What is the Difference Between SQL and Excel?

difference between SQL and Excel
Written by Marion Desvignes
Database
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December 28, 2021

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Google Sheets and Excel are the widely most advertised and most known applications used to create spreadsheets. The idea of a spreadsheet is a tool used to store and analyze data, but if I told you that there was something more advanced that not only could work faster but more efficiently. 💨

Data can be easily obstructed if not structured correctly, utilizing SQL you will not only get the luxury of nicely structured and organized data, it will allow you to connect to an outermost data source, table of contents, as well as imported data, all without the unnecessary hassle of inputting data in manually!

Nonetheless, before writing SQL language, to create, update, read, or delete data from the database, we have to define the relationship. We can explain the structure of the database in entity-relationship diagrams or ERD. This diagram is typical and widely used.

Choosing the right analytical tool can be a hassle, especially if you do not have a conceptual foundation. With that in mind, let us look at some of Excel and SQL similarities and differentiations, but first, let us discover what Excel and SQL mean first.

Definition of Excel and SQL databases 🕵️‍♂️

Definition of Excel

Excel is a program written for Microsoft windows. It is integrated with functions as of: data manipulation, statistics, and data organization, making it easy to collect data to set up calculations that are more effective. You are fundamentally able to create a connection to a relational or transactional database system. This allows you to get optimal value for data by leveraging existing data that is already in the system. You can then use the various features of Excel to help refine and cleanse that data 🧹 (i.e., blending data, creating new calculated fields, etc.…).

This program will keep you safe and secure, which only you will have access to. It is accessed through an admin panel and can be viewed by any user… The Excel file is controlled by the admin and all users’ changes are tracked.

Definition of SQL

SQL is a programming language that is used to communicate 💬 with database programs. It was developed in the early 1960s to improve the accessibility of data stored in computer systems from mainframes to other computers and users connected to them. It’s important to understand that SQL is not a programming language. It is a way of communicating with databases using one of the six database management systems (DBMS) in all programming languages, including English.

SQL stands for Structured Query Language.

Wherever your data is one step additional to a rhetorical masterpiece 👩‍🎨. You write and send queries in SQL to the info that receives these queries, so it offers you what you request or makes changes. The information is held on during a database and arranged by rows and columns. The beauty of querying is additional cooperative and traceable. These queries have a viewer history, so you’re ready to see the changes made. Users may save and share helpful data.

SQL syntax 📝

The select clause

Select [e-mail address], company

This is the select clause. The select clause includes an operator being (select) accompanied through identifiers ([email address] and company).

An identifier contains spaces and special characters (for example, “e-mail address”), it must be enclosed in square brackets or it won’t be properly read.

A select clause need not necessarily display and indicate the tables that contain the fields and cannot specify conditions to be met by the data to be included.

The select clause always appears in front of the from clause in a select statement.

The from clause

From contacts

This is the from clause. It includes an operator (from) followed through an identifier (contacts).

A from clause does not list the fields to select.

The where clause

Where city = “Paris” 🧀

This is the where clause. It includes an operator (where) accompanied by an [removed]city = “Paris”).

Note: in contrast to select and from clauses, the where clause isn’t always a required part of a select statement.

You can perform the various actions SQL you could carry out the usage of pick, from, where clause.

Here is an example of SQL syntax:

You can recognize this SQL syntax without even knowing the language 👌

⦁ Select all columns
⦁ From a table (data source)
⦁ When a column is the same to "a few value".

It is the same as including a filter in Excel.

SQL is the language that interacts with databases. It is used to create, retrieve, update, delete data from the database and normally use the pick-out assertion to request data from a table. From and in which clauses are used to choose which table(s) to query and which rows of facts should be returned from the table. Users also can specify computations in order to be carried out to the data before it’s far displayed. In this sense, SQL can act like a spreadsheet application. However, it lacks the advanced formatting equipment and formulation observed in Excel or Google sheet.

We all recognize how essential connectivity withinside the electronic world is. 🌐 In every stroll of life, whether or not you’re a business enterprise or a person, you want to keep database statistics to deal with online transactions and make them simpler to manage. SQL makes this stuff simpler for the user and therefore is famous amongst software program developers, managers, data analysts, 👩‍💻 and other individuals who run their personal businesses.

