# Data Engineering & Analytics

> The Cubeless Company does data engineering as a path from source systems to one decision, not a multi-year warehouse programme. We ship ingestion, a trusted model and a dashboard or export for a single question first — typically in four to eight weeks — then extend to sources that earn their place. Conflicting reports are treated as a definitions problem: each metric is named once and every dashboard reads that model.

Pipelines, warehouses and dashboards scoped to the decisions you need to make, not a multi-year data programme.

## Direct answer

**Do we need a data warehouse before we can use AI?**

The Cubeless Company does data engineering as a path from source systems to one decision, not a multi-year warehouse programme. We ship ingestion, a trusted model and a dashboard or export for a single question first — typically in four to eight weeks — then extend to sources that earn their place. Conflicting reports are treated as a definitions problem: each metric is named once and every dashboard reads that model.

## Also known as

- Data engineering
- Analytics engineering
- Data pipeline development
- Data warehouse implementation
- ETL and ELT development
- Business intelligence dashboards

## Compared with alternatives

| Alternative | Choose it when |
| --- | --- |
| A full warehouse programme | You have years, a platform team, and every source equally important. |
| A thin slice | One decision or AI feature needs reliable data now. |
| Another dashboard on raw tables | The number already exists; the team just cannot find it. |

## Facts

- **First pipeline:** Typically 4–8 weeks in production
- **Scope:** One decision, then the sources that earn a place
- **AI-ready:** Usable data where the model will look — not everywhere

## What's included

### Pipelines that hold

Ingest, transform and quality checks for the sources that matter to the next decision, not every table in the company.

### Warehouses and models

Schemas and metrics your analysts and product teams can trust week after week.

### Decision surfaces

Dashboards and exports that answer specific questions, not vanity charts.

## How we deliver it

### 1. Name the decision

What must be true, for whom, how often, then the minimum data path that serves it.

### 2. Ship a thin slice

One pipeline, one model, one surface in production before we widen the map.

### 3. Harden and extend

Monitoring, ownership and the next sources that earn their place.

## Who it's for

- Teams preparing data for AI without a warehouse rewrite
- Leaders who need trustworthy weekly numbers
- Product and ops groups drowning in conflicting reports


## FAQ

### What does a data engineering project actually deliver?

A working path from your source systems to a decision. That means ingestion and transformation for the sources that matter, a warehouse and data model your analysts can trust, quality checks that fail loudly, and a dashboard or export that answers a specific question. We ship one pipeline end to end before widening the map.

### Do we need a data warehouse before we can use AI?

Not usually. You need reliable, well-modelled data for the specific decisions or AI features you are building next. We scope the minimum data path that serves those, get it into production, then extend to further sources as they earn their place — which is far faster than a multi-year data programme.

### How do you fix reports that disagree with each other?

Conflicting reports are almost always a definitions problem, not a tooling problem. We agree what each metric means with the people who use it, implement those definitions once in a shared data model, and point every dashboard at that model, so the number is calculated in one place instead of five.

### Which data tools and warehouses do you work with?

We work with PostgreSQL, BigQuery, Snowflake and Redshift as warehouses, Python and SQL for transformation, orchestration with Airflow or Celery, and dashboards in the tool your team already opens. We choose based on your existing stack, data volume and team skills rather than a fixed preference.

### How long before we see something useful?

A first pipeline, model and dashboard in production typically takes four to eight weeks. We deliberately start with one decision rather than a full data platform, so the value arrives while the wider programme is still being planned.

Provider: [The Cubeless Company](https://www.thecubelesscompany.com/)