Data42Data42

Data engineering · Analytics · AI

Answers to data questions.

In the book, the computer took seven and a half million years to reach 42. Your data should take seconds. Pick your sources and what you want out, and watch the pipeline build itself, top to bottom.

Your sources
You want

It starts with your sources

  1. 01 · Ingestion · Build · foundations

    Nothing lost. Nothing doubled.

    Change data capture from your databases, incremental pulls from your APIs, and file drops picked up from SFTP and S3. Every load incremental, idempotent, and replayable.

    +1,204 rows · 0 duplicates · rerun safe

    • Airflowschedules every load, with retries and backfills
    • Pythonthe extraction code
    • Matilliondatabase loads into Snowflake · or Airflow and Python
    • AWS Lambdapicks up a file the moment it lands

  2. 02 · Landing

    Keep the original.

    Raw data lands untouched and immutable, in open formats, with its schema. Everything below can be rebuilt from here.

    s3://lake/raw/orders/dt=2026-09-10/ · parquet · 400 d retention

    • S3the raw zone
    • Icebergopen table format, so no vendor owns your data
    • Parquetcolumnar files

  3. 03 · Integration · Build · models

    One customer, not four.

    Typed, deduplicated, and matched across systems. Tests for uniqueness, nulls, and relationships run on every load.

    'PAID ' → paid · 1299000 → 12,990.00 EUR · 4 tests ✓

    • dbtstaging and integration models, tested on every run
    • SQLthe transformations

  4. 04 · Modeling

    Revenue, defined once.

    Facts, dimensions, and a semantic layer every tool reads. Docs and lineage come from the code, so they never go stale.

    revenue = sum(amount) where status = 'paid'

    • dbtmarts and the semantic layer
    • Snowflakethe warehouse · or BigQuery, Databricks, PostgreSQL

  5. 05 · Data products · Build · products

    Where value shows up.

    Dashboards with row-level security per customer. Built by the people who built the pipeline.

    €61,430 today · +4.2% vs last Tuesday

    • Apache Supersetdashboards, embedded, with row-level security · or Power BI, Metabase, Looker

Alongside every stage

The parts nobody draws.

Step Functions · CDK · GitHub Actions · CloudWatch

Orchestration
Schedules, retries, and backfills in one place.
Tests & freshness
A stale source blocks publishing. Yesterday's numbers stay up.
Observability
Alerts that reach the person who can act.
Governance
Row-level security, PII tagging, an audit trail.
CI/CD
Every change is a reviewed pull request. Infrastructure is code.
Cost
The right compute for the job, and a bill someone reads.

42 · The answer

Ask the question. Get the answer, with its sources.

Your analysts get the answer from the semantic layer, through the dashboards. An agent that answers in plain language is one toggle away.

Q: Why is revenue up today? A: Three enterprise orders from Germany, largest #48213 · sources: fct_orders, dim_customer

  • Apache Supersetthe semantic layer, through the dashboards

Your pipeline

3 sources · 6 stops · 11 tools · 2 disciplines

Technical call → discovery of your 3 sources → build, 6 stops → handover, or we stay.

Airflow · Python · Matillion · AWS Lambda · S3 · Iceberg · Parquet · dbt · SQL · Snowflake · Apache Superset

Book a 30-minute technical call

30 minutes · no pitch deck · you speak with the engineer who would do the work

Six ways to work with us

All services
2021
Founded in Yerevan
5 yrs
Embedded in one enterprise data team
AWS
Certified Data Engineer on the team
GMT+4
Overlaps EU mornings and US afternoons

Industries · Consumer goods · Market research & brand intelligence · Pharma & medical affairs · Payments & fintech · Logistics · Influencer marketing · Startups