![]() ![]() Of course, implementing an HTTP downloader and parser from scratch would be a lame idea since many great open source solutions are out there at your service. This is where the concept of scraping comes into play. The data would be most likely intended for humans, not ETL agents, and presented in HTML format as a traditional browsable website. The above situation when the data is available in a structured format is unfortunately rarely the case, especially since the data source ownership is outside the scope of your organization. In such a case it would make sense to compress files before publishing, e.g. A less efficient yet viable option is storing data in a file and publishing it on a web server. It is highly recommended to follow REST protocol principles when designing your endpoint. Therefore the most efficient and hence popular way to access source data is to create an HTTP endpoint that will feed the data in a structured format like XML, JSON, or CSV. On the other hand, getting direct access to a source DB is overkill as we need to read the data and any writing is out of the ETL functionality scope. However, in the real world, granting direct access to the database service is rarely the case as it imposes high-security risks and might generate breaches and vulnerabilities. In case the source data is accumulated in a structured data storage like PostgreSQL or MongoDB and the source owner belongs to your organization, direct access via query language interface and DB driver could be an option. ![]() The more complex and valuable your data is, the higher are the chances it will be scattered across numerous sources and presented in various formats. Here is the list of some of the challenges you may face when developing your data processing stages: Extract, Transform and Load. When building an ETL system you are likely to run into a combination of issues that depend on the nature of your data. This article will get you familiar with issues, challenges, known solutions, hacks, and best practices applied in the world of the modern ETL process. In simple words, if your system is going to move data from one or more sources, change its format in some way, make it available via some data management system, and do it repeatedly then you can be sure you are going to develop an ETL system even if you don’t call it that way. They say “data is the new oil”, so just like with oil, it’s not enough to find it, you will need to invest a lot in its extraction, processing, transportation, storage, final product distribution, etc. ETL (Extract, Transform, Load) is a well-known architecture pattern whose popularity has been growing recently with the growth of data-driven applications as well as data-centric architectures and frameworks. ![]()
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