What is ETL (extract, transform, load)?
ETL is a three-step data integration process in which data from various sources is extracted, transformed into a consistent format, and loaded into a central system – providing the foundation for high-quality analyses, reports, and decision-making processes. Bissantz supports companies in integrating data from various source systems and making it usable for analysis, planning, and reporting.
| Characteristic | Details |
| Category | Data integration / business intelligence / data architecture |
| Application | Integration and preparation of data from various source systems for centralized analyses |
| Typical areas of application | Data warehouse, business intelligence, controlling, reporting, machine learning |
| Related terms | Data warehouse, ELT, data integration, data lake, data quality, business intelligence |
| Benefits | Unified data foundation, higher data quality, faster decisions, scalability |
At a glance
Three steps: extract, transform, load.
The foundation for business intelligence, data warehouses, and data-driven business management.
Ensures data quality, consistency, and a unified data foundation from heterogeneous sources.
Difference from ELT: the order of transformation determines the architecture and area of application.
ETL definition
The abbreviation ETL stands for extract, transform, load and describes an essential process in the field of data integration.
The goal of the ETL process (also known as an ETL pipeline) is to extract data from various sources (extract), transform it into a consistent format (transform), and load it into a central repository – usually a data warehouse (load). This three-step process ensures that data from both internal and external sources is available in a clean and consistent form. ETL therefore provides a unified, high-quality data foundation that companies can use for data analysis, reporting, machine learning, decision-making processes, and business intelligence applications such as DeltaMaster.
Why is ETL important?
The ETL process is crucial for many companies to efficiently leverage their diverse data sources, such as CRM software, IoT devices, or social media. ETL is therefore a central component of modern data strategies due to several benefits:
Data integration and centralization: ETL enables data from various sources to be combined and transferred to a central data warehouse. This creates a unified, comprehensive data foundation that can be used for BI and analyses.
Data quality and consistency: During the transformation process, data is cleaned, standardized, and made consistent. This reduces incorrect or duplicate entries and ensures reliable analyses.
Efficiency in decision-making: ETL makes data available more quickly, supporting timely and well-founded decision-making. Companies can identify trends, optimize processes, and proactively respond to market changes.
Scalability and flexibility: A well-implemented ETL process can be easily adapted and scaled to accommodate growing data volumes and changing requirements. This ensures that companies can build on a solid data strategy over the long term.
How does ETL work? – The ETL process
The ETL process consists of three main steps that ensure data is transferred from its source systems to a central data system and prepared for analysis:
1. Extract
In the first step, extraction, data is collected from various source systems. These may include relational databases, cloud applications, CRM systems, or even simple Excel files. The goal is to extract the required data as efficiently as possible without affecting the source systems. The extracted data is transferred to an intermediate storage area (staging area) so that it is available for subsequent processes. Extraction can be performed in different ways, including full extraction or incremental extraction, which captures only changed data.
2. Transform
After extraction comes the transformation of the data. During this phase, the extracted raw data is cleaned, standardized, and converted into a format suitable for analysis and compatible with the target system. Typical transformation steps include:
- removing duplicates
- correcting errors and inconsistencies
- filling in missing values
- standardizing data types and formats
- performing calculations and aggregations
Transformation is essential for ensuring high data quality and consistency, which are necessary for meaningful analyses.
3. Load
The final step of the ETL process is loading the transformed data into a target system, usually a data warehouse. During loading, the data is stored in a way that enables fast access and comprehensive analyses. A distinction is made between full loading (for the initial population) and incremental loading, in which only new or updated records are transferred. Loading can be automated and performed either in real time or as a batch process, depending on the company’s requirements and data volume.
What is ELT? – The difference between ETL and ELT
The main difference between ETL and ELT lies in the order of the steps: ELT (extract, load, transform) is a data processing method that works similarly to ETL (extract, transform, load), but follows a different sequence. While data is transformed before being loaded into the target system in an ETL process, transformation takes place after loading in ELT.
