Data Processing & Reporting Checklist for Web Development

Lock field names, types, and nullability using JSON Schema or Pydantic models, then version and store them with your code. Reject payloads that do not match and surface diffs in CI to avoid silent breakage when upstream exports change.

Parse large files with Node csv-parse or fast-csv using columns:true, skip_empty_lines:true, and relax_column_count:false, or pandas.read_csv with chunksize. Stream to avoid OOM, apply backpressure, and validate column counts as you go.

Probe file encodings with chardet or uchardet, convert via iconv-lite, and strip BOMs to prevent garbled characters. Persist original encoding in metadata for audit and troubleshooting.

Use Luxon or date-fns-tz in Node, or pendulum in Python, to convert input timezones to UTC and store the original zone in a separate column. This ensures consistent aggregation and correct scheduling logic across regions.

Parse numbers with locale-aware rules and use Decimal.js or Python decimal for currency to avoid floating point drift. Standardize nulls and empty strings, and flag invalid values for quarantine rather than guessing.

Normalize emails to lowercase, format phones to E.164 with libphonenumber, and build deterministic fingerprints for person or company entities. Use rapidfuzz thresholds for fuzzy matches and keep a merge table with provenance.

Use Polars or pandas with vectorized operations and avoid per-row loops, or run SQL with DuckDB over Parquet for fast joins and aggregations. Profile code paths and move heavy transforms server-side where possible.

Create staged, intermediate, and mart layers, define sources, and add dbt tests for uniqueness and non-null constraints. This makes refactors safer and helps new developers understand lineage and intent.

Call services like Clearbit, ipinfo, or Mapbox with p-limit concurrency control and exponential backoff. Cache responses in Redis with TTL keyed by normalized inputs to reduce cost and latency.

Fetch daily FX rates from Open Exchange Rates or exchangerate.host, apply the rate at ingestion time, and store monetary values as integers in minor units. Keep the source currency and rate for traceability.

Track last processed timestamps, file checksums, or S3 ETags to process only new or changed data. This reduces runtime and avoids rework when daily imports or event streams grow.

Use Camelot (lattice or stream) or pdfplumber to parse tabular data, validate column counts per page, and mark low-confidence pages. Fall back to Tesseract OCR for scanned PDFs and review sample outputs during QA.

Use Postgres for transactional workloads with JSONB for semi-structured fields, ClickHouse for append-heavy analytics and fast aggregation, and DuckDB or Parquet for embedded analytical tasks. Avoid one-size-fits-all databases.

Store datasets as Parquet with Zstd or Snappy compression and partition by date or tenant in S3 or MinIO. Push down predicates with engines like DuckDB or Spark to reduce scans and costs.

Create btree indexes for equality filters and ranges, GIN indexes with jsonb_path_ops for JSONB, and pg_trgm for fuzzy text search. Use partial indexes for hot subsets to reduce write overhead.

In Postgres, use INSERT...ON CONFLICT for upserts and include deduplication keys. Generate idempotency keys from source file checksums or record hashes to make retries safe.

Create materialized views or summary tables for top KPIs and refresh them incrementally, using REFRESH MATERIALIZED VIEW CONCURRENTLY where supported. Cache results per tenant to cut query latency.

Set Node stream highWaterMark appropriately, read CSVs in pandas with chunksize, and use pyarrow dataset scanning to control memory. Profile with flamegraphs and measure I/O to eliminate bottlenecks.

Use Chart.js, ECharts, or Recharts and ship pre-aggregated data to the client rather than raw rows. Downsample large time series and apply appropriate binning for readable visuals.

Use Playwright or Puppeteer to render report pages with CSS print styles, embed fonts, and wait for networkidle. Validate page breaks and table headers repeat across pages.

Use Handlebars, Liquid, or Nunjucks with helpers for formatting numbers, percent deltas, and trend arrows. Keep narrative rules in code so changes are reviewed and tested like any other logic.

Use csv-stringify or fast-csv for CSV and ExcelJS streaming workbooks for XLSX, writing records as they are read. Quote fields, enforce UTF-8, and sanitize values to avoid CSV injection.

Use cursor-based pagination built from sort key and id, clamp page sizes, and validate query parameters with zod or Joi. Support stable sort keys to make exports reproducible.

Apply mandatory org_id filters in a shared query builder and enable Postgres Row Level Security with policies per tenant. Add tests that prove cross-tenant data cannot leak into reports.

Use BullMQ on Redis, Celery on RabbitMQ, or Sidekiq to execute background jobs with retries, priorities, and concurrency control. Persist job metadata and expose status APIs for clients.

Use GitHub Actions cron, Cloud Scheduler, or Quartz for time-based triggers and keep schedules in UTC. Maintain a change log for schedule edits to avoid silent drift during daylight savings.

Derive idempotency keys from file checksums or batch watermarks and check for existing run records before executing. Store write-ahead metadata in an outbox table to make retries safe.

Wrap enrichment calls with tenacity in Python or opossum in Node to limit failures during provider outages. Expose partial results and mark records for reprocessing rather than blocking the whole run.

Run transforms in containers with CPU and memory limits or burst via AWS Lambda or Cloud Run, choosing appropriate memory and timeout settings. Separate queues for CPU-bound and I/O-bound tasks.

Write a manifest.json per batch to object storage with schema version, row counts, checksums, and timestamps. Store data diffs and sample rows to accelerate debugging and audits.

Use Pytest or Jest for unit tests, Hypothesis or fast-check for property tests, and maintain golden CSV fixtures to lock output formats. Fail builds when canonical samples change unexpectedly.

Run Great Expectations or Soda checks for uniqueness, non-null fields, referential integrity, and row count thresholds. Block deploys on critical failures and publish validation reports to your team.

Emit spans for each step and tag them with dataset, tenant, and job_id, plus Prometheus counters for rows_processed and error_count. Build Grafana dashboards to spot regressions quickly.

Use pino in Node or structlog in Python to log JSON that includes request_id, job_id, and tenant_id. Disable pretty logs in production to keep ingestion efficient and machine friendly.

Mask sensitive fields in logs, encrypt data at rest with KMS, and limit column access via database views or RLS. Keep a data classification registry and verify reports do not expose restricted fields.

Store credentials in HashiCorp Vault, AWS Secrets Manager, or GCP Secret Manager, and inject them at runtime. Rotate API keys and database passwords on a schedule and audit access to secret stores.