Data Ingestion & ELT

Technologies:

• Qlik Talend Cloud
• dbt (Data Build Tool)
• Snowflake Openflow
• Azure Data Factory

Data Ingestion (EL) and Transformations (T)

Querying source systems directly from BI tools may work for small use cases, but it quickly leads to performance bottlenecks, security risks, and limited governance. For scalable, enterprise-grade analytics, a structured data ingestion and transformation approach is essential.

Traditionally, ETL tools like Informatica or SAP Data Services handled data movement and transformation before loading into a warehouse. Today, modern cloud platforms and ELT patterns shift transformations closer to the data—unlocking greater flexibility, scalability, and speed.

Today, cloud platforms, real-time replication, and data streaming have reshaped the landscape.

Our Approach:

• Minimal impact on source systems, using Change Data Capture (CDC) where possible.
• Pragmatic balance between real-time and batch – avoiding unnecessary costs by not over-engineering when batch is sufficient.
• Transparency and team scalability through lineage, version control, and multi-developer support – because what works for a single analyst quickly breaks down when many people need to collaborate at scale.

This is why we’ve tested and selected specific technologies for different scenarios.

Transformations (T)

Transformations are often the most complex part of the data pipeline. They require a strong foundation in SQL, a deep understanding of business logic, and careful handling of data quality and change over time.

We approach transformations with:

• Clear, modular logic that scales across teams
• Strong governance and testing practices
• Tools like dbt to enable collaboration and maintainability

Done right, transformations turn raw data into reliable, trusted insights—powering analytics across the organization.