Data Processing & Reporting Checklist for AI & Machine Learning

Lock column names, types, required fields, and allowed values using a schema tool like Pandera or a schema registry. This prevents silent data drift and makes downstream feature engineering deterministic.

Standardize on UTF-8 without BOM, a single delimiter, and consistent quoting rules. Validate on ingest with quick sampling to catch BOMs and inconsistent quoting that can corrupt numeric parsing.

Convert all timestamps to UTC and store in ISO 8601 with explicit timezone offsets. Resolve ambiguous daylight saving times and document the conversion rules used for reporting windows.

Use high-water marks, updated_at columns, or CDC to ingest only new data via Airbyte or Fivetran. Persist bookmarks atomically so reruns do not duplicate or skip records.

Define stable primary keys and a tie-break rule such as latest last_updated then highest source priority. This ensures idempotent merges and consistent training sets across reruns.

Augment each record with source_file, source_system, ingest_timestamp, and a content checksum. This enables auditability and precise rollback when a bad file slips through.

Normalize headers to snake_case, trim whitespace, and map vendor-specific names to canonical ones. Keep a mapping table checked into version control to avoid report regressions.

Compare ingested row counts to expected ranges and sample a fixed set of records for manual spot checks. Persist sample keys to enable deterministic review across runs.

Create Great Expectations or Soda test suites for ranges, uniqueness, nullability, and distribution checks. Run them in CI and fail fast on contract violations before downstream transforms.

Coerce numeric and date types with strict parsing and document how nulls are imputed or dropped. Avoid mixed types in columns to prevent subtle bugs in aggregations and model features.

Use robust z-scores, IQR filters, or Isolation Forest to flag implausible values in key metrics. Record exclusion reasons so reports can show both raw and cleaned summaries.

Scan text fields using Presidio, AWS Comprehend, or spaCy patterns for emails, phones, SSNs, and addresses. Mask or tokenize PII prior to report generation and dashboard export.

Normalize Unicode, remove control characters, and standardize casing and punctuation. Apply fastText or langdetect to tag language so downstream NLP features and summaries use the right pipelines.

Use record linkage with Splink or fuzzy matching via rapidfuzz to merge near-duplicate entities. Persist cluster IDs and match confidence to explain merged rows in reports.

Parse and normalize addresses with libpostal, then geocode with Pelias, Nominatim, or a paid API. Store lat, lon, ISO country codes, and geohashes to power location-based reporting.

After enrichment joins, compute anti-join counts, duplicate key rates, and unexpected many-to-many expansions. Alert and block runs when integrity thresholds are breached.

Use Parquet with zstd or snappy compression, correct statistics, and partition columns aligned to query filters. Avoid small files by targeting 128 to 512 MB per file for efficient scans.

Use Polars for fast single-node transformations, DuckDB for in-process analytics, and Spark for large distributed joins. Benchmark realistic workloads and pin the chosen engine per pipeline.

Prefer overwrite plus atomic swap or MERGE into Delta or Iceberg tables. This ensures reruns produce the same outputs without duplicating or skipping rows.

Compute rolling sums, session windows, and percentiles with explicit boundaries. Align windows to business rules, for example event time vs processing time, to avoid report mismatches.

Fix numpy, pandas, and model framework seeds, and enable deterministic algorithms in PyTorch and cuDNN. Log seeds and deterministic flags in the report metadata.

Use Hypothesis to assert invariants such as nonnegative counts, monotonic date ordering, and consistent totals across splits. Run these tests on sampled data in CI.

Export feature tables with entity keys, event_time, and feature freshness SLA fields to a feature store like Feast. Align backfills to event time so training and reporting are consistent.

Store intermediate tables keyed by input checksums to avoid recomputing stable steps. This accelerates iteration and guarantees identical inputs produce identical outputs.

Use pdfplumber or Camelot for digital PDFs, and Tesseract, AWS Textract, or Google Document AI for scans. Evaluate on your corpus and record accuracy by document type.

Configure Camelot lattice vs stream modes and fallback to Table Transformer for complex layouts. Persist per-table confidence scores and flag low-confidence tables for review.

Store x, y, page number, and bounding boxes alongside extracted text. This supports clickable references in reports and speeds up human verification.

Parse currencies, thousands separators, and date formats using locale-aware libraries. Map vendor fields to a canonical schema to enable consistent joins and summaries.

Use LayoutParser or LayoutLM-based models to segment columns and reading order. Validate totals with checksums and cross-footing to catch misaligned cells.

Queue problematic pages in a simple review UI or spreadsheet pipeline and capture corrections. Feed these corrections back into rules or fine-tuning datasets.

Generate embeddings with sentence-transformers for section-level retrieval, deduplication, and similarity checks. Store vectors in a lightweight index to drive semantic cross-references in reports.

Apply rule-based or ML-based redaction on account numbers and personal identifiers before indexing or reporting. Keep a reversible tokenization mapping in a secure vault if re-identification is required for ops.

Use Papermill or Jupytext to inject parameters like date ranges, cohorts, or model versions. Record the data snapshot ID and commit hash in each output artifact.

Generate reports with Jinja2 templates and charts from Altair or Plotly, then export to PDF and HTML. Include machine readable sections like JSON blocks for downstream parsing.

Drive narrative paragraphs with prompt templates that return JSON adhering to a Pydantic or JSON Schema model. Add guardrails and unit tests with golden files to prevent regressions.

Attach Evidently AI drift reports, schema diffs, and row-level validation summaries to each deliverable. Link to the historical baseline used for comparison.

Include AUC, PR curves, calibration plots, and SHAP summaries by key slices. Call out material changes relative to the last release and attribute to data distribution or feature changes.

Send reports via Slack webhooks or email with expiring signed URLs and access scopes. Avoid embedding PII and apply watermarking to PDFs when needed.

Store outputs with content-addressed hashes in object storage, enabling immutability and deduplication. Attach metadata for data snapshot, code commit, and pipeline run ID.

Use Apache Superset, Metabase, or Hex for explorable views with row-level security. Keep metric definitions centralized in dbt metrics or a semantic layer to ensure consistency with static reports.

Use Dagster, Prefect, or Airflow with backfills, timeouts, and retry policies. Tag runs with dataset versions and propagate run context into outputs.

Run fast sample pipelines and Great Expectations or Pandera suites in GitHub Actions or GitLab CI. Block merges when contracts break or performance budgets are exceeded.

Track data snapshots and artifacts alongside code, enabling reproducible builds. Promote datasets from staging to production via pull requests and approvals.

Emit OpenLineage events and index datasets in DataHub for discoverability. Record ownership, documentation, and tags so report consumers can self-serve context.

Use AWS Secrets Manager, GCP Secret Manager, or Vault and load at runtime with short TTLs. Avoid storing tokens in notebooks or config files checked into git.

Push computation down to warehouses like BigQuery or Snowflake when possible, prune columns aggressively, and avoid data skew. Monitor query plans and warehouse logs for hotspots.

Apply row-level filters and column masking for sensitive attributes in both datasets and dashboards. Log report access and enforce retention and deletion SLAs.

Freeze Docker image digests and lock dependencies via pip-compile or conda-lock. Test restore from backups and verify that past reports can be rebuilt bit-for-bit.