A Leading U.S. Construction Firm Keeps 40+ Procore Data Feeds Reliable and Monitored on Databricks, with Lumenalta

A leading U.S. construction firm relied on Procore construction-management data to run its reporting and operations, from budgets and change events to RFIs, submittals, and daily logs.

Lumenalta®is embedded in the company's data team, working alongside its engineers across the Databricks platform on Unity Catalog adoption, Auto Loader ingestion patterns, pipeline development, and query optimization. When the company moved its Procore data to a new integration feed, Lumenalta®helped rebuild the ingestion layer that the rest of its reporting depends on.

Moving to the new integration feed meant rebuilding ingestion for roughly 40 business entities while hundreds of downstream reports and processes kept running. The business could not pause while the foundation underneath it was replaced.

The feeds were not uniform. Some arrived incrementally, some as full refreshes, and some as deleted records. Inconsistencies in the source system's API meant list and detail endpoints returned different sets of fields for the same entity.

Integrations can also stop delivering data without raising any error. A failed job is easy to spot. A feed that simply goes quiet is not, and the team needed early visibility when that happened.

Lumenalta®worked alongside the company's engineers to build the replacement pipeline, then added a monitoring layer designed around how data actually fails.

Ingest. More than 60 Lakeflow Jobs take Procore data from bronze to silver.

Reconcile. Schema-evolving loads reconcile incremental, full-refresh, and delete feeds into consistent tables.

Resolve. The pipeline resolves the source API's field-level inconsistencies along the way, so downstream users see one consistent version of each entity.

Analyze. Lumenalta®independently built a gold scheduling-analytics layer.

Monitor freshness, not just failures. Lumenalta®built a daily health check covering all 61 active feeds. Rather than simply reporting failed jobs, it reports the freshness of every feed each weekday morning and flags anything late or stale.

The health check gives the business, IT, and reporting teams a shared view during source-system outages and unexpected lags, so everyone is working from the same picture of what data is current.

Kept production reporting supplied. Over the past year, the pipeline completed more than 53,000 job runs at a 99.8% success rate. It now holds over 120 million records.

Became core to daily work. The pipeline feeds nearly 500 downstream tables and views, about 240 notebooks, and more than 100 scheduled jobs. Around 20 engineers and analysts query it directly.

Caught problems before reports broke. The daily health check has run for over a year at a 98.5% success rate. It has repeatedly surfaced upstream integration problems that were raised with IT and resolved before downstream reports were affected.

Kept the business informed during outages. When source systems lag or go down, business and reporting teams hear about it from the health check instead of discovering missing data themselves.

The biggest shift was not the migration itself. It was how the company knows its data is current. Instead of waiting for a job to fail, teams start each weekday with a freshness report on every feed, so silent gaps surface early and get resolved upstream.