SQL for Data Engineering: Success Blueprint for Data Engineers

Understand Why You Must Learn SQL For Data Engineering To Excel In the Big Data Industry | ProjectPro.

SQL for Data Engineering: Success Blueprint for Data Engineers
 |  BY Daivi

The demand for skilled data engineers who can build, maintain, and optimize large data infrastructures does not seem to slow down any sooner. At the heart of these data engineering skills lies SQL that helps data engineers manage and manipulate large amounts of data. Did you know SQL is the top skill listed in 73.4% of data engineer job postings on Indeed? If you are still wondering whether or why you need to master SQL for data engineering, read this blog to take a deep dive into the world of SQL for data engineering and how it can take your data engineering skills to the next level. 


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Almost all major tech organizations use SQL. Even Fortune 500 businesses (Facebook, Google, and Amazon) that have created their own high-performance database systems also typically use SQL to query data and conduct analytics. You will discover that more employers seek SQL than any machine learning skills, such as R or Python programming skills, on job portals like LinkedIn. According to the 2022 developer survey by Stack Overflow, Python is surpassed by SQL in popularity. In fact, approximately 70% of professional developers who work with data (e.g., data engineer, data scientist, data analyst, etc.) use SQL, compared to 61.7% who use Python, making it the third most popular programming language altogether. According to the 8,786 data professionals participating in Stack Overflow's survey, SQL is the most commonly-used language in data science. Despite the buzz surrounding NoSQL, Hadoop, and other big data technologies, SQL remains the most dominant language for data operations among all tech companies. Now, let us take a deep dive into why one should learn SQL engineering.

Why Learn SQL for Data Engineering?

  • Learning SQL can help data engineers work more effectively with data analysts and data scientists as they share a common language for querying and analysing data.

  • SQL skills are highly transferable as they can be applied to wide range of databases and data management tools, making it a valuable data engineering skill for people working in a variety of industries and business contexts.

  • Knowing SQL helps data engineers optimize data infrastructures for better performance and efficiency and also develop more effective data models and data warehousing solutions.

Data integration will become highly significant as the amount of data globally grows in volume, variety, and complexity. Businesses will be better able to make smart decisions and achieve a competitive advantage if they can successfully integrate data from various sources using SQL. Data can be extracted from different types of sources, transformed into a suitable format for analysis, and then loaded into a database or data warehouse using ETL and data pipelines. Data engineers are responsible for these data integration and ELT tasks, where the initial step requires extracting data from different types of databases/files, such as RDBMS, flat files, etc. And how is it done? By using SQL queries! 

Data engineers can extract data from a table in a relational database using SQL queries like the "SELECT" statement with the "FROM" and "WHERE" clauses. Engineers can also use the "LOAD DATA INFILE" command to extract data from flat files like CSV or TXT. Additionally, SQL enables data engineers to perform data transformation tasks like data cleaning or aggregation from various data sources and loading data into data warehouses or other storage systems using simple SQL queries. If your database is cloud-based, using SQL to clean data is far more effective than scripting languages. Moreover, SQL has several built-in advantages for enhanced data quality in data warehouses and databases. Data engineers can perform any quality checks using the DDL commands in SQL.

SQL plays a significant role in the entire data engineering project lifecycle, from data integration and extraction to data transformation and loading. This makes it a crucial skill for every data engineer if they want to build a successful career in the big data and data science industry.

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SQL for Data Engineers and Its Importance

Data engineers primarily need SQL for creating data integration scripts and executing analytical queries that transform and utilize data for business insights. Data engineers use SQL to modify any database and table structure and extract subsets of the data from the database for various business analytics use cases. For example, the data manipulation language (DML) and data definition language (DDL) allow engineers to collect and manipulate data scripts and design and modify data structures. Furthermore, creating reports from data analysis often involves repeating a process; stored procedures help data engineers overcome this challenge. In addition to processing one or more DML operations on a database, stored procedures can accept user input and execute SQL commands.

