How to process information stored in different business systems
Information is the lifeblood of any business. This information, subject to the appropriate analysis, gives a critical insight into the work of the company – whether everything is working optimally, or, on the contrary, there are signs of decreased efficiency. That is why you need etl tools for etl pipelines. They will help to identify the most profitable customers, areas that bring the most profit, cost-cutting measures, etc.
Before asking these questions, it is necessary to somehow obtain and process the information stored in various enterprise systems. This process is not trivial when we understand that data is in different formats, applications, and platforms. To get and integrate the necessary information, you can choose different ways, long and time-consuming, or fast and easy – in this case, we will use effective and efficient technologies.
ETL is understood as a mechanism for retrieving data from the company’s operating systems (economics, warehouse management, production, sales, etc.), their further processing, and provision to decision support applications (decision support systems, data warehouses, business intelligence). This issue is quite complex and makes up a small share (sometimes up to 70%) of the cost of creating decision support systems – both financial, time and human. In many projects, unfortunately, such systems are underestimated, especially at the initial stages of development and construction. This approach can negatively affect the speed of development and commissioning of the solution, the ability to respond flexibly to changing conditions, and, last but not least, the overall cost of the solution.
Extraction means getting data from the primary systems. In a typical business, there are many such systems that support vital operations. Typically, they have been deployed at different time stages and use different, sometimes very proprietary and little-discovered technologies. Extracting data from these systems can sometimes be a challenging task, especially if we understand that it is not a one-time action, but a recurring activity that aims to provide fresh data for further processing in data warehouse systems.
Transformation is the process of processing data received from primary systems into a form that meets the requirements of data warehouse systems. The transformation includes a range of operations from transformations, mathematical operations, filtering, normalization, and denormalization to complex methods of creating multidimensional structures. Data coming from primary systems can be (and in the vast majority of cases are) “dirty” with various types of erroneous or incomplete data. Thus, data quality control and data cleansing mechanism are also part of the transformation.
The result of the transformation process is correct and consolidated data with maximum information value.
The final stage is loading, i.e., filling the processed data into the target data warehouse system. Here, specialized technologies are often used for data storage, which involves the use of various proprietary mechanisms for fast and optimal data entry.
Ways to implement ETL
In many data warehouse projects, ETL processes are developed and run on the basis of scripts, i.e., programs created from scratch by the development team in one of the widely used languages – SQL, C/C++, Perl, etc. The advantage of this approach is the minimum initial investment, as developers can usually develop scripts without the need for extensive training, and the relevant infrastructure is already available.
However, the big risk is the demanding administration and support of a script-based solution and minimal flexibility. Writing code is tedious, and error-prone, and places high demands on developers. This is reflected in the relatively low performance and rapidly increasing cost of ETL solutions.
The negative aspects of the development of script-based ETL processes have caused the need to streamline this process. Several software companies have accepted this challenge and launched specialized ETL tools that significantly increase development productivity, provide the necessary flexibility, and, as a result, reduce the cost of developing an ETL system.
ETL tools
Visual Flow will show you which are benefits of using ETL tools:
- high productivity – in an easy-to-understand graphical interface, the developer can design and debug the transformation process in a fraction of the time compared to classical code writing. In addition, this approach is less prone to introducing errors, or possible errors are easily detected and eliminated.
- flexibility – Thanks to the object approach, it is very easy to modify, extend and adapt processes to changing requirements and conditions. A clear graphical representation of the transformation process and “self-documentation” allows even a developer who did not participate in the design of the process to quickly penetrate its logic and make the necessary adjustments.
- productivity. Performance is usually one of the key requirements for an ETL system. ETL tools are designed to make optimal use of hardware and system resources and achieve maximum performance. Ways they achieve this includes multithreaded architecture, consistent use of parallelism, native access to source and target systems, etc.
- openness – ETL tools include technologies to access different types of enterprise systems. The developer does not need to worry about what protocol or language a particular system uses, he just uses the appropriate component.
- metadata support – most ETL tools work intensively with metadata – descriptive information about the source and target objects, transformation rules, transaction statistics, etc. This metadata documents the entire system and can also be synchronized with other data warehouse applications and ensure the descriptive consistency of the entire system.
Types of ETL tools
There are several ETL tools on the market today, whose common mission is to make ETL process development easier and more efficient.
In general, these products are divided into two groups: first-generation ETL tools, which, based on a graphical transformation recipe, generate code that is compiled and executed mainly on the source platform, and second-generation ETL tools, the core of which is a transformation engine that implements ETL processes according to a prescription defined by an object stored in a metadata catalog and graphically designed by the developer.
The choice of the appropriate ETL tool depends on the requirements of the solution, but regardless of whether the choice falls on one or the other, it is always a step towards increasing productivity, bringing order to the development and easy manageability of the entire system. Based on the experience of using ETL tools in the creation and operation of the data warehouse, we can conclude that this is the way in the right direction, and the costs incurred for the purchase of an ETL tool will pay off in a short time.
