Data Pipelines & Analytics
ETL, warehouse, BI, and attribution — one source of truth for your business.
· Reviewed by senior engineers
01 What it is
What this service is
Data pipelines are the plumbing that moves data from operational systems (your app, CRM, payment provider, ad accounts) into a warehouse where analytics can be done at scale. The warehouse is Snowflake, BigQuery, Redshift, or Databricks. The BI layer is Looker, Metabase, Mode, or Hex.
Devinsta builds the full stack: extraction (Fivetran, Airbyte, or custom), transformation (dbt), warehouse, BI, and the reverse-ETL layer that pushes insights back into operational tools (HubSpot, Customer.io).
02 What it's for
What it's for
You need this when leadership is making decisions from spreadsheets that nobody trusts, when product analytics is fragmented across PostHog, Amplitude, and GA, or when finance and ops can't agree on a single number for revenue or LTV.
03 How to use it
How to engage devinsta
Discovery maps the data landscape and the questions the business needs to answer. We then ship in phases — extraction first, then transformation, then BI, then reverse-ETL — with usable outputs at each phase.
04 How to deploy
How we deploy it
Pipelines deploy as code (dbt projects, Airbyte connections, custom Airflow / Dagster). The warehouse and BI run as managed SaaS. Cost is monitored per pipeline; SLAs are documented; data quality tests run on every dbt build.
GDPR / CCPA / LGPD compliance is baked into the schema design — PII is segregated, retention is automated, and deletion requests propagate across the stack.
05 What we provide
What you get from us
- Data architecture and warehouse setup
- Extraction via Fivetran / Airbyte / custom
- Transformation in dbt with tests and lineage
- BI dashboards in Looker / Metabase / Hex
- Reverse-ETL to operational tools
- Attribution and LTV modelling
- Data governance and PII handling
- Cost monitoring and optimisation
FAQ
Common questions
Snowflake, BigQuery, or Redshift?
Snowflake is the safe default for most teams. BigQuery wins if you're heavy on Google Ads / GA4 and want zero-copy integration. Redshift is mostly chosen when AWS-everything is policy. We pick during architecture review.
Do we need a data team?
Not necessarily. We can run the stack as a managed service initially, then hand over to an internal hire. For smaller teams, an analytics engineer (one role) is usually enough.
Can you replace our PostHog / Amplitude?
Often yes — a properly instrumented warehouse + BI replaces most of what product analytics tools do, at a fraction of the cost. We keep specialist tools where their UX is genuinely better (session replay, funnel exploration).