§D Illustration einer Datenarchitektur

Data Architecture as a Strategic Lever

Never before has data infrastructure been as central to business success as it is today. Whether in marketing, controlling, or product development, data driven decision making has long become standard practice. Yet while many organizations focus on analytics, the underlying architecture is often overlooked.

And that architecture ultimately determines scalability, flexibility, and future readiness.

At the heart of this architecture lies a fundamental decision: ETL or ELT? Two approaches to preparing data from source systems. This article explores what these methods involve, how they evolved, and why ELT is often the better choice in the cloud era, but not always.

Looking Back: From Data Management to the Lakehouse

The origins of ETL date back to the 1970s, when Bill Inmon introduced the concept of data warehousing: structured data preparation on a dedicated platform, typically on premises.

Over time, this led to established standards such as:

  • ETL processes for data integration
  • Relational databases
  • Modeling approaches such as star schema, snowflake schema, and later Data Vault

The 2000s brought the first specialized BI tools such as QlikView, along with new performance and visualization requirements. The real disruption came in 2011 with the rise of cloud data warehouses like Google BigQuery and later Snowflake. They fundamentally changed how data is stored, processed, and analyzed.

With them, a new paradigm emerged: ELT.

What Is ETL and Why Was It the Standard for So Long?

ETL stands for Extract, Transform, Load. For many years, this classic data integration process was the de facto standard, especially in traditional IT environments.

The process begins with extraction: data is pulled from various source systems such as databases, CRM, or ERP systems. In the transformation step, the data is cleansed, formatted, and harmonized, for example by removing duplicates or standardizing formats. Finally, the data is loaded into a target system, typically a data warehouse.

Advantages:

  • High control over data quality and transformation
  • Strong fit for on premises infrastructures with limited target system performance
  • Proven in highly regulated industries such as banking, insurance, and healthcare

Disadvantages:

  • Scaling requires dedicated ETL servers
  • Long development cycles and high maintenance effort
  • Limited flexibility for new requirements or data sources

Typical tools include Talend, Informatica, IBM DataStage, and Microsoft SSIS.

What Is ELT and Why Is It the Standard Today?

Unlike ETL, ELT reverses the order of operations. ELT stands for Extract, Load, Transform. The key difference lies in when and where the transformation happens. In ETL, data is transformed outside the target system before loading. In ELT, raw data is first loaded into the cloud data warehouse and transformed there.

As with ETL, data is extracted from various source systems. However, instead of transforming it in a separate staging environment, the data is immediately loaded in its raw form into modern cloud platforms such as Snowflake, BigQuery, or Databricks. Transformation then takes place within these platforms, leveraging the full computational power of the cloud.

Advantages:

  • Scalability, as cloud platforms dynamically scale compute resources
  • Flexibility, since raw data remains available for exploratory analysis and AI use cases
  • Speed, with shorter time to value enabled by modern tooling and self service

Disadvantages:

  • Higher demands on target systems in terms of cost and governance
  • Risk of losing control without proper data management
  • Requires a modern data strategy and platform expertise

Typical tools include Fivetran, Airbyte, dbt, Qlik Talend Cloud, and Azure Data Factory in ELT mode.

Hybrid Strategies: When ETL and ELT Coexist

In practice, it is rarely a strict either or decision. Instead, both approaches often complement each other, for example in the following scenarios:

This table compares ETL and ELT methodologies, highlighting that while ETL is better for traditional on-premise systems and complex pre-loading transformations, ELT is the preferred choice for modern cloud-native platforms and advanced data science applications.

Example: Modern Architecture with ELT

A company wants to analyze marketing and sales data from Salesforce, HubSpot, and web tracking in a Snowflake data warehouse.

  1. Replication of raw data using Fivetran or Qlik Talend Cloud for extract and load
  2. Modeling and transformation with dbt for transform
  3. Visualization with Looker or Power BI
  4. Additional use cases such as forecasting with Python and Reverse ETL into HubSpot using Census

→ The result is fast, scalable, and flexible. Each layer can be replaced independently, enabling a composable architecture.

Future Trends: What Is Shaping the Industry?

The world of data processing is evolving rapidly, and modern ELT architectures are at the center of many current innovations.

One clear trend is the use of AI powered assistants, such as AI pair development tools or copilots. These solutions support data teams in writing SQL queries, defining data models, and even automating documentation. They increase productivity while also improving quality.

At the same time, streaming and near real time ELT are gaining importance. For use cases involving IoT devices or large scale log data, timely analysis is critical. Specialized tools such as ApacheKafka or Apache Flink enable processing of data almost in real time.

The technological foundation is also shifting. Modern open table formats such as Apache Iceberg, Delta Lake, or Apache Hudi allow ELT processes to run directly on data lakes using platforms like Databricks or Microsoft Fabric. This enables hybrid architectures that combine flexibility and performance.

Another major trend is the rise of Data Mesh and data products. In this approach, data is no longer managed centrally but is owned and provided by individual business domains. ELT plays a key role here as an enabler of domain ownership and flexible data architectures.

Finally, FinOps principles and sustainability considerations are moving into focus. Reducing unnecessary data movement and optimizing resource usage can lower both costs and energy consumption. This is increasingly important in the context of ESG and Green IT initiatives.

Conclusion and Recommendation

ELT is undoubtedly the direction of the future, but that does not mean ETL is obsolete. Organizations starting fresh or operating primarily in the cloud should strongly consider ELT. The advantages of modern cloud platforms combined with powerful ELT tools make it the preferred approach for data driven organizations.

At the same time, companies with established ETL structures or particularly complex requirements do not need to replace everything at once. In many cases, hybrid architectures that combine ETL and ELT offer a pragmatic and future ready solution.

What truly matters is not the specific tool but the underlying data strategy. Only organizations with a clearly defined strategy can work successfully with data in the long term, regardless of technology or architectural model.

A guiding principle should be Build for Flexibility. Data architectures must be designed to adapt quickly and efficiently to new requirements, whether for generative AI, new business use cases, or technological developments that may not even be foreseeable today.