ETL Solutions Tools: Quick Review

When discussing the undoubted advantages of various blockchain technology tools in the public space, attention is much less often focused on some weaknesses of this technology. And the first obvious disadvantage of blockchain, which paradoxically arose from the basic concept of this technology, is the high resource cost of all blockchain network processes. Imagine the work of millions of computers around the world, constantly processing a huge number of transactions and passing an incredible amount of data through their memory. It must be said that this entire continuously operating system consumes a large amount of energy, and this is not the only problem with blockchain technology. Obviously, a large amount of processed and stored information requires large amounts of memory and special places for storing both raw and processed data.

Every company always has some kind of data management system. However, at the stage of applying blockchain tools, the existing approach to handling data may not meet the needs of this technology. An individual approach will help you overcome many obstacles https://dysnix.com/blockchain-etl.

In the modern world, any business processes generate, process, store and transmit huge amounts of information. And, of course, various systems have been developed that are designed to facilitate the management and handling of this data. Among such numerous solutions, the ETL approach attracts much attention. Those who, one way or another, have encountered data engineering, certainly know the main stages of this approach: data is extracted, transformed, loaded. In other words, ETL is a process using traditional ETL tools, i.e. server/virtual machine and installed software. Since all the data that the user will retrieve from various sources is processed through this virtual machine, this means that its power must be comparable to the power of the data warehouse. For the practical implementation of ETL solutions, including in blockchain projects, there is a set of requirements and tools for them.

Basic requirements and tools for the Extract and Transform stages

1. First of all, you need to have a tool that will provide connection to data sources and data storage, as well as reading various file formats, for example, XML, JASON, Excel, etc.
2. The next requirement concerns the platform that will provide work with data - Platform Independence. That is, the tool used, especially its client part, must work on almost any operating system (Windows, Linux) or, at least, on an operating system developed by its own specialists.
3. Tools that provide scaling play an essential role. At the same time, an ETL tool that processes data internally must scale along with the data warehouse.
4. Another important requirement is parallel operation when processing a large amount of data. This principle is well implemented in the presence of cloud data storage.
5. With the Design Flexibility/Reuse tool, you can easily change transformations and create or copy new flows.
6. The requirement for Extensibility is met by almost all ETL tools, which makes it possible to independently expand the functionality and create your own components. However, such a requirement cannot be implemented using cloud-based ETL tools that operate within the framework of a ready-made service with predefined capabilities.
7. The requirement for security, authorization and authentication includes the procedure for connecting users to the service, how and how often passwords can be changed, and how access levels can be differentiated, for example, user and developer.
8. Requirements for process administration remain an indispensable attribute of the ETL data management solution.
9. Requirements for Version Control and Change Requests are extremely important, especially in cases where a development team is working. Implementation of these requirements will allow you to track various changes and ensure the storage of versions.
10. The requirement to Disaster Recovery and Backup implies the ability to restore solutions in the event of a complete loss of the server.
11. The Infrastructure as a Code requirement is more suitable for clouds or home-written solutions.
12. The ability to integrate with external systems, such as, for example, Tabcmd or Email, is reflected in the availability of 3rd party Integration tools.
13. Implementation of ETL solutions is impossible without the need to have tools for using popular programming languages, for example, Python, Java, R.Shell.

Speaking about the Transform stage, it should be noted that ETL solutions are most focused on this part of data engineering activities. And it is no coincidence that this process is ensured by a large number of different operations, including Slowly Change Dimensions, Lookup, Pivot/Unpivot, Parameters, Cycles, Aggregation, Creating new fields, Sorting, etc.

Conclusion

And in conclusion, we note that in cases where the Data pipeline fails when starting or crashes with errors, it is necessary to understand the reason for this. This means that it is necessary to trace events and find the moment when the error occurred. In this case, you should start the Testing and Debugging mode, trace all stages of working with data and find the moment and cause of the error.