PI Data Pipeline Sprint
We build your PI data extraction and loading pipeline in a focused two-week sprint. You get working, production-ready code that moves PI data where you need it -- with incremental extraction, error recovery, and monitoring.
What you get
Data extraction layer
Efficient PI Web API data extraction with batch reads, pagination handling, selectedFields optimization, and proper time-window management for historical pulls. Handles digital states and quality flags.
Transform and load
Data cleaning, type conversion, timestamp normalization, and loading into your target: PostgreSQL, SQL Server, Snowflake, BigQuery, or flat files. Schema design included.
Scheduling and orchestration
Cron, Windows Task Scheduler, or Airflow DAG configuration for recurring runs. Watermark-based incremental extraction so you only pull new data each run.
Monitoring and alerting
Structured logging with rotation, error notifications (email or webhook), and a health check endpoint so you know when the pipeline runs, succeeds, or needs attention.
Common use cases
Analytics warehouse
Move PI historian data into Snowflake, BigQuery, or a SQL data warehouse for reporting and dashboards. Typical: hourly or daily refresh, 100-5000 points, 1-5 year backfill.
Machine learning pipelines
Build training datasets from PI recorded values with proper time alignment, feature engineering, and label association. Handle the gotchas: compression exceptions, quality flags, digital states.
Cross-system integration
Combine PI data with ERP, CMMS, or MES data in a unified store. Timestamp alignment and data quality flags are critical here.
Compliance and regulatory reporting
Extract and archive PI data for regulatory requirements. Audit trail, data completeness verification, and tamper-evident storage.
Real-time dashboards
Feed PI current values into Grafana, Power BI, or custom dashboards at regular intervals (1-minute to 15-minute refresh).
How the sprint works
Scope and design (Days 1-2)
We map your source points, target destination, volume estimates, refresh frequency, and backfill requirements. You get a pipeline design document to approve before we start building.
Build extraction (Days 3-5)
We build the PI Web API extraction layer with batch reads, incremental watermark tracking, and error handling for your specific point set and data volumes.
Build transform and load (Days 6-8)
Data cleaning, schema mapping, and loading into your target database or file format. Tested with real data from your PI system.
Schedule, monitor, and handoff (Days 9-10)
Set up recurring execution, logging, alerting, and documentation. Run end-to-end with your team watching. Live handoff session.
Technical details
Every pipeline sprint includes these production-grade features:
Extraction
Reliability
Supported targets
Other targets available on request. If your team uses a different database or storage platform, we can discuss during scoping.
Frequently asked questions
How many points can the pipeline handle?
Typical sprints handle 100-5,000 points. For larger point counts, we design the extraction to run in parallel with rate limiting. We have built pipelines handling 10,000+ points.
Can you backfill historical data?
Yes. Historical backfill is included in the sprint. We use time-windowed extraction with progress tracking so you can see how the backfill is progressing and resume if interrupted.
What if our target is not in your list?
We can target any system that accepts data via SQL, REST API, or file upload. Just mention it during the scoping call.
Get started
Start a pipeline sprint
Tell us about your PI environment, the data you need to move, and where it needs to go. We will scope the sprint and confirm timeline within one business day. The scoping conversation is free.
Contact PiSharpNot sure what you need?
Start with a PI Integration Audit to assess your current state, or check our PI Web API Quickstart Package if you need basic PI Web API access first.