1. Upload the XER files
Start with ordered baseline and update snapshots, source hashes, and parser health.
Primavera P6 analytics for lakehouse teams
Bring Primavera schedule analytics into the Databricks lakehouse.
Keep XER as the scheduler's working language. Start with baseline and update files, complete the review, then give IT buyers and data teams versioned facts for Databricks notebooks, quality checks, pipelines, and governed portfolios.
possible now
iAppSys branded
direct URL demo
Why this matters.
Versioned schedule facts, not raw XER bodies.
Possible now
Versioned schedule facts, not raw XER bodies.
demo
Data boundary
Raw XER bodies and source paths stay out of API, MCP, admin, and visual payloads.
safe
Next conversation
Start with P6 XER review, then land curated facts in the lakehouse.
action
2. Check the baseline
Review schedule health, DCMA orientation, calendars, constraints, and path signals.
3. Check schedule and WBS adherence
Turn date, float, logic, and WBS movement into versioned quality facts.
4. Check activities
Land changed, delayed, added, removed, critical, and near-critical activity facts.
5. Check tasks and OBS
Carry task findings and available OBS-related coverage into governed notebook and pipeline models.
Choose your deployment model.
SuperWave-XER can be discussed as a product inside your data center, inside your cloud account, or as a managed hosted path. The boundary and operating responsibilities stay explicit.
On premise
Install inside your secure data center with your own network, storage, identity, and operations.
DIY
Run from Docker or source code when you want to own deployment and upgrades.
BYOC
Bring your own cloud account and retain control of tenancy, keys, network, and data placement.
FedRAMP environments
Shape a boundary-specific federal deployment with inherited controls reviewed separately from application controls.
Meet your analytics platform where it is.
Primavera P6/XER remains the scheduler input. Publish governed derived facts and evidence references to Snowflake, Databricks, Google BigQuery, Amazon Redshift, or MotherDuck when the selected architecture approves that path.
Snowflake
Warehouse trends, dashboards, and portfolio reporting.
Databricks
Lakehouse notebooks, pipelines, and governed models.
Google BigQuery
Curated schedule facts for SQL analytics and reporting.
Amazon Redshift
Warehouse-ready review summaries and portfolio views.
MotherDuck
Collaborative DuckDB analytics for lightweight sharing.