SQL queries

As the amount of data being generated and stored by companies and organizations continue to grow, the ability to effectively manage and manipulate this data using databases has become increasingly important for developers. Among the plethora of programming languages, we have SQL. Also known as Structured Query Language, SQL is a programming language widely used for managing data stored in relational databases.

SQL commands enable developers to perform a wide range of tasks such as creating tables, inserting, modifying data, retrieving data, searching databases, and much more. In this guide, we will highlight the top basic SQL commands that every developer should be familiar with. 

What is SQL?

For the unversed, the programming language SQL is primarily used to manage and manipulate data in relational databases. Relational databases are a type of database that organizes data into tables with rows and columns, like a spreadsheet. SQL is used to create, modify, and query these tables and the data stored in them. 

With SQL commands, developers can create tables and other database objects, insert and update data, delete data, and retrieve data from the database using SELECT statements. Developers can also use SQL to create, modify and manage indexes, which are used to improve the performance of database queries.

The language is used by many popular relational database management systems such as MySQL, PostgreSQL, and Microsoft SQL Server. While the syntax of SQL commands may vary slightly between different database management systems, the basic concepts are consistent across most implementations. 

Types of SQL Commands 

There are several types of SQL commands that are commonly used in relational databases, each with a specific purpose and function. Some of the most used SQL commands include: 

1. Data Definition Language (DDL) commands: These commands are used to define the structure of a database, including tables, columns, and constraints. Examples of DDL commands include CREATE, ALTER, and DROP.
2. Data Manipulation Language (DML) commands: These commands are used to manipulate data within a database. Examples of DML commands include SELECT, INSERT, UPDATE, and DELETE.
3. Data Control Language (DCL) commands: These commands are used to control access to the database. Examples of DCL commands include GRANT and REVOKE.
4. Transaction Control Language (TCL) commands: These commands are used to control transactions in the database. Examples of TCL commands include COMMIT and ROLLBACK.

Essential SQL commands

There are several essential SQL commands that you should know in order to work effectively with databases. Here are some of the most important SQL commands to learn:

CREATE 

The CREATE statement is used to create a new table, view, or another database object. The basic syntax of a CREATE TABLE statement is as follows: 

The statement starts with the keyword CREATE, followed by the type of object you want to create (in this case, TABLE), and the name of the new object you’re creating (in place of “table_name”). Then you specify the columns of the table and their data types.

For example, if you wanted to create a table called “customers” with columns for ID, first name, last name, and email address, the CREATE TABLE statement might look like this:

This statement would create a table called “customers” with columns for ID, first name, last name, and email address, with their respective data types specified. The ID column is also set as the primary key for the table.

SELECT  

Used on one of multiple tables, the SELECT statement Is used to retrieve data. The basic syntax of a SELECT statement is as follows: 

The SELECT statement starts with the keyword SELECT, followed by a list of the columns you want to retrieve. You then specify the table or tables from which you want to retrieve the data, using the FROM clause. You can also use the JOIN clause to combine data from two or more tables based on a related column.

You can use the WHERE clause to filter the results of a query based on one or more conditions. Programmers can also use GROUP BY to manage the results by one or multiple columns. The HAVING clause is used to filter the groups based on a condition while the ORDER BY clause can be used to sort the results by one or more columns.  

INSERT 

INSERT is used to add new data to a table in a database. The basic syntax of an INSERT statement is as follows: 

INSERT is used to add data to a specific table and begins with the keywords INSERT INTO, followed by the name of the table where the data will be inserted. You then specify the names of the columns in which you want to insert the data, enclosed in parentheses. You then specify the values you want to insert, enclosed in parentheses, and separated by commas. 

UPDATE 

Another common SQL command is the UPDATE statement. It is used to modify existing data in a table in a database. The basic syntax of an UPDATE statement is as follows: 

The UPDATE statement starts with the keyword UPDATE, followed by the name of the table you want to update. You then specify the new values for one or more columns using the SET clause and use the WHERE clause to specify which rows to update. 

