SQL Test Data Generator Tool

SQL Test Data Generator Tool

This tool allows you to generate random SQL files from a template. The utility allows you to generate SQL data, and export the generated SQL files. Mock Data can start developing an app and testing and problem solving when data service is unavailable or requires significant work to set up.

How to use SQL Generator Online

1.  Fill in the editor "Your SQL template" and click on the "Generate" button.
2. The "SQL generated" editor will contain the result.
3. You have a "Field Tags" section which shows you the available tags that you can use
4. You can dowload your CSV files below the editor. 

Using tags

You can also use tag in a property name. The tag must be in a string, enclosed in double curly brackets. Example:

INSERT INTO Customers (CustomerName, ContactEmail, Address, City, PostalCode, Country)
VALUES ('{{name}}', '{{email}}', '{{street}}', '{{city}}', '{{postcode}}', '{{country}}');

Test SQL Object Generator Overview

Data Generator for SQL is a tool for software developers and quality assurance engineers who need to generate test SQL documents in bulk for software or service testing. The document's structure can be explicitly created or imported from an existing SQL file. For smart data creation, the application uses a data generation engine that enables complicated data generation and value-to-value dependencies. The user can make test data more realistic by using the built-in Value Library, which contains predefined lists (names, dates, cities, streets, currencies, email, statecodes, firms, and so on).

SQL Data Generator

The Data Generator data source is a built-in engine that generate many types of property values. This method eliminates the need to manually create data storages and populate them with data. Manually populating a database is a time-consuming and stressful task.
A test data creation tool enables business owners to try out and test their technologies before releasing them to the general market. It's recommended to avoid manual operations and instead use one of the above-mentioned test data creation solutions, which have a higher success rate.

Generate SQL from a Template

The SQL Generator uses a SQL template to generate data. Basic and custom settings are available in the SQL Generator's dialogue box. You can personalise the generator by altering the parameters as needed.
SQL Generator also includes Template and Preview dialogue boxes. By modifying the default script, you can customise the default template.

How Do You Create Various Name Test Data?

Each set is randomly generated to simulate real data.

Test data is actually the input given to a software program. It represents data that affects or is affected by the execution of the specific module. Some data may be used for positive testing, typically to verify that a given set of input to a given function produces an expected result. Other data may be used for negative testing to test the ability of the program to handle unusual, extreme, exceptional, or unexpected input. Poorly designed testing data may not test all possible test scenarios which will hamper the quality of the software.

Testing is an iterative part of the development process that it performed to ensure the quality of the code. During the development process you will need fake data similar to real data for testing purposes.

Tag Reference

Use these tag to customize your test data in your template.

{{addressuk}} : Single-line UK address.
{{addressuk}} ---> 9315 Lancaster Circle, Haslingden, Nottinghamshire, HS15 6YD

{{addressus}} : Single-line US address.
{{addressus}} ---> 8184 Ambrose, Fontana, Minnesota, 44626

{{addressgerman}} : Single-line German address.
{{addressgerman}} ---> Bahnhofsstrasse 12, 80335 München

{{airport}} : Three-digit airport code.
{{airport}} ---> MTK

{{autoinc}} : Auto-incrementing number. starting number (default 1)
{{autoinc 1000}} ---> 1000
{{autoinc}} ---> 1001

{{boolean}} : Boolean value. probability of being true (default 0.5)
{{boolean}} ---> false
{{boolean 0.95}} ---> true

{{cat}}: Cat name.
{{cat}} ---> Smokey

{{city}} : US city name.

