Someone emails you a spreadsheet. You manually copy-paste it into your CRM. Two hours later, someone else emails you an updated version.
A customer fills out a form on your website. You export the CSV, clean up the formatting, and upload it somewhere else.
Your sales team takes notes in one app, your support team in another. Every Monday someone spends half the day reconciling them.
Data should flow in once and go where it needs to go automatically.
GATEWAY COMPONENT - Every automation starts with data coming in. This is how you control that flow.
Ingestion is how data gets into your systems. A form submission. A file upload. An API call from another system. A bulk import from a spreadsheet. Each is an ingestion pattern - a way to capture data at the boundary of your system.
The pattern you choose determines everything that follows. Forms give you structured data immediately. File uploads need parsing. Webhooks need validation. Bulk imports need conflict resolution. Pick the right pattern for the right source.
Most data problems aren't storage problems or processing problems - they're ingestion problems. Garbage in, garbage out. Control the entry point, control the quality.
Ingestion patterns solve a universal problem: how do you get data from the messy outside world into the structured inside of your systems without losing or corrupting anything?
Receive → Validate → Transform → Store. Every ingestion pattern follows this sequence. The variation is in how each step is implemented: real-time vs batch, structured vs unstructured, user-initiated vs system-initiated.
Choose a validation level and import 8 leads. See what makes it through. and what should have been caught.
Structured data from people typing things
The simplest pattern. User fills out fields, you validate on submit, data lands in your database. Works great when you control the input format and can guide users with dropdowns, date pickers, and validation hints.
Spreadsheets, CSVs, PDFs, images
User uploads a file, you parse it server-side. CSVs need column mapping. PDFs need OCR or extraction. Images might need AI processing. You're at the mercy of however they formatted their data.
System-to-system data exchange
Another system pushes data to you via webhook or you pull from their API. The data arrives as JSON with (hopefully) a documented schema. You validate, transform to your internal format, and store.
Moving large datasets in batches
Upload thousands of records at once from a data export, migration, or scheduled sync. Each row needs validation, duplicate checking, and error handling. Failed rows shouldn't stop the whole import.
Marketing returns with a messy spreadsheet of business cards. Half have typos, some are duplicates of existing contacts, and the format doesn't match your CRM. This flow ingests them cleanly: validated, deduplicated, and enriched. ready for sales outreach.
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You accept whatever data comes in because validation 'slows things down.' Now you have phone numbers in email fields, dates in three formats, and someone entered their life story in the 'company name' field. Good luck cleaning that up.
Instead: Validate at the boundary. Reject bad data early. It's cheaper to prevent than to fix.
You built one ingestion pipeline and force everything through it. API data that's already structured goes through the same parsing as messy CSV uploads. Now you're either over-processing clean data or under-processing messy data.
Instead: Match the ingestion pattern to the source. APIs get different treatment than file uploads.
Your bulk import processes 1,000 rows. 50 fail validation. You log the errors somewhere and move on. A month later someone asks why 50 customers are missing. The log file is gone.
Instead: Track every record. Show users exactly what failed and why. Make it easy to fix and retry.
You've learned how data enters your systems. The natural next step is understanding what happens immediately after - how raw input gets cleaned and validated before it can be used.