Data Processing & Reporting Checklist for Web Development

Publish a versioned schema for every input feed so producers and consumers have a stable contract, then validate at the edge to catch drift early. Use a registry and semantic versioning to manage breaking changes and deprecations.

Use stream.pipeline with backpressure to avoid loading entire files into memory and to prevent event loop stalls. Configure strict quoting, delimiter autodetection, and handle BOMs and mixed encodings to reduce parser flakiness.

Parse with Luxon or the Intl API, convert all timestamps to UTC, and store canonical ISO 8601 strings alongside epoch values. Handle locale-specific decimal separators and ambiguous formats to prevent downstream aggregation errors.

Compute stable row hashes or use composite natural keys and implement upsert logic to avoid duplicates on retries. Persist a load manifest and checkpoint offsets so reprocessing a batch does not double count.

Define runtime schemas that mirror your TypeScript types, coercing strings to numbers and dates where safe. Route invalid rows to a dead letter queue with reason codes to support triage and replay.

Wrap axios or fetch with exponential backoff and jitter, honor 429 retry-after headers, and use a circuit breaker like opossum. Cap concurrency to respect provider rate limits and prevent cascading failures.

Use pdf-parse or pdf.js for text, Camelot or Tabula for table extraction, and fall back to AWS Textract or Tesseract OCR for scanned documents. Record confidence scores and route low-confidence pages to a review queue.

Accept uploads to S3 or GCS with short-lived signed URLs, scan for malware, and trigger processing via bucket notifications or webhooks. Validate content type and size limits at the edge to protect your pipeline.

Encode each transform as a node with explicit dependencies and materialization strategy, then track lineage from raw to semantic layers. Use incremental models to keep run times predictable on large datasets.

Perform left joins in Postgres to keep unmatched rows, and cache hot lookup tables in Redis with versioned keys. Log join hit rates to catch taxonomy mismatches early.

Write small, composable mappers with exhaustive switch checks and io-ts or Zod guards at boundaries. Unit test edge cases like empty strings, nulls, and out-of-range values to avoid silent data corruption.

Apply algorithms like Jaro-Winkler or libraries such as fuse.js to match names and addresses, then capture confidence scores and keep audit trails. Gate merges behind thresholds to prevent false positives.

Queue geocoding or company lookup calls, enforce provider limits, and cache responses with a TTL to cut costs. Persist provider metadata and version to maintain provenance and reproducibility.

Remove unneeded fields early, hash identifiers with a salt using SHA-256, and encrypt sensitive columns with application or KMS managed keys. Keep a data classification map so you do not leak PII into logs or analytics.

Format phone numbers with libphonenumber-js, validate emails with validator.js, and implement UDFs for canonical casing and whitespace trimming. Store canonical and raw values to support reversible transforms.

Use Postgres with native partitioning or Timescale for transactional and time series needs, or BigQuery or Snowflake for analytics at scale. Align partitions with query patterns and retention to keep scans efficient.

Create fact tables with surrogate keys and narrow, conformed dimensions to simplify BI queries. Add a date dimension for rollups and seasonality analysis without complex date math in every query.

Preaggregate heavy queries into materialized views and refresh during low traffic windows. In Postgres use CONCURRENTLY where possible and index the view to accelerate filters and sorts.

Add covering indexes for common filters and order by clauses, then verify with EXPLAIN ANALYZE. Eliminate sequential scans and fix implicit casts that prevent index usage.

Maintain validity ranges and current flags on dimensions to support point-in-time reporting. Prefer soft deletes to keep facts joinable and make rebuilds deterministic.

Persist raw immutable data in bronze, cleaned conforming records in silver, and curated semantic models in gold. Use separate schemas or buckets to avoid accidental cross contamination.

Use Postgres Row Level Security for multi tenant isolation, enable TLS everywhere, and restrict IAM roles to the minimum required. Rotate KMS keys and audit grants regularly.

Set @page size, margins, and embedded web fonts to ensure consistent layout across environments. Preload assets and wait for network idle to eliminate incomplete charts and images.

Use Vega-Lite, ECharts, or Chart.js for visuals, and precompute aggregates in the warehouse to reduce client side work. Server side render chart shells to improve time to first paint and shareability.

Generate plain language captions using Handlebars or Nunjucks fed by computed indicators and thresholds. Keep it deterministic and rule based for auditability and avoid variable outputs on identical inputs.

Key by a stable hash of SQL and parameters plus user context, and invalidate on table or partition refresh using pub sub channels. Set short, adaptive TTLs during peak hours to keep data fresh.

Implement RBAC or ABAC checks on every request and sign expiring download links with HMAC. Verify signatures server side and scope data access by tenant to prevent cross account leakage.

Use SES or SendGrid for email and attach or link to S3 objects with short lived URLs. Retries and bounce handling should be logged to a delivery table for audit and replays.

Record render times, query latency, and output sizes to structured logs and metrics. Fail fast or degrade gracefully when budgets are exceeded to avoid cascading slowdowns.

Build deterministic fixtures and snapshot compare aggregates, CSV exports, and rendered HTML or PDF outputs. Catch subtle changes in formatting or rounding before they hit stakeholders.

Use fast-check to fuzz delimiters, quotes, encodings, and malformed rows, asserting that your parser never crashes and failure modes route to the DLQ. This hardens the edge cases you will see in the wild.

Use Pact or Schemathesis to lock down expected request and response shapes and error codes. Mock rate limiting and transient failures to ensure client logic degrades gracefully.

Run jobs with GitHub Actions, Temporal, or Dagster, store schedules in UTC, and test daylight saving transitions. Use BullMQ or SQS for backpressure and retries on individual tasks.

Use idempotency keys, upserts with unique constraints, and outbox or inbox tables to avoid duplicate effects. Implement saga compensations for multi step workflows so partial failures can be rolled back safely.

Attach correlation IDs from ingestion through transforms to report rendering and export. Export traces and metrics to Grafana Tempo or Jaeger and logs to a centralized sink for cross service debugging.

Use dbt tests or great_expectations to enforce constraints like uniqueness and ranges, and fire Slack or PagerDuty alerts on failures. Quarantine suspect batches automatically to protect downstream consumers.