{{cityGerman}} : German city name.
{{cityGerman}} ---> Berlin

{{company}} : Company name.|
{{company}} ---> Venusian Software Corp

{{country}} : Country name.
{{country}} ---> Lebanon

{{county}} : UK county name.
{{county}} ---> Derbyshire

{{creditcard}} : Credit card number.
{{creditcard}} ---> 6011867289904845

{{currency}} : Currency code.
{{currency}} ---> USD

{{date}} : Random date in YYYY-MM-DD format.
Parameters:
min - minimum date (default '1970-01-01')
max - maximum date (default 'now')
{{date}} ---> 1977-02-28
{{date 2015-01-01}} ---> 2018-08-02 // date after 2015-01-01
{{date 2015-01-01 2016-01-01}} ---> 2015-04-23 // date in 2015

{{date_iso}} : Random date in ISO-8601 format.
Parameters:
min - minimum date (default '1970-01-01')
max - maximum date (default 'now')
{{date_iso}} ---> 2013-05-24T02:44:04.687Z
{{date_iso 2015-01-01}} ---> 2018-07-09T22:15:30.512Z // date after 2015-01-01
{{date_iso 2015-01-01 2016-01-01}} ---> 2015-04-21T18:08:33.979Z // date in 2015

{{digits}} : String of numerals.
Parameters:
numDigits (default 5)
{{digits}} ---> 06984
{{digits 8}} ---> 61103920

{{dog}}: Dog name.
{{dog}} ---> Lucky

{{domainname}}: Domain name.
{{domainname}} ---> yallaer.com

{{email}} : Email address. Whether to use the previous firstname/surname for the email generation
{{email}} ---> jermaine.buchanan@drilling.com
# based on previous name
{{name}} ---> Sheba Arthur
{{email true}} ---> sheba21436@yes.etnedal.no

{{emojii}} : Emojii.
numChars - number of characters (default 1)
{{emojii}} ---> 
{{emojii 3}} ---> 

{{file}} : Get a random line from a simple text-file, e.g.
line1
another line
yeah
Parameters:
filename - full path to the file (no default)
{{file /path/to/file.txt}} ---> line1

{{firstname}} : Human first name.
{{firstname}} ---> Bethan

{{float}} : Floating point number.
Parameters:
min (default 1)
max (default 100)
decimalPlaces (default 4)
{{float}} ---> 13.8592
{{float 1000 2000 2}} ---> 1750.06

{{integer}} : Integer number.
Parameters:
min (default 1)
max (default 100)
{{integer}} ---> 99
{{integer 1000 2000}} ---> 1523

{{ip}} : IP4 address.
Parameters:
local - boolean (whether to generate an IP in the 192.168.1 range or not)
{{ip}} ---> 63.235.50.110
{{ip true}} ---> 192.168.1.58

{{ip6}} : IP6 address.
Parameters:
local - boolean (whether to generate an IP in the fc00::: range or not)
{{ip6}} ---> 487d:375d:7bfe:b23b:d1e2:8934:cfb2:c17e
{{ip true}} ---> fc00:cefe:5dfc:14da:691d:b4bf:63ac:6d17

{{kuuid}} : Time-sortable, unique identifier. see here
Parameters:
min (optional) - minimum date
max (optional) - maximum date
{{kuuid}} ---> 001g8LWk0Svk222Bd0Et0GeaBl1P1gkP
{{kuuid 2010-01-01 2019-01-01}} ---> 001eWhUE2HLix22HqmL5436NDm1p02X6

{{kuuidr}} : Time-sortable, unique identifier, but in reverse order. see here
Parameters:
min (optional) - minimum date
max (optional) - maximum date
{{kuuidr}} ---> zzzwSGtT2o36oK17mC4R26dkHI1f1Xm0
{{kuuid 2010-01-01 2019-01-01}} ---> zzyTAKwG1uzH1N4Cl4xi1vZIE22oxrrT

{{last}} : The last generate value of the supplied tag. This is used to re-use generated data elsewhere in your template.
Parameters:
tag - the name of the tag
{{last uuid}} ---> HSFC5LQPCP84IVY7 (the last uuid that was generated by datamaker)

{{latitude}} : Decimal latitude.
{{latitude}} ---> -54.4371

{{letters}} : String of uppercase characters.
Parameters:
numLetters (default 5)
{{letters}} ---> TVEHJ
{{letters 10}} ---> WOBOJRJFCU

