Preamble
It is no secret to anyone that today a huge amount of all kinds of data is generated in the world, and many of the actions that we perform are digitized and stored in some databases. Any company which operates with data stores it in some particular system. There can be several such systems, and, in fact, it doesn’t matter what these systems are, since any data in any system ends up in storage for further analysis. This data must be clean, manageable, and ready for analysis. This means that before the data can be used, it must be enriched, shaped and transformed. To cope with this task, you can use various ETL solutions. A similar approach is applicable for web 3 projects using blockchain technology. And in order to speed up and facilitate the work of any company in the data processing of its projects, you need to use etl services specialists as consultants and assistants.
The essence of the ETL process
For a better understanding of the essence of ETL solutions, let’s reveal the meaning of this abbreviation:
Extraction (E) – receiving and transferring unstructured data from the pool to temporary storage.
Transformation (T) – processing data in a temporary storage by structuring, enriching and transforming it.
Loading (L) – transfer of processed structured data from temporary storage to the main one for their further use in analytical processes, including business intelligence (BI) tools.
Based on the essence of ETL, it becomes obvious that this process is carried out in several stages. At the same time, at each stage, interaction between developers and engineers should be carried out. Not the best feature of the process is that you have to work with the capacity limitations of conventional databases. In addition, during the ETL process, access to information is closed until the end of the data processing process itself. The last feature of the ETL is especially upsetting for analysts and BI users.
ELT vs ETL
Today, as an alternative to ETL, a new improved process for working with databases is launched – the ELT process. The essence of this process remains the same, but the procedure is changing. With ELT, the data loading phase begins immediately after the data has been retrieved from the pool. This increases both the scalability of computing and the capacity of data storage. This became possible thanks to the use of cloud technologies. And this is far from the limit for ELT solutions. In the long term, to save time and effort, the ELT process offers BI developers and analysts virtually unlimited access to all data at any time. With such undoubted promising advantages, ELT still remains an imperfect and developing technology.
ETL solution in practice
Consider the importance of using ETL on a specific example. A certain company is engaged in a catalog that contains a variety of information about products: their name, cost, manufacturer, stock balances, etc. The company receives all the information it needs from numerous data providers, and the data is extracted in completely different ways. Since not only the methods of obtaining data differ, but also their formats, for further work the company had to bring them to some kind of average product. After the conversion, the data was loaded into the database of the company itself. All this was done through a kind of “small” ETL solutions, as a result of which consumers always had access to a catalog that contained the latest up-to-date information about products.
Initially, the company, which was interested in applying the ETL process, formulated some requirements that it wanted to see implemented:
1) ETL solution should be standard and widely used.
2) New integrations should be implemented quickly enough. This means that connecting a new network or changing the data transmission format should not lead to lengthy development and implementation processes.
3) Adequate cost of the solution, which implies an acceptable cost of development, implementation and further support of the ETL project.
After the implementation of ETL development, the company faced the following challenges:
1) It was necessary to constantly monitor the state of integrations, that is, to monitor which processes are running at any given moment, which ones work and at what speed, and how to adequately respond to problems.
2) The sidekiq, resque movers require certain improvements, as they are not well suited for multi-step tasks.
3) To build ETL, it is convenient to use DAG, and this leads to the presence of a large amount of software. As a result, a separate highly qualified specialist is required to support ETL/DAG.
4) Programming languages are often used that differ from those known and used in the company.