Your SQL skills as a data engineer are crucial for data modeling and analytics tasks. Making data accessible for querying is a common task for data engineers. Collecting the raw data, cleaning it, modeling it, and letting their end users access the clean data are all part of this process. Additionally, they must analyze the data, look for any data issues/anomalies, validate any assumptions, approximate the expansion of the data size, validate the business rules, look for missing/duplicate rows on joins, etc. They must load the raw data into a data warehouse for this analysis. There are numerous ways to import data into a data warehouse using SQL. For instance, data engineers can easily transfer the data onto a cloud storage system and load the raw data into their data warehouse using the COPY INTO command.

Data transformation is another crucial task for data engineers. This entails managing data access, restricting data movement inside the warehouse, and SQL query optimization strategies. But how does SQL play a vital role here? SQL enables engineers to perform data transformations within data warehouses, significantly accelerating data processing. Transformations can be broadly categorized as either narrow or wide. The majority of SQL queries combine wide and narrow transformations. For narrow transformations, SQL methods like Lower(), Concat(), etc., are applied directly to the data in memory. Joins and anti joins, string, numeric, and date functions, group by, aggregates, order by, union, having, Window functions, stored procedures, subqueries, etc., are some of the options for wide transformations that SQL offers.

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SQL for Data Engineering - The Basics

This section will explore some of the most important basic SQL queries that every data engineer must have some knowledge about.

SQL Datatypes and Operators

The first and most important step is to learn about SQL datatypes in general. SQL data types determine the type of value that can be placed in a table column. For instance, you can specify the data type of a column to be INT if you only want it to contain integer values.

The following categories can be used to classify SQL data types broadly.

  • Numeric data types (INT, TINYINT, BIGINT, FLOAT, REAL, etc.)

  • Date and Time data types (DATE, TIME, DATETIME, etc.)

  • Character and String data types (CHAR, VARCHAR, TEXT, etc.)

  • Unicode character string data types (NCHAR, NVARCHAR, NTEXT, etc.)

  • Binary data types (BINARY, VARBINARY, etc.)

  • Miscellaneous data types (CLOB, BLOB, XML, CURSOR, TABLE, etc.)

An SQL operator is a special word or character used to perform database operations. These tasks can range from complex comparisons to simple arithmetic calculations. There are six categories of SQL operators:  Arithmetic, Bitwise, Comparison, Compound, Logical, and String. 

  • Arithmetic Operators- Mathematical operations on numerical data, such as addition (+), subtraction (-), multiplication (*), division (/), and remainder/modulus (%), are performed using arithmetic operators.

  • Bitwise Operators- You can perform bit manipulation between two expressions of the integer data type using a bitwise operator. To turn the binary result back into an integer, bitwise operators first convert the integers into binary bits and then perform the AND (&), OR (|, ^), or NOT () operation on each individual bit.

  • Comparison Operators- When comparing two values, a comparison operator is used to determine whether they are the same. E.g., Equal to (=), Not equal to (!=), greater than (>), etc.

  • Compound Operators- Compound operators carry out an operation on a variable and then set the variable's result to the outcome of the operation. E.g., Add equals (+=), subtract equals (-=), etc.

  • Logical Operators- Logical operators have a true or false outcome, such as the AND operator, which returns true when both expression conditions are satisfied.

  • String Operators- The main purposes of string operators are string concatenation (adding multiple strings together) and string pattern matching.

Basic SQL Important Queries

Now, you can move on to the other basic SQL queries- Data Manipulation Language (DML), Data Definition Language (DDL), Data Control Language (DCL), and Transaction Control Language (TCL). You should familiarize yourself with standard SQL commands (SELECT, INSERT, DELETE, UPDATE, etc.) and commands for managing databases and tables (CREATE, ALTER, DROP, BACKUP, RESTORE, etc.). 

  • SELECT 

The SQL SELECT command or statement is used to get all the records from a database table and display them as a result set. It is generally considered a DQL command, although it can also be considered a DML command.

