75,000+ Record Ingestion Pipelines
If your CRM chokes on your voter file, it wasn’t built for campaigns. It was built for newsletters.
Large-scale data ingestion pipelines automate the import, validation, and transformation of voter files exceeding 75,000 records, handling format inconsistencies, encoding mismatches, and schema conflicts that crash generic CRM import tools.
The 10,000-Record Ceiling
Most SaaS CRMs advertise “bulk import.” What they mean is: upload a CSV under 10,000 rows, wait 45 minutes, and pray the date columns parsed correctly. At 25,000 rows, the import times out. At 50,000, it silently drops records. At 75,000, it doesn’t even try.
These platforms were designed for small business contact lists, not statewide voter registration exports. The architecture doesn’t scale because it was never intended to. You’re trying to run a campaign on software built for a real estate agency.
What Production-Grade Ingestion Looks Like
Our pipelines are built in .NET with streaming readers that process records in chunks, not by loading the entire file into memory. This means:
- No file size limits. 75,000 records or 750,000. The pipeline doesn’t care. It processes row-by-row with constant memory usage.
- Schema normalization on ingest. County voter files ship in wildly different formats. Our pipeline maps columns dynamically, resolving “FIRST_NAME” vs “FirstName” vs “fname” before the data touches your CRM.
- Validation and rejection logging. Bad records don’t silently vanish. They’re flagged, logged, and queued for manual review. Every record is accounted for.
- Incremental updates. Re-running the pipeline on an updated voter file doesn’t duplicate your database. It diffs against existing records and applies only the changes.
The Cost of “Good Enough”
Every record your CRM drops is a voter your campaign doesn’t know exists. Every duplicate it creates is a voter who gets two mailers and thinks you’re disorganized. Every timeout is a day your field team operates on stale data.
Serious campaigns don’t import data. They engineer data pipelines. If you’re still manually uploading CSVs and spot-checking row counts, you’re running a 2016 operation in a 2026 cycle.