Stop nuking production with a bad CSV.
Custom import tool with validation, diff preview, rollback, and scheduled exports. Built for your schema, your rules, your team.
Who this is for
Ops lead doing monthly CSV wrangling where a bad file has already corrupted production data once and the validation layer is still a hope and a prayer.
The pain today
- Bad CSVs silently corrupting production data
- No diff preview before commit — imports feel like a live coin flip
- Validation rules hardcoded in scripts nobody maintains
- Exports stuck in CSV when integrations need JSON or webhooks
- Scheduled exports emailed to an address on someone's old laptop
The outcome you get
- Web-based import tool with drag-and-drop CSV upload
- Validation rules applied per column with per-row error reporting
- Diff preview showing adds, updates, and deletes before commit
- Rollback capability — undo the last import in one click
- Scheduled exports to S3, SFTP, webhook, or email
Import tool UX patterns that prevent data disasters
Most import disasters share a shape: the operator uploads, the system commits, something in the file was wrong, production data is now wrong, nobody noticed until Tuesday. The fix is a 4-step UX pattern. Upload: parse and validate before doing anything. Preview: show a diff — new rows, updated rows, deleted rows, row-level errors. Confirm: operator explicitly commits after reviewing the preview. Rollback: if anything looks wrong post-commit, one click restores previous state. This pattern isn't fancy — it's basic workflow UX that most internal tools skip. When your data matters, the extra 30 seconds of preview saves hours of recovery.
Validation and diff preview
Validation runs in three layers. Syntactic: does the CSV parse, do column counts match, are required fields present. Semantic: do values match expected types, do IDs reference existing records, do enum values match allowed lists. Business rules: do operator-defined rules hold (e.g., 'price must be > 0', 'customer status must be active'). Errors report per-row with the specific column and reason. Diff preview then shows what would change: 15 rows added, 23 updated (with before/after diffs visible), 2 deleted. The operator sees the consequences before commit. No surprises, no post-commit firefighting.
Async processing and queue architecture
Large imports (100k+ rows) need to run async, not block the operator's browser. Pattern: upload triggers a background job via Redis or SQS queue, operator gets a progress bar, system processes in chunks, completion notification pushes via SSE or email. Resumable imports (if the job dies mid-way, restart from last checkpoint) are important at scale. For exports, the same async pattern — click export, job enqueues, completed file lands in S3 or email. This architecture handles 100k rows as comfortably as 100 rows, without changing the operator UX.
Case study: Imohub 120k+ records
Imohub's core job is ingesting property data from 20+ sources (scraped feeds, partner APIs, manual uploads) with duplicate detection and normalization across different schemas. 120k+ property records managed through ingestion + admin tooling. Search and query response under 0.5 seconds. Infrastructure cost 70% lower than the legacy stack. The import pipeline patterns — validation layers, diff preview for manual operator uploads, async processing for bulk feeds, rollback for recovery — are identical to the patterns I use on smaller-scale import tool builds. Volume changes; discipline doesn't.
Pricing
Data import/export tools fit the Applications Standard tier at $3,499/mo. Complex ETL (multi-source, transformation logic, scheduled jobs) moves to Pro at $4,500/mo. First-version timeline: 3–4 weeks. Subscription continues as new data sources or validation rules are added. 14-day money-back, cancel anytime, Work Made for Hire. For one-off migrations (move data from system A to system B, done), a fixed-price project under Websites pricing may be more appropriate — ask and I'll scope accordingly.
Integration with downstream systems
Import tools don't live alone. Once validated data commits, it has to reach downstream systems — your database, CRM, search index, analytics warehouse. I wire downstream writes as part of the import transaction (all succeed or all roll back) so partial imports don't leave downstream in an inconsistent state. For async downstream writes (e.g., indexing to Elasticsearch or Algolia), I use outbox patterns — write to outbox table in same transaction, separate worker drains outbox into downstream systems with retries. Boring but robust.
Recent proof
A comparable engagement, delivered and documented.
Rebuilt a real estate portal at a fraction of the cost
Rebuilt Imóveis SC's real estate portal as ImoHub — a faster, more scalable successor — handling 120k+ properties with sub-second search and drastically reduced AWS costs.
Frequently asked questions
The questions prospects ask before they book.
- What CSV formats can you handle?
- Standard CSV, TSV, Excel (.xlsx), and JSON. Custom formats (fixed-width, EDI, XML) supported with a short additional spec phase. Large files are streamed, so memory usage stays flat regardless of file size — import tools shouldn't choke on a 1GB CSV.
- Can I build validation rules without coding?
- Yes — common rules (required, unique, type, min/max, regex, referential) configure from a UI with no code. Complex custom rules (multi-field validation, external API checks) need a small plugin, which I can write or your engineering team can maintain.
- How does rollback work?
- Each import creates a snapshot of the affected data before commit. Rollback restores from the snapshot. Window is configurable — typically 7 days of rollback history, retained as S3-backed snapshots. After 7 days, rollback requires a deeper restoration from full backups (slower, operator request).
- Can exports run on a schedule?
- Yes. Daily, weekly, monthly, or cron-expression schedules. Exports land in S3, SFTP, webhook (POST to URL), or email attachment. Failed exports retry with exponential backoff and alert on permanent failure. You can also trigger exports on-demand from the UI or via API.
- What if my data is PII-sensitive?
- Exports can be encrypted (GPG, AES) before landing in S3 or SFTP. Imports can pre-mask PII fields in preview views (operator sees last 4 of SSN, full value committed to database). Audit logging captures every import and export event for compliance review. Security review questionnaire answers included.
Ready to start?
Tell me what you need in 60 seconds. Tailored proposal in your inbox within 6 hours.