The following SQL syntax is used to write SELECT queries:

Image for SELECT Query

Example of SELECT query-

Let us take an example of two different tables- Customer table and Orders table.

Customers Table-

CustomerID

CustomerName

Contact

City

Country

1

William Baker

9923

London

UK

2

Daniel Stuart

3499

Berlin

Germany

3

Andrew James

2267

New York

USA

4

Dino Clark

3866

Rome

Italy

Orders Table-

OrderID

CustomerID

Quantity

101

1

11

102

3

22

103

4

33

104

7

44

Performing SELECT query on these tables-

Image for SELECT Query Example

Output:

CustomerName

City

William Baker

London

Daniel Stuart

Berlin

Andrew James

New York

Dino Clark

Rome

  • INSERT

Data records or rows are added in a database table using the INSERT SQL command. In an INSERT statement, the data value to be inserted and the column names for which the entry must be made are both specified.

The following syntax is used for writing INSERT statements in SQL:

Image for INSERT Query

Example of Insert Query-

We will take the same example tables from above.

Performing INSERT query on these tables-

Image for INSERT Query Example

Output-

CustomerID

CustomerName

Contact

City

Country

1

William Baker

9923

London

UK

2

Daniel Stuart

3499

Berlin

Germany

3

Andrew James

2267

New York

USA

4

Dino Clark

3866

Rome

Italy

5

Matt Gregory

2188

Vancouver

Canada

  • DELETE

One or more rows can be deleted from a database table using the DELETE statement in SQL. The data records are not permanently deleted. A DELETE command can always be reversed by using a rollback action. 

The following is the syntax for writing a DELETE statement:

Image for DELETE Query

Example of Delete Query-

We will take the same example tables from above.

Performing DELETE on these tables-

Image for DELETE Query Example

Output-

CustomerID

CustomerName

Contact

City

Country

1

William Baker

9923

London

UK

2

Daniel Stuart

3499

Berlin

Germany

3

Andrew James

2267

New York

USA

4

Dino Clark

3866

Rome

Italy

  • UPDATE

A database table's existing column values can be changed by using the UPDATE command or statement.

The following syntax must be used when writing an UPDATE statement:

Image for UPDATE Query

Example of Update Query-

We will take the same example tables from above.

Performing Update on these tables-

Image for UPDATE Query Example

Output-

CustomerID

CustomerName

Contact

City

Country

1

William Baker

9923

London

UK

2

Daniel Stuart

3499

Berlin

Germany

3

Andrew Finn

2267

Chicago

USA

4

Dino Clark

3866

Rome

Italy

 

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  • CREATE

This statement is used for creating tables/databases and all its objects, including the tables, functions, views, etc.

A CREATE statement should be written using the following syntax:

Image for CREATE Query

Example of Create Query-

We will take the same example from above.

Performing Create query for the Customers table-

Image for Create Query Example

Output-

CustomerID

CustomerName

Contact

City

Country

  • ALTER

This statement is used to change database structures and/or objects currently present in the database.

The following syntax can be used for adding a column in the existing table:

Image for ALTER Query

Example of Alter Query-

We will take the same examples from above.

Performing Alter query for these tables-

Image for Alter Query Example

Output-

CustomerID

CustomerName

Contact

City

Country

Email

  • DROP

This statement is used to delete a database object permanently.

The following syntax should be used when writing a DROP statement:

Image for DROP Query

Example of Drop Query-

We will use the same examples from above.

Performing Drop query for these tables-

Image for Drop Query Example

Output-

CustomerID

CustomerName

Contact

City

Country

CustomerID

SQL for Data Engineering - Advanced Concepts

Once you have a strong knowledge of SQL core concepts, you must also learn about advanced SQL queries. These will help you perform complex operations and database programming on large amounts of data stored in data warehouses or lakes. Let us look at the advanced SQL examples and queries every data engineer must know.

