Catalog organizes the resulting assets into secure, business-aligned domains. This structure is part of the scalable and reliable data foundation the new platform gives the client.
Key deliverables included:
Bronze, Silver, and Gold medallion layers
Assets organized with Unity Catalog
Secure, business-aligned data domains
Validate: Automate Testing of Every Data Load
Automated test cases now validate every data load, replacing the manual checks the client previously relied on. This reduces manual effort, improves data quality, and builds trust in downstream information, so teams can make faster decisions using data they can rely on.
Key deliverables included:
Automated test cases for every load
Manual data checks replaced
Greater trust in downstream information
Sustain: Keep Pipelines Resilient and Near Real Time
Monitoring, alerting, and self-healing job orchestration identify and resolve failures quickly. This keeps pipelines resilient and operating near real time, supporting the client’s goals of improving support efficiency, reducing operational support effort, and preventing pipeline issues from contributing to a growing ticket backlog.
Key deliverables included:
Pipeline monitoring and alerting
Self-healing job orchestration
Faster identification and resolution of failures
The Outcome: A Scalable, Reliable Data Foundation for Faster Decisions
The end-to-end Databricks data platform gives the client a scalable, reliable data foundation and enables teams to make faster decisions using trusted information.
What Was Built
Automated Data Ingestion: Data from Salesforce, BambooHR, and MongoDB unified on Databricks through CDC-enabled pipelines
Near Real-Time Processing: Ingestion latency of approximately six seconds and throughput of roughly 1,000 rows per second
Medallion Architecture: Bronze, Silver, and Gold layers for processing data
Data Governance: Assets organized into secure, business-aligned domains with Unity Catalog
Automated Data Validation: Every data load validated by automated tests, replacing manual checks and improving trust in downstream information
Resilient Data Pipelines: Monitoring, alerting, and self-healing orchestration to identify and resolve failures quickly
What This Proves
The platform improves data quality and availability, reduces operational support effort, and enables faster decisions using trusted information.
By unifying data across multiple systems, automating validation, and introducing self-healing operations, the client now has a scalable and reliable data foundation that supports its growing business and operational complexity.