Sql vs Excel

Google Sheets and Excel are the widely 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.

Google Sheets and Excel are the widely 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 allows you to connect an outermost data source, table of contents, as well as imported data all without the unnecessary hassle of inputting data in manually.

Nevertheless, before writing SQL language in order to create, read, update or delete data from the database (aka crud), we have to define the relationship. We can explain the structure of the database in an entity-relationship Diagram or ERD. This Diagram is standard and widely used.

Sql vs Excel: definition of Excel & SQL databases

Excel

Excel is a program written for Microsoft windows. It allows for data manipulation, statistics, and data organization.it makes it easy to collect data and set up meaningful calculations. 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.…).

It will keep your data safe and secure and only you will have access to it. 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.

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. Your data is one step further away in this case. You write and send queries in SQL to the database which receives these queries and then gives you what you request or makes changes. The data is stored in a database and organized by tables. The beauty of querying is more collaborative and traceable. These queries can be traced back to see who made what changes to which table. Users can also save and share useful data.

SQL syntax

The select clause

Select [e-mail address], company

This is the select clause. It consists of an operator (select) followed by two identifiers ([email address] and company).

If an identifier contains spaces or special characters (for example, “e-mail address”), it must be enclosed in square brackets.

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 them from the clause in a select statement.

The from clause

From contacts

This is the from clause. It consists of an operator (from) followed by an identifier (contacts).

A from clause does not list the fields to select.

The where clause

Where city = “seattle”

This is the where clause. It consists of an operator (where) followed by an expression (city = “Seattle”).

Note: unlike select and from clauses, the where clause is not a required part of a select statement.

You can perform many of the actions SQL you can perform using the select, from, where clause. More information on how you use these clauses can be found in the following additional articles:

• SQL access: select clause
• Access SQL: from clause
• Access SQL: where clause

Here is an example of SQL syntax.

You can understand this SQL syntax without even knowing the language :

SELECT *
 FROM table_name
WHERE column_name = 'some value';

• Select all columns
• From a table (data source)
• When a column is equal to “some value”.

It is the same as adding 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 typically uses the select statement to request data from a table. From and where clauses are used to select which table(s) to query, and which rows of data should be returned from the table. Users can also specify computations that will be applied to the data before it is displayed. In this sense, SQL can act like a spreadsheet application.

However, it lacks the advanced formatting tools and formulas found in Excel or google sheet we all know how important connectivity in the electronic world is. In every walk of life, whether you are an organization or a person, you need to maintain database records to handle online transactions and make them easier to manage. SQL makes these things easier for the user and hence is popular among software developers, managers, data analysts, and other individuals who run their own business.

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.

Sql vs Excel: 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 is tedious to work when the data set contains a few hundred thousand rows.
• and, it is difficult to share important spreadsheets with other people

The differences between SQL and Excel

The readability

The emergence of SQL made humans can write queries or instructions easier. We can extract data that we only want not all from the table.

The main difference between SQL and Excel databases is that Excel gives more flexibility and simplicity, it has more complex steps users have to work on. Moreover, if there is new data, we have to repeat the same instruction again, so that error can easily occur. Last but not least, provided we need to collaborate with our teammates, we have to add comments in a worksheet in order to make our teammates understand what we do. All I mentioned is quite a waste of time.

Whereas SQL users just code in a few lines, then they get the same result. Furthermore, they can reuse their code and do not have to change anything, and the code is much easier to understand. This is because its syntax is quite similar to the English language.

Performance and speed

Excel has a limitation which is that the more amount of data and the more complexity of the functions which Excel users use, the worse performance and speed they experience. Also, Excel can run the program at most 1 million rows.

Compared to SQL, it can be up to a billion rows and its performance is much faster. In some cases. With SQL, the query can be executed within a couple of minutes, whereas it takes an hour in Excel.

Collaboration

You can imagine if the amount of data continues growing, the size of Excel files becomes larger as well. It may be dozens of Mb. It certainly appears slow and difficult, especially when uploading and sending these files to teammates. Apart from this, there is concern about naming convention and version control. I believe most Excel users often get the problem I have shown below:

Nonetheless, this kind of problem could be mitigated if you change to use google spreadsheets.

Turning into SQL, it stores only a piece of text query (small size of code or instruction ), not keeping a large size of data in a file like Excel. As a result, everyone in the team can access the same database. Now, everybody can run their own queries, and they do not affect one another. This means that version control is no longer necessary.

Plus, knowing SQL makes us speak the same language it professionals who take care of the database roles, following smoothly collaboration.

Learning curve

Since Excel is a Microsoft Office product, it provides a good and easy-to-use UI (user interaction). Although people who never programmed before, they can easily use it. They just try clicking and using keyboard shortcuts, and they can master Excel in a short time.

