Flatfile and OneSchema solve inbound CSV import for your app users. datathere solves enterprise integration across systems and partners. Different buyer, different problem.
iPaaS platforms connect SaaS apps through pre-built connectors. datathere maps messy partner data into clean structure. When you need both, here is how to think about it.
Most integration projects start with 'we will build it ourselves.' Here is what that decision actually costs in engineering time, maintenance, and opportunity.
Sending uncertified mappings to production is like deploying code without review. Certification validates configuration, locks it for production, and creates an audit trail.
When a record fails validation during a pipeline run, what happens next matters. Quarantine it, flag it, or stop the job. Each action serves a different purpose.
Most integration tools handle one source at a time. Real-world pipelines need to join customer records from a CSV with transaction data from an API and reference tables from a database.
Instead of writing code to transform data, describe what you want in plain English. The AI writes the expression, validates it, and shows you the result before it runs.
AI data mapping uses machine learning to analyze field names, data types, and sample values to generate mappings between source and destination schemas automatically.
Safety Data Sheets arrive as PDFs from hundreds of suppliers in different layouts. Extracting GHS classifications, storage requirements, and regulatory fields by hand does not scale.
Stock status depends on data from multiple fulfillment partners, each reporting in different formats, at different intervals, with different field definitions.
Every channel demands different attribute structures, naming conventions, and compliance fields. Organizations rebuild the same product data for every marketplace, retailer, and distributor.
Small vendors send spreadsheets. Large suppliers send API feeds. Some send PDFs. Getting all of them into a unified product catalog is the bottleneck most retailers never solve.
Transaction monitoring alerts, sanctions screening results, and network-level intelligence from JPMorgan Access, SWIFT GPI, and FinCEN arrive in incompatible formats. Building a unified AML view requires mapping all of them.
Identity verification, fraud detection, and regulatory compliance depend on external providers who each deliver data in proprietary formats with different scoring models.
Banks, custodians, and counterparties each report positions and transactions in different message formats. Reconciling across SWIFT MT, ISO 20022 CAMT, and proprietary ledger exports is where operations teams lose days.
Merchant applications arrive from multiple platforms with different field structures for business details, ownership records, and compliance documentation.
Authorization codes use different labels across systems. Settlement structures follow different sequences. Every processor update forces custom rework.
New customers spend weeks waiting for integration setup. Published mapping templates and adapters let them self-serve initial connections and shorten time to value.
Prospects evaluate software by connecting their own data. When that connection requires custom development, trials stall and deals die in technical setup.
Quotes, delivery terms, and performance data arrive from dozens of suppliers in different formats. Comparing vendors requires normalizing all of it into a common structure first.
Technical specifications arrive as PDFs and spreadsheets with inconsistent layouts, embedded tables, and non-standard field names. Extracting structured data from them is manual and error-prone.
Every supplier describes the same part differently. Part numbers, material descriptions, unit measures, and tolerance specifications arrive in incompatible formats.
Modeling cost and service trade-offs requires order data from ERP, inventory from WMS, and transit data from TMS, three systems that never speak the same language.
Conveyor sensors, barcode scanners, and sorting systems each generate their own error formats. Finding the root cause of a fulfillment issue means correlating data across all of them.
Each carrier reports tracking events differently: different status codes, different timestamp formats, different milestone definitions. Building a unified view requires normalizing all of them.