How can SQL help data analyst?

You can use SQL to help you with the following work:

⦁        Creating databases and tables.

⦁        Adding data to a table.

⦁        Selecting data.

⦁        Editing data.

⦁        Deleting data.

⦁        Sorting data.

⦁        Finding unique values.

⦁        Combining data from two or more tables.

What are the limits of Excel? 🌌

If you’ve ever used a spreadsheet program, you know that:

⦁        It’s easy to accidentally change something that will mess up your data.

⦁        It is difficult to replicate an old analysis on new data.

⦁        It’s tedious to work when the data set contains a few hundred thousand rows.

⦁        It is difficult to share important spreadsheets with other people

The differences between SQL and Excel

The readability 👀

The emergence of SQL made humans write queries or command simpler. We can extract data that we only need, not all from the table.

It may be summarized that despite the fact that Excel offers greater flexibility and simplicity, it has more complicated steps users ought to work on. Moreover, if there’s new data, commands will need to be repeated once more, so that error can easily occur. Last but not least, provided we want to collaborate with our peers, we must upload commentary inside a worksheet or notes permitting our teammates to apprehend the context better.

Whereas some people simply code in some lines, then they get a similar result. Furthermore, they could reuse their code and do not have to alternate anything, and the code is much simpler to apprehend. This is because its syntax is pretty much like the English language.

Performance and pace 🏃‍♀️

Excel has boundaries: the more data and the more complexity of the functions which Excel users use, the more serious overall performance and pace they experience. Also, Excel can run the program at most 1 million rows.

Compared to SQL, you may create a billion rows, and its overall performance will continue to be a whole lot faster. In a few cases. With SQL, the query may be executed in more than one minute, whereas it takes an hour in Excel.

Collaboration 🤝

You can consider if the amount of facts continues growing, the size of Excel documents will become more extensive as well. It can be dozens of MB. Unfortunately, it seems sluggish and difficult, specifically while importing and sending those files for your peers. Apart from this, there is concern about naming convention and version control. I consider most Excel users frequently get the trouble is I’ve proven below.”

Nonetheless, this form of the problem may be mitigated in case you alternate to the usage of Google spreadsheets.

Turning into SQL, it stores only a bit of textual content query (small length of code or instruction), now no longer preserving a massive length of facts in a document like Excel. As a result, all of us in the team can access the same database. Now with SQL, absolutely each person can run their very own queries, which does not affect anyone. Meaning that version control 🛂 is now no longer necessary.

Plus, understanding SQL makes us speak the same language because of the IT specialists who contend with the database roles, following easy collaboration.

Learning curve 🎓

Since Excel is a Microsoft Office product, it gives an excellent and easy-to-use UI (user interaction). Although humans who’ve by no means programmed before, they could easily use it. They simply try clicking and usage of keyboard shortcuts, and they could grasp Excel in a brief time.

SQL isn’t always so tricky, too. However, the maximum essential element is to apprehend the kind of facts and their relationship. If you want to dig deep into SQL, my recommendation is to study as follows:

1- Basic SQL command

2- SQL join

3- SQL aggregate function

4- SQL sub queries

5- Data cleaning with SQL

6- SQL window function

7- SQL performance tuning

Adoption with other tools 🧰

Data visualization

In Excel, data visualization could be a piece of cake 🍰 because Microsoft has already exceptionally prepared integral features. Excel users can effectively produce numerous graphs like line charts, bar charts, histograms,  even time series. We can then export these graphs to alternative Microsoft Office programs, particularly PowerPoint and Word.

SQL is simply a query programming language. Therefore data visualization is often done in alternative ways; for example, most SQL users integrate with external libraries, namely Matplotlib in Python, D3.js in JavaScript, Processing.js in JavaScript, etc. And these tools are open source and free.

One of the well-known data-driven libraries is D3.js. It permits people to style unlimited data visualization documents. Its results are even more stunning and versatile compared to Excel ones. Several developers use it to design and build new styles of charts such as zoomable geography graphs 🗺️, sequence sunburst graphs, etc. However, within the accurate word, a simple traditional graph like line charts or histogram, which can be created merely by Excel, is enough for showing insight into the data. Therefore, D3.js is taken into account to be excessive, and it’s appropriate for only extraordinarily complicated and specific data sets. Moreover, its learning curve is sort of high. In addition to information skills, knowledge of the JavaScript language and front-end web development fundamentals are needed for mastering the library.

