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Nexus Accelerator ยท Data Engineering

Data ready before the morning meeting

We rebuild the pipelines behind your reporting on a metadata-driven framework, on Fabric, Databricks, Snowflake, AWS or Google Cloud. Loads run on schedule, every run is logged, failures reach a named owner, and adding a new source becomes a configuration change instead of a project.

Request a scoping callSee what's included
Who it's for

A strong fit if you want

  • Morning loads that finish on time, with no one checking.
  • Failures caught before anyone sees a wrong number.
  • One framework and one log for every source.
  • New sources added by configuration, not a new build.
What you get

Running in production

Included

  • Up to 5 sources rebuilt on a metadata-driven framework (control tables, Bronze, Silver and Gold layers)
  • A run log for every pipeline and every table: rows in, rows out, duration, status
  • Alerts to named owners in Teams, Slack or email when a load fails or data goes stale
  • Restart from the failed table instead of rerunning everything
  • A runbook and a handover session for the people who will operate it

What success looks like

  1. Every source in scope loads on schedule.
  2. A failed load alerts its owner within minutes.
  3. Your team adds new sources by configuration.
Week by week

Four weeks, start to finish

Every week ends with a short review of progress.

  1. Step 1: Week 1: Sources and owners

    Confirm the sources in scope, their schedules and who owns each one. Set up the control tables and the target workspace.

  2. Step 2: Week 2: Framework and first loads

    Deploy the generic pipelines and notebooks. The first two sources load end to end with logging.

  3. Step 3: Week 3: Remaining sources and alerts

    Bring the remaining sources onto the framework. Switch on owner alerts and stale-data checks.

  4. Step 4: Week 4: Handover

    Add a new source together, confirm alerts reach each owner, and hand over the runbook.

Start smaller

A first phase

Two sources on the same framework, with logging and alerts. It becomes the first stage of the full build.

The same approach on a narrower scope, ready to grow into the full accelerator.

Part of our Data Engineering service
Examples

What this looks like

Example scenarios from the industries we know best.

Utilities

Meter reads, work orders and asset data land before the 8 a.m. operations meeting, and a missed meter feed alerts the data owner instead of the control room.

Retail

Point-of-sale and inventory data from every store load nightly, and a store that fails to send its file is flagged by name the same morning.

Other accelerators

Each accelerator makes the next one easier

All accelerators

Governed Data

Core datasets are findable, owned, protected and trusted.

Part of

Data Governance

Trusted Semantic Models

The same question gets the same answer in every report.

Part of

Reporting & Analytics

One AI Agent in Production

An agent answers from your data, cites its sources and refuses when it lacks evidence.

Part of

AI-Powered Development

Talk to us about Reliable Pipelines

Tell us which sources feed your reporting and how you find out today when a load fails.

Request a scoping callinfo@nexusbi.com