ETL Pipeline Development
Extract, transform and load flows for structured, analysis-ready data.
ETL and ELT Services
ETL and ELT Services
Trusted across 20+ countries by Fortune 500 companies and growth-stage brands
We design extract, transform and load flows that turn raw source data into clean, modelled, analysis-ready data your warehouse, BI and AI can trust. Over a decade of experience, 250+ digital solutions delivered.
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What are ETL and ELT services?
ETL and ELT are the patterns for turning raw source data into clean, modelled, analysis-ready data. ETL transforms data before loading it into the target; ELT loads raw data first and transforms it inside the cloud warehouse. Noseberry builds tested, version-controlled transformation flows, usually with dbt, so your analytics and AI run on trustworthy data.
Key takeaways
- ETL and ELT turn raw source data into clean, modelled, analysis-ready data.
- ETL transforms before loading; ELT loads first and transforms in the warehouse.
- Modern cloud warehouses make ELT with dbt the common default.
- Good transformation and testing are what make analytics and AI trustworthy.
2M+Lives touched
15+Fortune 500 clients
20+Countries served
250+Digital solutions delivered
What we do
Our ETL and ELT services
ELT on Cloud Warehouses
Load raw data first, then transform in Snowflake, BigQuery or Databricks.
Data Transformation and Modelling
dbt-based transformation and modelling for consistent, reusable datasets.
Data Cleansing and Validation
Cleaning, deduplication and tests so bad data never reaches reports.
Source Integration
Connecting databases, apps, APIs and files into one transformation layer.
ETL to ELT Modernisation
Migrating legacy ETL tools to modern, warehouse-native ELT.
Where it delivers value
Where ETL and ELT deliver value
Clean, consistent, analysis-ready data
Trusted metrics feeding BI and dashboards
Reliable inputs for AI and machine learning
Retiring brittle, manual or legacy ETL tools
Faster, testable, version-controlled transformations
How we work
Our five-phase process
We choose ETL or ELT per case, build tested transformations, and validate against your real data before scaling.
1
Discovery and Audit
We map your sources, targets and transformation logic.
2
Strategy and Roadmap
We choose ETL or ELT per case and plan the models.
3
Rapid Proof of Concept
We build a transformation flow on your real data.
4
Build and Integrate
We build tested, modelled pipelines into your warehouse.
5
Deploy and Optimize
We monitor, test and tune as data and needs evolve.
Technology we use
Transformation
- dbt
- SQL
- Python
Orchestration
- Apache Airflow
- Cloud-native schedulers
Warehouses
- Snowflake
- BigQuery
- Databricks
- Redshift
Integration
- Kafka
- CDC tooling
- SaaS connectors
Security and compliance
Secure, validated data flows
Transformation flows are built with encryption, access controls, testing and audit logging. We align to GDPR, HIPAA and SOC 2, on AWS, Azure and Google Cloud.
GDPRHIPAASOC 2
Real success storiesFinTech · Digital Insurer PropTech · Real-estate marketplace E-Commerce · Retail leader
Outcomes we have driven
Challenge
Raw claims data needed heavy cleaning before it was usable.
Solution
Tested ELT flows in dbt producing analysis-ready, validated data.
Impact
Clean data behind 93% of fraud caught pre-payout.
Challenge
Legacy ETL tools were brittle and slow across markets.
Solution
Migration to warehouse-native ELT with version-controlled models.
Impact
40% faster, more reliable data delivery.
Challenge
Inconsistent transformations produced conflicting metrics.
Solution
Centralised dbt models as one transformation layer.
Impact
+28% lift in conversion from trustworthy data.
Sector-anonymised outcomes shown until named clients are approved.
Why Noseberry
Why choose Noseberry for ETL and ELT
Specialist
AI, Cloud and Data is our core, no generalist dilution.
Tested by default
Transformations are version-controlled and tested, not fragile scripts.
Right pattern per case
We choose ETL or ELT on fit, not habit.
Proven at scale
250+ solutions delivered across 20+ countries.
ETL and ELT, answered.
ETL transforms data before loading it into the target. ELT loads raw data first and transforms it inside the cloud warehouse. Modern cloud warehouses make ELT the common default, and we choose per case.
A data pipeline is the broad flow that moves data. ETL/ELT is the transformation pattern within it that cleans and models data for analytics.
Yes. dbt is our default for transformation and modelling, giving version control, testing and reusable models.
Yes. We migrate legacy ETL tools to modern, warehouse-native ELT in phases, with validation at each step.
Through automated tests, validation and monitoring built into the pipelines, so bad data is caught before it reaches reports or models.
Need clean, analysis-ready data?
Book your free 30-minute strategy session and we will scope your ETL or ELT.
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