Read more about data visualization here.

Connection with database 📎

For Excel users, the perfect way to get entry to the database is through Microsoft Access. It not only helps us create relational databases, but it additionally affords integrated SQL query features, in addition to permitting the ability to apply the end result from those queries to be constantly carried out subsequently in Excel. Still, it isn’t free, and it can be bought from the Microsoft 365 package.

At the same time, if we use SQL, we’ve got extra alternatives while considering having access to a traditional database. The most known gear is access, MySQL, PostgreSQL, etc.

Extended to new features including gadget mastering.

If the Excel users need to apply a few not-built-in features, they have to shop for add-ons 🛒 from Microsoft. Besides, its machine mastering feature has a few limitations, so I no longer advocate Excel for this work.

Similarly, SQL isn’t a language for machine mastering, for the reason that top languages to develop machine mastering are r and python. SQL opens many wonders to machine mastering, consisting of- predictive modeling, data science, and AI development.

One of the top machine mastering frameworks is tensor flow, supported through Google. It permits us to write with python and JavaScript.

From a learning curve perspective, python and JavaScript aren’t so strict by themselves. The most challenging component is the idea of gadget mastering, and it calls for a robust basis of mathematics.

So, if you have any trouble, and you may break that problem down right into a structured and organized question, then the IFC converter feature available on SeveUp App can help you discover the solution to your question, as it works with the SQL layout that makes it feasible to transform a .ifc files to. SQL files to create an object-relational dashboard.

Why transfer from Excel to SQL? ➡️

If you save data in Excel files and are having trouble getting into and querying your data, then switching to a relational database can be really well worth considering.

Suppose you need to look at literary production in the French language. You create an Excel file with a title column to go into the title of the works.

But how can you control the seizure of authors?

If a piece has multiple authors, will you create as many columns as authors or input the authors in the identical cell in the author’s column?

In this case, a way to sort the works with the aid of using a writer?

How to show all results written by the same author?

And in case you need to understand as an instance all of the works written with the aid of using authors born in Paris 🥐, a way to input the metropolis and the year of birth of the authors?

You can choose to create a brand new worksheet to enter only author data, assign more than a few to identify the authors, and input only the author ID in the worksheet. But in case you delete an author, how do you automatically replace the works assigned to them?

To remedy this kind of trouble, the answer is relational databases.

Relational databases store data in tables or relationships. As a result, the time period is relational to refer to this type of database. The contents of a table may be displayed as a table, with rows and columns. However, a database table has nothing to do with an Excel spreadsheet.

Then if you switch from Excel to SQL, all the issues I stated in advance might be history 📜 in addition to providing you with some benefits:

SQL is quicker than Excel. What takes a few hours in Excel may be finished in a few minutes in SQL.

SQL separates evaluation from data. When you operate SQL, the data you examine is saved separately. Concretely, because of this, you may send your colleagues a small code report to access your evaluation. They can rerun the evaluation without destroying your data, and all of your code is reusable.

Relational database control systems

To create and control relational databases, you want a relational DBMS, a relational database control system. The most common relational DBMS are MySQL, SQLite, and PostgreSQL.

You have probably by no means heard of that DBMS; however, while you check your email, while you update your blog in WordPress, whilst you operate Dropbox, iTunes, or Skype. At the same time, you seek advice from your favorite newspaper online 📰, and you’re using it without it. Know DBMS.

Conclusion

SQL is a crucial factor of data evaluation as it gives more powerful, effective analytics that might not be available using Excel. It could take hours of work in Excel ⌚ and a long way greater in-depth; however, SQL can assist find what you’re looking for almost instantaneously.

SQL is sort of a spreadsheet on steroids 🦍. We work greater efficiently and creatively with data. It is how speedy we can run it…and… The truth is that servers are not involved.

By now, I hope that you are pretty determined to work with SQL databases! 😎

Sources:

https://docs.microsoft.com/en-us/dax/dax-overview

https://data2ml.com/2021/11/05/data-science-tools/

https://dzone.com/articles/why-use-sql-over-excel

https://geekactuary.com/en/excel-vs-sql-en/

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