JOINS are one of the most useful and popular advanced SQL queries. Joins are SQL operations that combine rows from multiple tables based on a common column. They are often used when a user has to extract data from tables that contain one-to-many or many-to-many relationships between them. 

There are mainly four types of joins-

  1. INNER JOIN

When the join condition is satisfied, SQL INNER JOIN retrieves all rows from all tables. The inner join returns the records with matching values in both tables.

Image for INNER JOIN Query

Example of INNER JOIN:

Let us use the same examples from above.

Performing Inner Join on the two tables-

Image for Inner Join Example

Output:

OrderID

CustomerName

101

William Baker

102

Andrew James

103

Dino Clark

  1. FULL JOIN

When the join condition is not met, the SQL FULL JOIN, also known as the FULL OUTER JOIN, returns all rows from the LEFT-hand table and the RIGHT-hand table with NULL values in place. It returns every record that either matches a row in the left table or a row in the right table.

Image for FULL JOIN Query

Example of Full Join-

Let us work with the same example from above.

Performing Full Join on both tables-

Image for Full Join Example

Output-

CustomerName

OrderID

William Baker

101

Daniel Stuart

NULL

Andrew James

102

Dino Clark

103

NULL

104

  1. LEFT JOIN

The LEFT JOIN, also known as the LEFT OUTER JOIN in SQL, retrieves all rows from the LEFT-hand table specified in the ON condition and only rows from the second table where the joined columns are equal (join condition is met). The output or result set will also include NULL values for records without matching values in the right table.

Image for LEFT JOIN Query

Example of Left Join-

Let us work with the same example from above.

Performing Left Join on both tables-

Image for Left Join Example

Output-

CustomerName

OrderID

William Baker

101

Daniel Stuart

NULL

Andrew James

102

Dino Clark

103

 

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  1. RIGHT JOIN

The RIGHT JOIN, also known as the RIGHT OUTER JOIN, outputs all rows from the RIGHT-hand table given in the ON condition and just those rows from the other table whose joined columns are equal (join condition is met). The output or result set will also include NULL values for any rows for which there are no matching values in the left table.

Image for RIGHT JOIN Query

Example of Right Join-

Let us work with the same example from above.

Performing Right Join on both tables-

Image for RIght Join Example

Output-

OrderID

CustomerName

101

William Baker

102

Andrew James

103

Dino Clark

104

NULL

Subqueries are SQL statements that are nested inside of other statements. Multiple queries may be placed inside a subquery, one after the other. They allow you to choose specific rows at runtime that match specific conditions. They are also referred to as the inner query or inner select, while the query containing them is called the outer query or outer select.

Image for Subqueries

Example of Subqueries-

Let us work with the same example from above.

Performing Subqueries on both tables-

Image for Subqueries Example

Output-

CustomerID

CustomerName

1

William Baker

3

Andrew James

4

Dino Clark

A stored procedure is a named object that an application can call (or run in SQL Management Studio). When an external application has provided a parameter, stored procedures are generally used to carry out common INSERT, DELETE, and UPDATE operations on data. They typically include a variety of loops, variables, and calls to other stored procedures, tables, and views. The CREATE PROCEDURE command, followed by SQL commands, creates stored procedures.

Image for Stored Procs Query

Example of Stored Procs-

Let us work with the same example from above.

Performing Create Procedure on both tables-

Image for Create Procedure Example

Output-

CustomerID

CustomerName

1

William Baker

2

Daniel Stuart

3

Andrew James

4

Dino Clark

Views are virtual tables, meaning they don't exist in the database and don't need any storage. Virtual tables in a database also include rows and columns like actual tables do. Views are created by simply taking data (fields) from one or more database tables while applying certain conditions to the table's row selection. The CREATE VIEW statement is used mainly for creating views in SQL.

Image for VIEWS in SQL

Example of Views-

Let us work with the same example from above.