SQL is not so hard too, but the most essential part is to understand the type of data and its relationship. If you want to dig deep into SQL, my recommendation is to study as follows:

• Basic SQL command
• SQL join
• SQL aggregate function
• Data cleaning with SQL
• SQL  subqueries
• SQL window function
• and, SQL  performance tuning

Adoption with other tools

Data visualization

In Excel, data visualization is a piece of cake, because Microsoft has already especially prepared a built-in feature. Excel users can quickly and effectively create various graphs such as line charts, bar charts, histograms, or even time series. We can then export these graphs to other Microsoft Office programs, namely PowerPoint and word.

SQL is just a query programming language, so data visualization can be done in different 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 helps us to unlimitedly design data visualization documents. Its results are even more beautiful and flexible compared to Excel ones. Many developers use it to design and create new kinds of charts such as zoomable geography graphs, sequence sunburst graphs, etc. However, in the real world, just a normal graph like a line chart or a histogram, which can be easily created by Excel, is enough for showing insight into the data. Therefore, d3.js is considered to be overkill, and it is suitable for only highly complex and specific data set. Moreover, its learning curve is quite high. In addition to data skills, knowledge of the javascript language and front-end web development fundamentals are required for mastering the library.

Read more about data visualization here.

Connection with database

Again, for Excel users, the easiest way to access the database is via Microsoft access. It does not only help us to easily create a relational database and provide built-in SQL query features, but it also allows the ability to use the result from those queries to be continuously applied next in Excel. Still, it is not free, and it can be bought from Microsoft 365 package.

At the same time, if we use SQL, we have more options when we consider accessing the traditional database. The famous tools are access, MySQL, PostgreSQL, etc.

Extended to new feature such as machine learning

If the Excel users want to use some not-built-in features, they have to buy add-ons from Microsoft. Besides, its machine learning feature has some limitations, so I do not suggest Excel for this work.

Similarly, SQL is not a language for machine learning, since the top languages to develop machine learning are R and Python. Yet, SQL opens a door to machine learning, predictive modeling, data science, and ai development by reason of its nature of compatibility with other famous open source libraries.

One of the top machine learning frameworks is TensorFlow, backed by google. It allows us to write with both Python and javascript.

From a learning curve perspective, Python and javascript are not so hard by themselves. The most difficult is the concept of machine learning, and it requires a strong knowledge of mathematics.

Using Excel alone, the workflow of creating a database is time-consuming and inefficient. With SQL, however, working with databases is simple and efficient.

SQL is one of the most powerful and prevalent software languages, it is a powerful tool that can be used to manipulate data and produce reports for an entire organization. From simple data joins to complex groupings, it can do almost anything that Excel, access, or tableau can. It is a universal and versatile programming language. It is used to create reliable, fast, and accurate relational databases.

So, if you have a problem, and you can break that problem down into a structured and organized question, then the IFC converter feature available on SeveUp App can help you find the answer to your question because it works with the SQL format that makes it possible to transform a .ifc files to .sql files to create an object-relational dashboard.

Why switch from Excel to SQL?

If you store data in Excel files and are having difficulty entering and querying your data, then switching to a relational database may be worth considering.

Suppose you want to study literary production in the Arabic language. You create an Excel file with a title column to enter the title of the works. But how are you going to manage the seizure of authors?

If a work has multiple authors, will you create as many columns as there are authors or enter the authors in the same cell in the author’s column? In this case, how to sort the works by the author? How to display all works written by the same author? And if you want to know for example all the works written by authors born in Egypt, how to enter the city and the year of birth of the authors? You can choose to create a new worksheet to enter only author data, assign a number to identify the authors, and enter only the author id in the worksheet. But if you delete an author, how do you automatically update the works assigned to them?

To solve this type of difficulty, the solution is relational databases.

Relational databases store data in tables or relationships, hence the term relational to refer to this type of database. The contents of a table can be displayed as a table, with rows and columns, but a database table has nothing to do with an Excel spreadsheet.

Then if you switch from Excel to SQL, all of the problems I mentioned earlier will be history in addition to giving you a few benefits:

SQL is faster than Excel. What takes a few hours in Excel can be completed in a few minutes in SQL.

SQL separates analysis from data. When you use SQL, the analyzed data is stored separately. Concretely, this means that you can send your colleagues a small code file to access your analysis. They can rerun the analysis without destroying your data, and all of your code is reusable.

Relational database management systems

To create and manage relational databases, you need a relational DBMS, a relational database management system. The most common relational DBMS are MySQL, SQLite, and PostgreSQL.

You have probably never heard of DBMS, but when you check your email, when you update your blog in WordPress, when you use dropbox, Itunes, or Skype, when you consult your favorite newspaper online, you are using without it.

The main advantage of relational databases is SQL, an acronym for a structured query language, a query language that allows you to find, add, modify, or delete data in relational databases.

Sql vs Excel: conclusion

SQL is an important aspect of data analysis because it provides more powerful analytics that may not be obtainable using Excel. It could take hours of work in Excel and far more in-depth, but SQL can help find what you are looking for almost instantaneously.

SQL is like a spreadsheet on steroids. We work more efficiently and creatively with data. It is how fast we can run it…and… The fact that servers are not involved.

By now, I hope that you are quite determined to learn SQL!