A data warehouse describes a data storage platform that needs to be assessed against specific standards. In this context, the primary data warehouse is often used to refer to the actual process. A data warehouse architecture covers the entire data analysis process. Within the data warehouse process, the data warehouse is managed and evaluated in four steps:
1. Extract relevant data source systems and transform and transfer them to the data warehouse.
2. Long-term archiving of the data in the data warehouse.
3. Supply and data storage of the data required for the current queries.
4. Analysis of the respective data or the supply of downstream application systems.
Data Warehouse Architecture
The first is a functional database containing, for example, relational information. This is followed by a storage area where data is predefined. Special ETL (Extract, Transform, Load) procedures are used to transfer the data to the data warehouse, where the information is organized and collected.
A data warehouse is, therefore, a form of data storage that operates in parallel with operational data warehouses. This separation is implemented in a way that does not interfere with normal search processes and allows separate access to data.
Several tools are used to access the data in the data warehouse at this stage. Access can be provided at different levels, called data tokens. Data warehouse systems primarily work with relational databases, which can be read using a structured query language.
For example, the most common format for evaluating the data is pivot tables from excel. OLAP databases are used to structure the data as the number of data increases. They can compress data of different sizes and create hierarchies. An example is the query of turnover by production area and production zone. However, the data storage systems must be used correctly. Many user problems are not caused by the system but by poor data quality or lack of technical documentation.
Excel spreadsheets, for example, are among the most popular data formats. As data volumes increase, OLAP databases are used to structure the data. However, the data storage systems must be used correctly. Many user problems are not caused by the system but by poor data quality or lack of technical documentation.
Tasks of Data Warehouse
The purpose of data warehousing systems is to provide the company with an overview of the data it holds and to evaluate them. Four factors are necessary for this:
1. Central Data Storage:
All data relating to the analysis should be stored in the data warehouse.
2. Separation of Analytical and Operational Databases:
The data of the warehouse and the operating system must be managed separately. The data in the data warehouse are used for a broad search. To ensure that the operating system stays intact, it should be separated from the analytical data.
3. Integration of Data From Different Sources:
Most sources provide data formats that ETL procedures can read. The type of data format determines which databases and read-out systems are used.
4. Data History:
Data is stored over time. This allows data to be evaluated over time. This is impossible in operational databases, as information is overwritten several times.
Advantages of Data Warehouse System
– Proven architecture
– Area-specific tools
– Optimal data quality
– High data integrity
Disadvantages of Data Warehouse Systems
– Cannot process unstructured data (video, audio files).
– Long response time for ad hoc queries
Difference Between Data Warehouses and Big Data Analytics
According to blogger and IT expert Bill Inmon, data warehouses are a fixed system architecture, while big data is a technology. Ultimately, both are systems or methods of data analysis. Data warehouses specialize in analyzing general and structured information from SQL databases and can be used with various tools and optimization techniques.
On the other hand, extensive data analysis is not dependent on a fixed system database and is more flexible. It includes several tools for evaluating unstructured data, which are becoming increasingly important in the market.
In addition, big data analytics is beneficial for processing large amounts of data without increasing load time or reducing performance. As big data analytics is still a very new field, some analytical methods and evaluation tools still need to be fully developed.
While a simple architecture for data warehouses can undoubtedly be used as a blueprint, many things must be considered individually and thus modified. Whether internal or external data sources, whether full extract or incremental, whether raw dump or transferred directly to the warehouse, whether high volume or high variability, depending on the requirements you place on the data warehouse, a suitable architecture must be selected.