Performing View query on the tables-

Image for Create View Example

Output-

USA Customers

CustomerID

CustomerName

Contact

City

Country

3

Andrew Finn

2267

Chicago

USA

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A database table may contain a lot of columns, making it often challenging and time-consuming to retrieve the same type of data in these columns. The identical rows that are present in a table's columns are grouped using the GROUP BY statement. You can efficiently analyze the data by using the SQL aggregate methods COUNT(), MAX(), MIN(), SUM(), AVG(), etc., in conjunction with the GROUP BY statement.

Image for GROUP BY Query in SQL

Image for AGGREGATE Query in SQL

Example of Group By with Aggregate function (COUNT() function)-

Let us work with the same example from above.

Performing Group By query on the tables-

Image for Create View Example

Output-

CustomerID

Country

1

UK

1

Germany

1

USA

1

Italy

Window functions allow you to carry out an action on a set of rows and return a result for each row in the table. A group of table rows on which the function will execute is called the "window" in the window function. When you use a window function in SQL, all the rows are returned along with a column with a specific function applied to each row in the table. 

Image for WINDOW FUNCTION Query

Some common Window functions are RANK, DENSE_RANK, etc.

  1. RANK

This function assigns each record in the table a value, which may be the same for two or more records with identical values. If the records are identical, it also skips ranks.

Image for RANK FUNCTION Query

Example of Rank window function-

Let us work with the same example from above.

Performing Rank query on the tables-

Image for Rank function Example

Output-

CustomerID

CustomerName

Contact

CustomerRank

1

William Baker

9923

1

2

Daniel Stuart

3499

2

3

Andrew James

2267

3

4

Dino Clark

3866

4

 

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  1. DENSE_RANK

A rank is assigned to each row in a result set partition using the window function DENSE RANK(). The DENSE RANK() method, in contrast to RANK(), returns a series of rank values. If the values in two adjacent rows in a partition match, they are ranked equally.

Image for DENSE_RANK FUNCTION Query

Example of Dense_Rank window function-

Let us work with the same example from above.

Performing Dense_Rank query on the tables-

Image for Dense_Rank function Example

Output-

CustomerID

CustomerName

Contact

CustomerRank

4

Dino Clark

3866

1

3

Andrew James

2267

2

2

Daniel Stuart

3499

3

1

William Baker

9923

4

The relational operations Pivot and Unpivot in SQL are used to change one table into another to create a table with a more simple view. The pivot operator transforms the table's row data into its column data. The Unpivot operator performs the reverse operation, turning column-based data into rows.

Image for PIVOT Query

Example of Pivot function-

Let us work with the same example from above.

Performing Pivot query on the tables-

Image for Pivot function Example

Output-

CustomerName

William Baker

Daniel Stuart

Andrew James

Dino Clark

Contact

9923

3499

2267

3866

Image for UNPIVOT Query

Example of Unpivot query-

Let us work with the same example from above.

Performing Unpivot query on the tables-

Image for Unpivot function Example

Output-

CustomerName

Contact

William Baker

9923

Daniel Stuart

3499

Andrew James

2267

Dino Clark

3866

SQL for Data Engineering Pipelines

There are two major aspects of data engineering pipelines- ETL and Big Data. Let us understand how SQL works efficiently with ETL workflows and big data technologies.

ETL

Extract, Transform, and Load, or ETL, is a process that pulls data from multiple data sources, transforms it according to business requirements or demands, and then loads the transformed data into a target database. SQL has the ability to execute every step of ETL, and although other query languages are available, SQL is the most commonly used for ETL purposes. Adopting a hybrid approach by combining the best of both worlds – SQL and ETL is the need of the hour. The market's current ETL solutions come with a SQL action that users can employ to execute any SQL queries (DDLs, Stored Procedures, Index Creations, etc.). Data validations or data type checks can be performed using SQL, while duplicates, foreign key constraints, and NULL checks can all be identified using ETL solutions. Data processing tasks containing SQL-based data transformations can be conducted utilizing Hadoop or Spark executors by ETL solutions. It is possible to separate the ETL pipeline from the SQL scripts by moving the transformation logic to SQL. This also makes it simpler to test SQL scripts and maintainability.