DELETE 

Next up, we have another SQL command DELETE which is used to delete data from a table in a database. The basic syntax of a DELETE statement is as follows: 

In the above-mentioned code snippet, the statement begins with the keyword DELETE FROM. Then, we add the table name from which data must be deleted. You then use the WHERE clause to specify which rows to delete. 

ALTER  

The ALTER command in SQL is used to modify an existing table, database, or other database objects. It can be used to add, modify, or delete columns, constraints, or indexes from a table, or to change the name or other properties of a table, database, or another object. Here is an example of using the ALTER command to add a new column to a table called “tablename1”: 

In this example, the ALTER TABLE command is used to modify the “users” table. The ADD keyword is used to indicate that a new column is being added, and the column is called “email” and has a data type of VARCHAR with a maximum length of 50 characters. 

DROP  

The DROP command in SQL is used to delete a table, database, or other database objects. When a table, database, or other object is dropped, all the data and structure associated with it is permanently removed and cannot be recovered. So, it is important to be careful when using this command. Here is an example of using the DROP command to delete a table called ” tablename1″: 

In this example, the DROP TABLE command is used to delete the ” tablename1″ table from the database. Once the table is dropped, all the data and structure associated with it are permanently removed and cannot be recovered. It is also possible to use the DROP command to delete a database, an index, a view, a trigger, a constraint, and a sequence using a similar syntax as above by replacing the table with the corresponding keyword. 

TRUNCATE  

The SQL TRUNCATE command is used to delete all the data from a table. Simultaneously, this command also resets the auto-incrementing counter. Since it is a DDL operation, it is much faster than DELETE and does not generate undo logs, and does not fire any triggers associated with the table. Here is an example of using the TRUNCATE command to delete all data from a table called “customers”: 

In this example, the TRUNCATE TABLE command is used to delete all data from the “customers” table. Once the command is executed, the table will be empty, and the auto-incrementing counter will be reset. It is important to note that the TRUNCATE statement is not a substitute for the DELETE statement, TRUNCATE can only be used on tables and not on views or other database objects. 

INDEX  

The SQL INDEX command is used to create or drop indexes on one or more columns of a table. An index is a data structure that improves the speed of data retrieval operations on a table at the cost of slower data modification operations. Here is an example of using the CREATE INDEX command to create a new index on a table called ” tablename1″ on the column “first_name”: 

In this example, the CREATE INDEX command is used to create a new index called “idx_first_name” on the column “first_name” of the ” tablename1″ table. This index will improve the performance of queries that filter, or sort data based on the “first_name” column. 

JOIN  

Finally, we have a JOIN command that is primarily used to combine rows from two or more tables based on a related column between them.  It allows you to query data from multiple tables as if they were a single table. It is used for retrieving data that is spread across multiple tables, or for creating more complex reports and analyses.  

INNER JOIN – By implementing INNER JOIN, the database only returns/displays the rows that have matching values in both tables. For example, 

LEFT JOIN – LEFT JOIN command returns all rows from the left table. It also returns possible matching rows from the right table. If there is no match, NULL values will be returned for the right table’s columns. For example, 

RIGHT JOIN – In the RIGHT JOIN, the database returns all rows from the right table and possible matching rows from the left table. In case there is no match, NULL values will be returned for the left table’s columns. 

FULL OUTER JOIN – This type of JOIN returns all rows from both tables and any matching rows from both tables. If there is no match, NULL values will be returned for the non-matching columns. 

CROSS JOIN – This type of JOIN returns the Cartesian product of both tables, meaning it returns all combinations of rows from both tables. This can be useful for creating a matrix of data but can be slow and resource-intensive with large tables. 

Furthermore, it is also possible to use JOINs with subqueries and add ON or USING clauses to specify the columns that one wants to join.

