![]() For example, a user might click on a table column to put the numbers in that column in descending order. Most of the basic functions in a BI tool are abstracted, meaning that the BI tool is turning some sort of human input into an SQL query all by itself. It’s much faster to just drag and drop cards. It also takes longer to actually build a transformation using a coding tool, since the user has to manually type out the whole process. However, as we’ve already mentioned, they’re not very user-friendly. They can also do more complicated transforms that wouldn’t be possible with the basic cards in a drag-and-drop tool. Instead of relying on the drag-and-drop tool, users can transform their data exactly how they’d like it. Tools like these allow for greater flexibility in how a user can perform a transformation. Tools that abstract some data transformation functions have to explain to their users how to use them correctly. Data transformation, in particular, is notoriously difficult to abstract, since so much of the processes rely on the specific data contained in each data set. Most data functions can be more complex than that. A BI tool can easily sort a column in ascending order, because a ‘1’ is always going to be less than a ‘2’, no matter what the data actually looks like. In general, the more specific the function is to the data, the harder it is to abstract. Users don’t need years of experience, like with some other programming languages. With just minimal training, users can often become proficient enough in the language to perform basic transformations. Luckily, SQL isn’t a massively complicated coding language. However, most BI tools use strategies that keep the end-user from having to do literally everything themselves by typing out an SQL query. Even basic operations like filtering a data set or putting it in alphabetical order are done using SQL. Most of what a BI tool does is perform various SQL functions on the data that it currently has stored. In fact, almost every program that stores data uses SQL, which makes it a very valuable programming language to be familiar with. SQL is an industry-wide standard, meaning that every BI tool uses SQL to manage its data. BI tools use SQL to do all sorts of different operations involving data, from data transformation, to data analysis, to filtering and cleaning data. It’s the programming language that databases use to manage and query data. SQL stands for ‘Structured Query Language’. To understand what makes the differences between ETL tools so important, a BI customer needs to understand what SQL actually is. We’ll go over what makes both approaches unique, what would make someone use one kind of tool over another, and which sorts of businesses would benefit most from each approach. Both styles have pros and cons and are good fits for different audiences. A SQL-heavy approach is just as valid as an abstracted, drag-and-drop approach. ![]() Just because these tools are harder to use doesn’t necessarily mean they’re worse, though. There will be a steep learning curve before the average person can build something useful. For those without any SQL knowledge, these tools can be nearly impossible to use. Often, even simple data transformations require SQL queries to function. Tools like these expect their users to bring more SQL knowledge to the program. Many BI tools on the market don’t have very robust abstracted tools for performing data transformation. These aren’t just last-gen or niche programs, either. However, some BI tools still force their users to input a lot of SQL at the data transformation step. This means that they can make more effective visualizations, since they have access to more useful data. This means that, with the help of drag-and-drop tools and other, similar features that abstract the data transformation process, users can transform their data in complex ways without any real training in SQL. These tools tend to abstract common data transformations, preventing the need for complex SQL formulas to do that sort of work. With tools like these ones, it’s much easier for those without much BI or data experience to build data transformations. These tools, such as Domo’s Magic ETL, allow users to build out complex transformations with an intuitive, drag-and-drop toolkit. ETL stands for Extract, Transform, Load, and is a common term used within the BI space. ![]() ![]() Many business intelligence (BI) tools have features meant to streamline and demystify the ETL process. ![]()
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