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These days, companies must often cope with and organise large amounts of data. The ETL process helps to centrally collect data with different structures from one or more sources.
ETL stands for extract, transform, load. The data is extracted from the existing sources, then transformed, and finally loaded into a database or data warehouse. Thanks to the ETL process, companies have well-defined data for their business intelligence applications. Data that is always available for business-related analyses and decisions. This is a clear competitive advantage, as it enables well-founded and quickly realisable strategies. Just when they are needed.
Companies know from experience:
Where can I now find reliable sales figures from the last quarter?
Why is the data incomplete?
Why doesn't the import work?
Why don't I have access authorisation?
For business-related decisions, in practice data must often be derived from heterogeneous sources. In addition, the data is then often redundant. The access times are usually high, evaluation difficult and decision-making is delayed. Sometimes the data query is not only difficult, but also flawed, because different data sources can provide different results. Due to this inconsistency, management and employees cannot always rely on identical and valid data.
Moderne Business-Intelligence-Technologien wie Qlik und Jedox decken den ganzen ETL-Prozess ab. Ei Data Warehouse kann optional einbezogen werden, ist jedoch nicht mehr nötig.
Modern business intelligence technologies such Qlik and Jedox cover the entire ETL process. A data warehouse can be included optional, but is no longer necessary.
The development of a structured ETL process can solve these existing business problems. It controls the data integration in databases and consists of three typical steps:
In the first step, the data source and the data to be extracted are defined for the data integration process, since during extraction, only relevant sections of the source data are selected for the analysis. This ensures high performance and at the same time lowerer complexity.
In the second step, the data is transformed.It consists of several sub-steps, where for example defects are adjusted, data enriched and related data is summarised. The advantages of this are in the standardised data formats and the alignment to the target system instead of the source system.
In the third step, the now consistent data is read into the destination server as a database or data warehouse. Existing data analysis systems can be updated accordingly. The decentralised data is now centrally available and the source system is no longer burdened
Our Individual standard BI platform iVIEW provides a ready-made framework that covers the entire ETL process. The data warehouse is no longer necessary, but can be included optional. Thanks to a simplified configuration with parameterized control functionality iVIEW is suitable for implementation in standard connections such as ABACUS, SAP, etc.
Depending on the infrastructure and the IT workflow of each company, ELT can also be used instead of ETL. This does not change the process, but only the order of the steps. Unlike in the ETL process, the extracted data is initially loaded as raw data on the target server, and then transformed by the ELT process. The same objective is achieved in both variants: Reliably supplied data from different sources can be used centrally within a single business intelligence solution.
Extraction (extract) of the relevant data from different sources
Loading (load) of data in the data warehouse (target database)
Loading (load) of the raw data in the data warehouse (target database)
Transformation (transform) of the data in the schema and format of the target database
Informed decisions are based on reliable information. Business intelligence solutions ensure management-relevant information, at all times. This can be displayed correctly and consistently by data from ETL processes.
Businesses use standardised data from a single source, thanks to the ETL process.
All relevant information for daily business is available to all the decision makers, at all times.
Due to the transformation, the data is extended by business KPIs.
Related data is structured and thus easily found in the database, due to transformation-related compression.
The data can be individually imported into programs for company calculations, business analyses or graphical display.
The validity of the data gives a secure basis for the development of corporate strategies.
Processes within the company can be accelerated, because the data can be accessed more quickly.
The use of data from various systems, as is common in some industries, is now possible from a single direct source.
When companies cannot make relevant and safe management decisions due to accessing data from multiple sources.
When data queries are difficult to realise, partially incorrect or actually impossible.
When it is desirable to introduce a single data source database in the company.
When large volumes of data processing must be handled, as in business intelligence or big data analytics.
Particularly for SMEs, when they need reliable sources of information in the face of growing market requirements and data volumes.
We support discerning SME clients, using innovative business intelligence solutions, to find sound answers to business-related issues and so make the right decisions for safe and reliable company management.