Bottom line 

In conclusion, SQL is a powerful tool for managing and retrieving data in a relational database. The commands covered in this blog, SELECT, INSERT, UPDATE, and DELETE, are some of the most used in SQL commands and provide the foundation for performing a wide range of operations on a database. Understanding these commands is essential for anyone working with SQL commands and relational databases.

With practice and experience, you will become more proficient in using these commands and be able to create more complex queries to meet your specific needs. 

 

 

Data democratization is a complex concept. The concept, in any organization, rests on four major pillars: data, tools, training, and people.

Data democratization allows end-users to assess data in a digital format without requiring help (typically from IT).  The culture of a company and how its employees think are driven by people who are quite passionate about data.

Today, Data democratization can be a game-changer because it makes it easier, faster, and simpler for employees to access the insights they require. Data democratization safeguards the company from becoming a top-down organization where the highest-paid person’s opinion wins. Users are given more ownership and greater responsibility with data democratization and need no longer be driven by hunches or assumptions. Let us see how this happens.

Pillars of data democratization

1. Data

A considerable percentage of data exists in silos and is spread across the enterprise.  It could be stored in flat files accessed by Microsoft SQL Server; it could be saved in folders on an employee’s hard drives, or it could be stored at (and shared by) partner companies. As you’d expect, this is not conducive to viewing the “big picture.” Enterprises have created cloud-based data warehouses to tear down the silos. For data analytics, warehouses serve as a solitary, consolidated source of truth.

2. Tools and training for data democratization

Data democratization can be empowering to users, but only if the data is properly used.  To make sure data democratization doesn’t lead to misinterpreting data.  After training (typically by IT), users may reinforce that training by creating or joining mailing lists or chat rooms; they may even ensure that beginners and experts physically sit next to each other.

As companies identify which business users need to explore the data more deeply and freely on their own, they must also understand the different levels of user needs when it comes to data. Instead of limiting the analytics by offering just summarized or just raw data to all users, a multi-tiered approach is essential to provide the right depth of data to a user’s analytical skills and needs.

A first tier might provide only dashboards and static reports, and the second tier might add interactive, dynamic dashboards where the users can drill down to additional insights.

The third tier could include guided analysis that a senior analyst prepares for an individual user or a group of business users to work in a safe and rich environment in which technical users can follow the analysis process through annotations and explanations.

The fourth and final tier provides access to a visual data discovery tool so business users can visually explore a broad set of data (perhaps through a simple, familiar tool such as Excel) instead of using less intuitive means such as data tables and SQL queries. An enterprise will need to ensure that the more data a user can access, the greater their understanding of that data must be to avoid data misuse or misunderstanding.

3. People

Expertise in data analytics is strongly associated with open, persistent, positive, and inquisitive people. Enterprises must ensure that such enquiring minds are regularly challenged and involved.  Employees need to be motivated and engaged to think, play with data, and ask questions. Engage them with regular seminars on key concepts, tools, and modern technologies.

4. Challenges faced while implementing data democratization

The main challenge enterprises face in their move to data democratization is that data teams are struggling to keep up with the rising hunger for data throughout the organization.  More data demands a more complex analysis. For many organizations, moving to data democratization may require more resources than they have.

This problem can be addressed by self-service analytics. It enables everyone in the company to become a data analyst.  In many cases, users need only a dashboard that provides real-time data; others need that data available in analysis tools. Putting the technology in place is not just enough to make it work. The training that is essential for the staff must be offered to bring real value. Thus, data democratization is made highly efficient by self-service analytics.

Conclusion

To enhance data democratization in your enterprise, you must keep in mind that this is a slow process in which small wins are brought via incremental transformations in a culture that drives the next culture change. Today, more organizations are trying to provide access to data to all their staff via data democratization and this, in turn, is helping them enhance the job performance and overall health of the organization.

Learn more about data science at our Data Science Bootcamp.