By Brian Laleye · August 9, 2022 · 9 min read
SQL is a language used to manipulate data in databases.
It’s a standard query language that’s been around since the 1970s, but it has never been more popular than it is right now.
The popularity of SQL has been rising recently because it’s so easy to learn.
People who aren’t programmers can read SQL queries and understand what they do.
This makes it easy for non-technical people (such as business analysts) to create reports without having to learn an entire programming language like Python or Java.
In short, SQL is the lingua franca when it comes to data transformation and data manipulation from various sources.
Let’s dive into the pros and cons of using a language that has been around since the 1970s!
Is it still the only (or at least the best) way to get insights from various, disparate, structured / unstructured, and asynchronous data sources?
SQL stands for Structured Query Language.
It’s a way of interacting with databases and other data stores.
SQL is used by people who work with data all day long — analysts, marketers, data scientists and more — because it lets them perform complex transformations on their data without having to write code or use complex APIs.
In SQL, you describe what you want to do with your data (such as “find all customers living in Houston”), rather than how to do it (such as “read all records from table X and filter out those greater than $2,000”).
This makes it easier to understand how the query will work and what results it will produce, which is especially important if you need to share your queries with others or reuse them later on.
SQL permits complex manipulations of tables and columns, including grouping by multiple variables at once, using expressions like SUM() to calculate a result based on other column values (“what is each customer’s total spending?”), joining multiple tables together into one query (“show me all orders placed by customers who live in Texas”), and much more.
It’s portable between databases. If you have experience writing SQL queries for one database system (for example, PostgreSQL), then you can use that knowledge when working with others. Even if there are some differences in terms of syntax and versions.
SQL is designed for relational databases only.
You can’t use SQL to access documents (JSON, XML) effectively because it doesn’t know how to traverse the tree structure of JSON or XML documents and it can’t easily perform operations across them.
You need to use MongoDB or Neo4j to perform queries on non-relational databases.
SQL doesn’t allow you to write loops or functions in it.
You also can’t do things like create objects or control other processes from within your SQL statements.
That means that if you want to add an element to an array or change its type, you have to do that with a separate update statement for each element in the array.
This means that if you want to execute two queries at once and return their results as an array, you can’t do this without using multiple queries or temporary tables.
Another thing, SQL is getting low when you need to query high volumes of data from different tables from different databases.
So, what about mixing structured and unstructured data sources then? What about performing queries to get insights from disparate sources like Hubspot, Snowflake, Facebook Ads, and Google Analytics all at the same time?
Without any doubt, SQL is the go-to query language to get analytics from data sources but based on the feedback of 100+ data professionals and its intrinsic limitations, it’s about time to rejuvenate the way we handle data with SQL and not just structured ones!
No Code / Low Code SQL Editor is one type of next-gen tool that has emerged to smooth rough edges down of SQL.
This type of platform tries to make SQL readable for a wider audience and enables you to code SQL using visual components (drag-and-drop mainly).
This visual query creation method appeals to both SQL experts and newcomers as a means to speed up and simplify the process of querying data and enhance the collaboration with teammates.
No Code / Low Code platforms have become a powerful tool for engineers, data, and ops teams to build applications faster with less time and effort.
The main advantages are:
The ability to build rich applications without writing any code gives technical and non-technical teams more time to focus on business logic, which can result in faster development, deployment, and scale.
There is no need for you to go through the hassle of writing code manually. This greatly reduces risks such as security vulnerabilities and bugs caused by manual coding.
No Code /Low Code platforms enable better collaboration with any stakeholders (teammates, users, partners, providers…) because they can work together directly in an all-in-one platform.
Stakeholders can easily provide feedback during the development process and help teams improve product quality.
There are two major types of No Code / Low Code platforms:
Regarding SQL, what are the enhancements that a No Code /Low Code could provide?
What are the advantages of a No Code / Low Code SQL editor? And how to choose one?
The best relational-database query language is still SQL, it just needs to be rejuvenated to be widely adopted and used by any team.
Consequently, your No Code /Low Code SQL tool should be a next-generation Visual SQL editor that has no intentions to replace SQL but to highlight it at its best.
The No Code / Low Code SQL builder should help you build and generate the query on the fly and even allows you to edit raw SQL without friction.
Using automation and intuitive design, a No Code / Low Code SQL editor makes SQL coding faster, easier, and more efficient.
To overcome the limitations of SQL, a No Code / Low Code SQL editor would need to provide a set of built-in features for both SQL experts and newbies.
First, when you utilize a No Code / Low Code SQL editor, visual components such as drag-and-drop operations or choosing previews should instantly create code.
The SQL query will be updated when you pick an option or make changes to the visual components.
SQL experts may therefore proceed considerably more quickly because they aren’t tied down with coding or changing repetitious queries.
This automation can redirect experts’ attention away from SQL tinkering and onto truly analyzing and examining their data.
Also, when it comes to analyzing and optimizing the query, a top No Code / Low Code SQL editor needs to embed handy features:
These kinds of features are crucial because, despite its quirks, SQL isn’t going away any time soon.
In fact, many of its flaws are evidence of its tenacity. SQL is here to stay, so make sure your data teams have the right tools they need to utilize it effectively.
SQL experts want automation to work swiftly, sophisticated editing options to work deeply and advanced collaboration features to work efficiently with teammates.
The idea is to democratize the use of data in any organization for both technical and non-technical teams.
So, the main principle is to help them collaborate fluently by adopting, using, and developing the same (query) language without barriers.
This new kind of tool makes it easy to create data pipelines for dashboards and data analytics purposes.
It is now possible for Ops and Business teams with non-technical abilities to create and automate data analysis using complex queries from multiple and disparate data sources (SQL and/or NoSQL).
Thanks to a next-gen No Code / Low Code SQL editor or also called a Data Transformation Tool, developers and engineers can free up to 20% of their time by giving autonomy to data teams and removing the endless communication back-and-forth.
At RestApp, we’re building a Data Activation Platform for modern data teams. We designed our next-gen data modeling editor to be intuitive and easy to use.
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