It is probably impossible to find a company in the modern world of business that does not form its data into a single, ordered database. Within one 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 them. Here we outline a relevant data engineering process.

Here are the main characteristics of a data warehouse. Subject-oriented, time-variant, nonvolatile, and summarized. 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.

This architecture of the data management process, called the ETL (Extract-Transform-Load) approach, greatly simplifies the management of any database. Speaking about blockchain data, it should be noted that the ETL approach can also be successfully used to manage it. In other words, blockchain etl is one of the most optimal approaches for analyzing data related to transactions in any blockchain, especially in complex and voluminous blockchains.

The essence of the ETL approach

Speaking about the ETL approach, it should be noted that, at its core, it consists of 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, standardization, cleansing and enrichment are included in this stage. Identifying duplicate data and their 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 organized in such a way that the downloaded data can correspond to the queries that will be generated by users of this data. The ETL approach is not an optimal data management process.  This is because it is not without certain inconveniences and problems. One of the main problems that can accompany the ETL process is the problem of scaling.  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 itself does not change. 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 control of the user. 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, in the process of its practical development, became the forerunner of 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 side by side in the target Data Warehouse.

The first scheme includes the presence of 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 his business purposes. The advantage of this approach is that the data is immediately in the target source, which makes it very easy to find and use.

Data Trends

Summarizing all of the above, we can say that the essence of the ETL approach is that the data is transformed in RAM, which requires additional power, but the advantage is that already at the input of the process, some unnecessary data can be immediately cut off. 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 is possible to immediately work with raw data if there is such a need.

Today, due to the increase in disk space, the number of data sources and the amount of data themselves has 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 become the ability to use data warehouses as a source of data for other services, or the so-called “reverse ETL” process.