It is likely impossible to find a company in the modern business world that does not organise its data into a single, ordered database. Within a company, there may be several databases. Large companies may even have several dozen or even hundreds of ones. For data to be used for further analysis, it must be obtained from databases or warehouses in a form that is suitable for working with it. Here we outline a relevant data engineering process.

Here are the main characteristics of a data warehouse. Subject-oriented, time-variant, nonvolatile, and summarised. The architectures of different data warehouses can be arranged in different ways. They can be single-level, two-level or three-level. In such architectures, only their input and output will be common: data sources and data consumers. At the same time, the use of a two- or three-level architecture implies the presence of a certain intermediate array. One in which the data must be transformed for its intended use.

The architecture of the data management process, known as the ETL (Extract-Transform-Load) approach, significantly simplifies database management. Regarding blockchain data, it is worth noting that the ETL approach can also be successfully applied to manage it. In other words, blockchain etl is one of the most optimal approaches for analysing data related to transactions in any blockchain, especially in complex and voluminous blockchains.

The essence of the ETL approach

Regarding the ETL approach, it is essential to note that, at its core, it comprises three main logical stages. The first step is to extract data from one or more sources, and data extraction methods may vary. The second stage includes the data transformation process.  This, in turn, can consist of several local actions. Actions such as data profiling, standardisation, cleansing and enrichment are included in this stage. Identifying duplicate data and its possible deduplication are also included.  The third step in the process is loading the converted data into a specific storage location.

At the same time, the download must be organised in such a way that the downloaded data can correspond to the queries that users of this data will generate. The ETL approach is not an optimal data management process.  This is because it is not without certain inconveniences and problems. One of the primary challenges that can arise during the ETL process is scaling issues.  Scaling is associated with the efficiency of data movement from source to consumer.

The Staging Area

Data management experts believe that the ETL model can be expanded by applying some additional concepts. One of which is the possibility of using the SA (Staging Area) in ETL. When using ETL SA, the essence of the approach remains unchanged. The difference is that after the extraction stage, the “raw” data is placed in temporary storage. This temporary storage is known as the Staging Area, which is completely under the user’s control. The ETL SA concept has several advantages compared to the traditional ETL approach:

  • Lower costs for restoring a “fallen” process;
  • The backup process of source data is facilitated.
  • The debugging process is simplified in case of any errors.
  • The disadvantage of ETL CA is the increased cost of creating an additional database.

A New Approach to Data Engineering

It should be noted that the ETL SA concept, as it evolved in practice, became the precursor to a new approach in data engineering: the ELT (Extract-Load-Transform) approach. As the name suggests, the conversion and loading steps are swapped, which gives this approach certain advantages. The essence of the ELT approach is determined by the order of actions in which two data management schemes operate concurrently in the target Data Warehouse.

The first scheme includes a Raw Data zone. This is where “raw” data from the source/sources is loaded and stored. In the same target Data Warehouse, all necessary data transformations are carried out. After which, they are loaded into the second schema, which contains the Transformed Data zone. Next, the BI Tool comes into play, and the consumer receives the necessary information for their business purposes. The advantage of this approach is that the data is immediately available in the target source, making it very easy to find and use.

Data Trends

Summarising all of the above, we can say that the essence of the ETL approach is that data is transformed in RAM, which requires additional power; however, the advantage is that some unnecessary data can be immediately eliminated at the input of the process. With the ELT approach, the transformation is carried out “in place”, that is, in the target Data Warehouse itself, but this requires more disk space. Another advantage of ELT is that it allows for immediate work with raw data when necessary.

Today, due to the increase in disk space, the number of data sources and the amount of data themselves have begun to grow, which means there is a need to use more complex data engineering process models. Accordingly, the number of different tools for working with data is rapidly growing. A noticeable trend has emerged, enabling the use of data warehouses as a source of data for other services, also known as the “reverse ETL” process.