ETL is a term for the things that happen when data is moved from different systems to a single repository. The abbreviation stands for Extract, Transform, and Load. This is precisely what happens to files during the transfer process.
Typically, ETL processes are used when you need to transfer a lot of heterogeneous data, like collecting them, bringing them to a standard view, downloading them into a new system, and saving all the information along the way. The systems are different, and the task of ETL is to adapt data from different sources. In this article, we will observe etl data modeling best practices. Let’s start!
How helpful is ETL?
ETL is a set of data warehouse management processes, including:
- Extracting data from external sources (database tables, files).
- Data conversion and cleaning according to business needs.
- The uploading of processed information to the corporate data warehouse (CQD).
- The concept of ETL arose from the emergence of many corporate information systems that need to be integrated to unify and analyze the data stored in them. A relational data presentation model suitable for the needs of transactional systems has proven ineffective for integrated information processing and analysis. The search for a unified solution led to the development of data warehouses.
The application of ETL is to organize such a data structure by integrating different information systems. Considering BI technologies are positioned as «concepts and methods for improving business decision-making using data-based business systems,» we can conclude that ETL directly belongs to this technological stack.
What do ETL systems include?
Regardless of the construction and operation of some ETL-system, it is responsible for the implementation of 3 main stages of the ETL process:
- extracting data from one or more sources and preparing them for transformation;
- data transformation – the transformation of formats and coding, aggregation, and cleaning;
- Data upload – recording of the converted data, including information about the structure of their presentation in the necessary data storage system or display case.
Thus, the ETL process transfers information from the source to the recipient through an intermediate region containing auxiliary tables created temporarily and exclusively to organize the unloading process. The analyst describes the data flow organization requirements. Therefore, ETL is not only a process of transferring data from one application to another but also a tool for preparing data for analysis.
The main functions of the ETL system can be presented as a sequence of data transfer operations from OLTP to OLAP:
- Uploading of data (Raw Data) to ETL for further processing. This is done by matching the incoming rows: the download fails if the source system has more rows than Raw Data.
- Validation of data, when data is checked for correctness and completeness, an error report is compiled for correction.
- To match the data to the target model, add columns to the validated table based on the number of directories in the target model and then match the values of the target directories in each additional cell row.
- Data aggregation is required due to the difference in detail between OLTP and OLAP systems. OLAP is a fully denormalized fact table and its surrounding reference tables based on an asterisk or snowflake scheme. Thus the full detail of OLAP sums is equal to the number of permutations of all elements of all directories. An OLTP system may contain multiple amounts for the same directory elements. OLTP detail mapping is required to trace which rows of OLTP formed the sum in the OLAP cell.
- Unloading to the target system using the connector and interface tools.
What is ETL data modeling?
Data modeling is creating a data model for storing data in a database. This data model conceptualizes data objects, relationships between different data objects, and rules. Data modeling helps visualize data and ensures compliance with business rules, regulations, and government data policies. Data models provide consistency in agreements about names, default values, semantics, and security.
The data model emphasizes what data is needed and how it should be organized, not what operations should be performed with the data. The data model is similar to the architect’s design, which helps to construct a conceptual model and establish relationships between data elements. Moreover, Visual Flow can always share with you the best ETL data modeling best practices.
Why use a data model?
The primary purpose of using the data model is:
- Provides accurate representation of all data objects required for the database. Skipping data will result in erroneous reports and incorrect results.
- The structure of the data model can indicate comparative tables, primary and external keys, and collected proceedings.
- It shows the primary data and can be transformed by database developers in large projects.
- As well it helps find missing and redundant data.
- Although the data model’s initial creation is time-consuming, it ultimately makes IT infrastructure upgrade and maintenance cheaper and faster.
Data model types
There are three different types of data models:
- Conceptual: This data model defines WHAT the system contains. Stakeholders and data architects usually create this model. The aim is to organize, capture and illustrate business concepts and rules.
- Logical: specifies how the system should be implemented independently of the DBMS. Data engineers and business experts usually create this model. The aim is to develop a technical map of rules and data structures.
- Physical: This model is usually created by the database administrator and developers. The objective is the actual implementation of the database.
Conclusion
The most straightforward implementation of ETL can be written yourself. You need to know the appropriate programming language, understand the architecture of processes, and can apply algorithms for data conversion.
Free ETL tools can be found simply by downloading and installing them. It will require a learning environment with databases or other repositories from which data can be transferred.
To work effectively with ETL processes, you need to understand the theory. You will help with textbooks, tutorials, or professional courses (under the supervision of mentors, you will receive structured and up-to-date information). Or, to save time, you can rely on the professionals from Visual Flow. They will help you to generate and maintain any of your projects!
