Solutions

From raw sources to certified numbers, with the receipts.

A medallion warehouse is the shape most customers build. ELTMaestro gives each layer its own steps, its own quality gate and its own audit record.

Medallion

Bronze, Silver, Gold as pipeline steps

Bronze

Land as received

Whole-schema loaders and parallel extractors land source tables immutably, with watermarks per table and a row-count assertion between extract and load.

Silver

Clean, conform, validate

Joins, unions and expressions shape conformed dimensions and facts; SCD Type 2 keeps history; rows that fail a rule go to a quarantine table, flagged, never dropped. A control-test gate decides whether Gold runs.

Gold

Serve certified metrics

Marts and KPI tables defined once and consumed identically by dashboards, self-service and the AI sidecar. Identity resolution happens here, so PII never needs to reach a consumer.

MASTER_DAILY_BATCH
  Bronze loads (4 parallel)  ─▶ [sync] ─▶ 6 dimensions (parallel) ─▶ [sync] ─▶ FX lookups
  ─▶ [sync] ─▶ fact_revenue ─▶ [control-test gate] ─▶ 4 Gold marts (parallel) ─▶ alert hook
  full rebuild from empty: ~25 s on the reference dataset · every run reconciles fact = sum of branches

Served

What the business sees at the end of the pipeline

Dashboards over the gold layer of the reference banking build, synthetic data. Every number ties back to a control test. Click to enlarge.

Executive KPIs · headline tiles, trends, channel and segment mix, branch table

Data quality · control-test results, the orphan rows that were kept, and SCD2 change history

Customer 360 · dormancy, KYC status, value tiers, balances by province

Loan 360 · book, outstanding, risk bands, purpose and the loans needing attention

Use cases

Where customers put it to work

Warehouse migration

Move pipelines from one engine to another by repointing connections. Same jobs ran on ClickHouse and Redshift in our own test harness.

Replacing a legacy ETL licence

Customers report replacing six-figure annual licences while matching extraction rates on multi-terabyte Oracle sources.

Group-wide reporting

A group template deployed per business unit with reuse, reconciled totals across units, and one customer identity across companies.

Regulated data at volume

Legally mandated call-detail-record processing at 7 TB a day inside the nightly window, with edge engines next to the sources.

ML in the pipeline

Feature engineering and model scoring as pipeline steps: dosage recommendation, fraud detection, loan-risk classification, churn.

Governed self-service and AI

Certified datasets plus a natural-language layer that respects the same classification and row policies as every other consumer.

See reported results →

Delivery

How an engagement runs

Week 1

Reference pipeline

One of your sources landed, conformed and served, with control tests and a dashboard.

Weeks 2 to 6

First subject area

Your team builds alongside ours in the designer. Git-based promotion from dev to production from day one.

Weeks 6 to 12

Scale out

Remaining sources, SCD history, scheduling, alerting into your paging tool, the AI sidecar if wanted.

Ongoing

Support

Patches as drop-in jars with a tested rollback. Fixes default to the old behaviour unless you opt in.