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Datalloy

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Data Engineering

Datalloy builds the ingestion and transformation layer that everything else depends on. Source systems change, volumes grow and business rules move — the pipeline has to absorb that without breaking the reports downstream.
The data engineering stage covers source systems, ingestion, data lake, transformation, warehouse load.

Where this sits

  1. 01

    Source Systems

    ERP, CRM, databases, APIs, files, SaaS

  2. 02

    Ingestion

    Batch and incremental extraction

  3. 03

    Data Lake

    Raw and curated zones

  4. 04

    Transformation

    Cleansing, standardisation, business rules

  5. 05

    Warehouse Load

    Facts, dimensions and history

Scope

What data engineering covers

Ingestion, transformation and orchestration built as engineered software — version controlled, parameterised, monitored and repeatable.

  • Source connectivity

    Extract from ERP and CRM systems, relational databases, files, REST APIs and SaaS platforms — batch or incremental, with change tracking where the source supports it.

  • ETL and ELT design

    Choose the right pattern per workload: push transformation into the warehouse where it scales, keep it in the pipeline where it needs control and lineage.

  • Orchestration

    Dependency-aware scheduling, retries, alerting and run history, so a failed load is visible immediately rather than at the morning report.

  • Engineering discipline

    Source control, parameterised environments, deployment pipelines and documented logic — pipelines maintained as software, not as a set of manual jobs.

  • ETL / ELT
  • Data pipelines
  • Data ingestion
  • Data transformation
  • Data integration
  • Pipeline automation
  • Data migration

Signals

When this is usually needed

  • Reports are rebuilt manually from exports each month
  • Pipelines fail silently and are discovered by business users
  • The same transformation logic is duplicated in several places
  • Nobody can explain how a number was produced
  • Load windows keep getting longer as volumes grow

Deliverables

What you get

  • Source-to-target mapping documentation
  • Ingestion pipelines with incremental load logic
  • Transformation layer with reusable, parameterised components
  • Orchestration schedules with dependency handling
  • Logging, monitoring and failure alerting
  • Deployment process across development and production environments

Next

Related services

The stages either side of this one — most platforms need more than a single layer.

  • Data Warehousing

    Dimensional warehouses and data marts that hold history, resolve conflicting definitions and stay fast as volumes grow.

  • Business Intelligence

    Power BI semantic models, governed measures and dashboards designed around the questions the business actually asks.

Need data engineering help?

Tell us where the data currently breaks down and we will tell you what we would do about it.

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