This results in a large collection of data in different formats. Transformation is performed later within the target system itself. The advantages of this approach are the flexibility of retaining data in its original state and the ability to perform analyses directly in the target system. This means new data and transformations can be added without changing existing data structures.
| Characteristic | ETL | ELT |
| Sequence | Extract → transform → load | Extract → load → transform |
| Location of transformation | Before loading, in the Staging Area | In the target system itself |
| Data state in target system | Cleaned and structured | Raw, in its original format |
| Typical architecture | On-premises data warehouse | Cloud data warehouse |
| Strengths | High control, compliance, data security | Flexibility, speed, real-time analyses |
| Typical application | Financial institutions, regulated industries | E-commerce, big data analyses, cloud-native environments |
The choice between ETL and ELT depends on the company’s individual requirements, existing infrastructure, and objectives:
ELT is particularly suitable for companies that want to analyze large volumes of data in real time and already have a powerful cloud architecture. The target system is therefore often a cloud data warehouse. Since transformation takes place in the target system, ELT is often faster and more flexible. Example: an e-commerce platform that collects terabytes of usage data every day and loads it directly into a cloud data warehouse, where flexible transformations are performed for ad-hoc analyses.
ETL is useful when data requires extensive cleansing and standardization before loading. Companies that rely on existing on-premises architectures or have strict compliance requirements frequently use ETL. Example: A financial institution that transforms and cleans sensitive customer data before loading it into an internal data warehouse to ensure maximum control.
ETL and Bissantz
At Bissantz, ETL is a central foundation for high-quality data analysis. The DeltaMaster business intelligence software is based on the principle that only well-integrated and cleansed data can deliver reliable results. DeltaMaster therefore seamlessly supports ETL processes – whether as part of a traditional BI architecture or in conjunction with modern data warehouse and data lake infrastructures.
AI-supported ETL processes ensure that data from source systems such as ERP, CRM, or production units is consolidated and transformed into a single, trusted source of truth. This ensures that KPIs are calculated correctly, dashboards are built consistently, and analyses can be performed efficiently.
Practical example: An industrial company with five subsidiaries operates different ERP systems. An ETL process loads all relevant data into a central data warehouse on a daily basis, cleanses it, and prepares it according to a standardized KPI logic. DeltaMaster accesses this data foundation and automatically delivers up-to-date reports – without manual intervention, inconsistencies, or delays.
FAQ – frequently asked questions
ETL is the process of bringing together data from different systems, preparing it, and storing it in a central location. It is like a translator between languages: data from different sources often “speaks” different formats – ETL translates, cleans, and combines it into a common, reliable language.
The staging area is an intermediate storage area where extracted raw data is temporarily held before being transformed and loaded into the target system. It protects the source systems from excessive load and enables a clean separation of the individual process steps.
Full extraction transfers all data – typically for the initial population of a data warehouse. Incremental extraction captures only new or changed data. This is more efficient for regular updates because it reduces the load on source systems and shortens loading times.
During the transformation step, errors are corrected, duplicates are removed, and formats are standardized. A poorly configured ETL process, on the other hand, can cause incorrect data to enter the data warehouse, directly affecting the reliability of all downstream analyses.
Bissantz supports companies from the design of the ETL architecture through to technical implementation. DeltaMaster integrates data from ERP, CRM, and other source systems via standardized ETL processes, prepares it according to a consistent KPI logic, and provides it as a consistent single source of truth for Controlling, planning, and reporting.
Summary
ETL is the invisible foundation of every high-performance business intelligence architecture: it ensures that data from heterogeneous sources is available in a clean, consistent, and analysis-ready form – providing a reliable basis for reporting, controlling, and data-driven decisions. With DeltaMaster and its consulting expertise, Bissantz makes ETL an integral part of an end-to-end data strategy – from source connectivity to decision-oriented analysis.
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