Input & Capture includes eight components: event triggers for real-time reactions, time-based triggers for scheduled tasks, condition-based triggers for threshold monitoring, listeners for change detection, ingestion patterns for structured data entry, OCR for document parsing, email parsing for message extraction, and web scraping for website data. The right choice depends on your data source and timing requirements. Most systems use multiple capture methods together. Start with event triggers for real-time needs or ingestion patterns for user-submitted data.
A customer submits a form. You find out three hours later when you check your inbox.
Someone emails an invoice. You squint at the PDF, type the numbers into your system, and wonder why machines cannot do this.
Your team manually copies data between apps every morning. When they are out, nothing syncs.
Every automation starts with data entering your system. Control the entry, control the quality.
Part of Layer 1: Data Infrastructure - The gateway to everything that follows.
Input & Capture is about getting data into your systems cleanly and quickly. Triggers start workflows when events happen. Listeners watch for changes. Parsing methods extract structure from emails, documents, and websites. The wrong capture method means missed events, manual work, and dirty data. The right choice means automation that actually works.
Most systems need 3-4 capture methods. Event triggers for real-time reactions. Time triggers for scheduled jobs. Ingestion patterns for user input. Parsing for unstructured sources. The question is not "which one?" but "which ones, and for what?"
Each capture method optimizes for different data sources and timing requirements. Choosing wrong means fighting your inputs.
Event Triggers | Time Triggers | Condition Triggers | Listeners | Ingestion | OCR/Parsing | Email Parsing | Web Scraping | |
|---|---|---|---|---|---|---|---|---|
| Data Source | Users submitting structured data | |||||||
| Timing | On submit | |||||||
| Data Format | Structured fields | |||||||
| Reliability | High (controlled format) |
The right choice depends on where your data comes from and how fast you need to react. Answer these questions to find your starting point.
“I need to react instantly when a customer takes an action”
Event triggers fire within milliseconds when webhooks or events arrive.
“I need to run a report or sync at the same time every day”
Time triggers run on schedules, reliably, whether anyone remembers or not.
“I need to act when data crosses a threshold (low inventory, SLA breach)”
Condition triggers watch your data and fire when criteria are met.
“I need to detect changes in a system that does not push events”
Listeners poll systems and detect changes by comparing current to previous state.
“I need users to submit structured data through forms or APIs”
Ingestion patterns handle forms, file uploads, and bulk imports with validation.
“I receive scanned invoices, contracts, or documents as PDFs or images”
OCR extracts text and structure from visual documents.
“Customer requests arrive via email and need to be processed”
Email parsing extracts sender, intent, and reference numbers from messages.
“I need data from websites that do not offer an API”
Web scraping extracts structured data from HTML pages.
Answer a few questions to get a recommendation.
Data capture is not about the technology. It is about matching how data enters to how quickly you need to react and how clean it needs to be.
Data exists somewhere outside your system
Choose capture that matches source and timing
Clean data flows in without manual work
A customer fills out a form. You discover it three hours later...
That is event blindness. An event trigger would notify you instantly, letting you respond while the customer still remembers their question.
Every morning someone manually runs the inventory sync. When they are sick, it does not happen...
That is human dependency. A time-based trigger would run reliably at 6 AM every day, whether anyone remembers or not.
Invoices arrive as PDFs. Someone squints at numbers and types them into your accounting system...
That is manual transcription. OCR would extract vendor, amount, and line items automatically, with validation to catch errors.
Customer requests arrive via email. Your team copies order numbers into the CRM manually...
That is copy-paste workflow. Email parsing would extract order numbers, classify intent, and route to the right queue automatically.
Where is data entering your systems manually right now?
These mistakes seem small at first. They compound into lost data, angry customers, and broken workflows.
Move fast. Structure data “good enough.” Scale up. Data becomes messy. Painful migration later. The fix is simple: think about access patterns upfront. It takes an hour now. It saves weeks later.
Input & Capture is the category of components that handle how data enters your systems. It includes eight types: three trigger types (event, time, condition) for starting workflows, listeners for monitoring changes, ingestion patterns for structured data entry, and three parsing methods (OCR, email, web scraping) for extracting data from unstructured sources. Choosing the right capture method determines how quickly you can react to events and how clean your data will be.
Event triggers react instantly when something happens because the source system pushes notifications to you (via webhooks or events). Polling checks for changes on a schedule by asking the source system repeatedly. Event triggers are faster and more efficient but require the source to support push notifications. Polling works with any system but introduces delay and wastes API calls when nothing has changed. Use event triggers when available, polling as a fallback.
Use event-based triggers when you need instant reactions to external actions (form submissions, payments, file uploads). Use time-based triggers for scheduled tasks that run at specific times (daily reports, nightly syncs, weekly cleanup). Use condition-based triggers when you need to react to data crossing thresholds (inventory below 50, payment failed 3 times, SLA breached). Most systems combine all three for different workflows.
Use OCR when your data arrives as scanned documents, images, or PDFs that need text extraction. Use email parsing when requests arrive via email and you need to extract sender, intent, and reference numbers. Use web scraping when the data you need is on public websites without an API. Each handles a different input format. Many systems use all three for different data sources.
The biggest mistakes are: processing events synchronously (causes timeouts and duplicates), ignoring failed events (lost data), polling too aggressively (rate limits and blocking), skipping validation at the boundary (garbage in, garbage out), assuming stable formats (breaks when sources change), and not handling partial failures in bulk imports. Always validate at the entry point and build in error handling from the start.
Yes, most real systems use multiple capture methods. A typical setup might use event triggers for real-time customer actions, time-based triggers for nightly data syncs, ingestion patterns for form submissions, and email parsing for customer support requests. The key is matching each data source to the capture method that handles it best. Some workflows even combine methods for reliability (webhooks with polling backup).
Input & Capture is the first step in your data pipeline. Once data enters through triggers, listeners, or parsing, it flows to transformation components: Data Mapping converts formats, Validation checks quality, Normalization standardizes values, and Enrichment adds context. The capture layer controls what comes in; the transformation layer controls how it gets cleaned and structured for use.
Use listeners when the source system cannot push events to you. Listeners continuously monitor external systems (file folders, databases, APIs) and detect changes by comparing current state to previous state. They work with any system that can be queried. Triggers are preferred when available because they react instantly, but listeners are essential for legacy systems or sources without webhook support.
Ingestion patterns handle structured input from known formats: forms give you typed fields, APIs give you JSON, bulk imports give you spreadsheets. Parsing methods handle unstructured input that needs interpretation: OCR reads images, email parsing extracts intent from prose, web scraping navigates HTML. Ingestion patterns are predictable and reliable. Parsing methods are flexible but require more error handling and validation.
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