SQL in Big Data

SQL is not just limited to data warehousing and traditional relational database management systems (RDBMS). To analyze big data and create data lakes and data warehouses, SQL-on-Hadoop engines run on top of distributed file systems. The SQL-on-Hadoop platform combines the Hadoop data architecture with traditional SQL-style structured data querying to create a specific analytical application tool. SQL is essential for effectively adopting loosely structured and structured data in Hadoop as it has become increasingly popular for enterprise data architecture.

Data engineers can extract data from the Hadoop system using Hive and Impala, which offer an SQL-like interface. They are built on top of Hadoop and can query data from underlying storage infrastructures. Working with data stored in a Hadoop cluster requires using SQL tools like Hive and Impala. Impala offers a SQL-like interface that lets you read and write Hive tables, allowing simple data exchange.  In addition, Impala speeds up and optimizes SQL operations on Hadoop, enabling the use of this DBMS in big data analytics. Impala uses Apache Hive infrastructure that has already been set up to run Apache Hive long-running SQL batch queries whenever possible. Additionally, Impala keeps its table definitions in a metastore, a standard MySQL or PostgreSQL database, which is the same location where Hive stores data that is identical to this. This enables Impala to access Hive tables so long as all columns use the available data types, file formats, and compression codecs for Impala.

SQL for Data Engineers - The Best Practices

You can significantly improve the efficiency of your SQL queries by regularly monitoring and updating your SQL database. To optimize SQL queries, there are a few best practices SQL data engineers can adhere to.

Before writing the query, you should clearly understand your requirements. This will help you get the required data, reduce runtime, and optimize SQL queries. Filtering your data will also minimize the size of your tables and help accelerate SQL queries. Using both clustered and non-clustered SQL Server indexes will improve SQL query performance and data retrieval.

You must isolate the SQL unit test from other dependencies if you want the unit test to be well-written and to prevent fragility. SQL unit test names must be self-descriptive to be easily understood. A SQL unit test that has been named properly is easier to read and makes maintenance easier. Additionally, avoid using multiple assertions because you won't know the outcomes of the subsequent test cases if the first test case fails.

Git allows you to manage file versions and track changes by integrating modern version control capabilities. This will automatically back up all data models and SQL queries for your projects. As a result, you can always access the most recent version of your SQL queries and data models. Git-based version control is time-efficient because data engineers can reuse SQL code, review query history, and update files for SQL queries. 

By matching the criteria of the WHERE clause, indexes in SQL help you find a record or a set of records. Using indexes can help queries in their search result for a particular value or a range of values. By speeding up the search process, query performance is ultimately improved. Datasets can be sorted using indexes so that the database can identify the index and skip sorting when running the query. Additionally, indexing speeds up the grouping of records using a GROUP BY clause.

Although data breaches are becoming more and more common, adopting effective security measures lowers the risk of being a target and helps prevent successful breach attempts. Implementing a multi-factor authentication method will provide your database with an additional layer of security. To further reduce the risk of a possible breach, only valid IP addresses should be allowed access to the database. Regular database backups are necessary to reduce the risk of losing critical data due to cyberattacks or data corruption.

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SQL for Data Engineering - A Match Made in Heaven

Mastering SQL engineering requires practice and patience, but the rewards are significant. As a data engineer with strong SQL skills, you'll be better equipped to tackle complex data challenges and deliver value to your organization. If you want to enter the big data and data science field, one of the most crucial skills to master is SQL. Exploring new data sets and incorporating them into practical use cases is the best way to gain practical knowledge using any of the key tools for data engineering. ProjectPro offers unique industry-level big data and data science projects that will help you master SQL and many other skills crucial for data engineering roles.

 

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About the Author

Daivi

Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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