{{longitude}} : Decimal longitude.
{{longitude}} ---> 175.2526

{{mac}} : Mac address
{{mac}} ---> 23-0d-a3-3e-cf-d8

{{marque}} : Car manufacturer.
{{marque}} ---> Bugatti

{{monarch}} : King or Queen.
{{monarch}} ---> Henry VIII

{{oneof}} : Picks one of supplied values.
Parameters: any number of strings
{{oneof Gryffindor Hufflepuff Ravenclaw Slytherin}} ---> Slytherin

{{name}} : Combination for firstname and surname. Equivalent of {{firstname}} {{surname}}.
{{name}} ---> Anna Flint

{{normal}} : Generates numbers on a normal distribution
Parameters:
mean - centre of distribution (default 50)
stddev - standard deviation (default 1)
decimalPlaces - number of decimal places (default 4)
{{normal}} ---> 50.1097
{{normal 20000 1000 2}} ---> 20370.88

{{password}} : A commonly-used password.
{{password}} ---> abcd1234
{{password | md5}} ---> 1f3870be274f6c49b3e31a0c6728957f

{{president}} : A US president
{{president}} ---> Richard Nixon

{{postcode}} : UK postcode.
{{postcode}} ---> KT4 0XS

{{price}} : Floating point price.
Parameters:
min (default 1)
max (default 100)
{{price}} ---> 65.29
{{price 500 700}} ---> 521.98

{{prime}}: Prime number. If you supply impossible parameters, you will get 1 in reply.
Parameters:
min (default 1)
max (default 100)
{{prime}} ---> 17
{{prime 1000 3000}} ---> 1657

{{product}} : Product name.
{{product}} ---> CARABAO
{{product | toTitleCase}} ---> Salt

{{sic}} : Industry standard SIC code - business category
{{sic}} ---> Growing of tobacco

{{state}} : US state name.
{{state}} ---> Ohio

{{statecode}} : 2-letter US state code
{{statecode}} ---> NC

{{street}} : Street address.
{{street}} ---> 6502 Chantler Avenue

{{streetGerman}} : German Street name.
{{streetGerman}} ---> Schulstrasse

{{surname}} : Human surname.
{{surname}} ---> Doyle-Tyson

{{tel}} : International telephone number.
{{tel}} ---> +43-3040-100-474

{{time}}:Time in HH:MM:SS format.
{{time}} ---> 10:45:59

{{timestamp}} : Timestamp in milliseconds since 1970.
Parameters:
min - minimum timestamp (default 0)
max - maximum timestamp (default 'now')
{{timestamp}} ---> 351543517819
{{timestamp 946684800000}} --> 1163308913102  // timestamp after 2000-01-01
{{timestamp 946684800000 978307200000}} --> 959753617250  // timestamp in year 2000

{{title}} : Honorific or title e.g. Mr, Mrs etc.
{{title}} ---> Reverend

{{tld}} : Top level domain, or common domain name ending.
{{tld}} ---> gov.pr

{{town}} : UK town name.
{{town}} ---> Dudley

{{unit}} : An SI Unit
{{unit}} ---> °C

{{url}} : Full URL.
{{url}} ---> https://www.jovinianist.com/microcosmology.html?adrenalone=VEF0TSB23N04V8MO

{{uuid}} : Unique identifier.
{{uuid}} ---> C3QTU8YIVKZY126Q

{{uuidv4}} : Unique identifier v4.
{{uuidv4}} ---> d1e606b0-0452-46a7-9190-8671a82fdea0

{{website}} : Full website URL.
{{website}} ---> https://unlyrically.com

{{word}} : Single word.
{{word}} ---> synentognathous

{{words}} : Multiple words.
Parameters:
count (default 5)
{{words}} ---> piacularly burp archisymbolical glumaceous Calinago
{{words 2}} --->Xenomorpha mauler

{{youtube}} : Random YouTube URL.
{{youtube}} ---> https://www.youtube.com/watch?v=dQw4w9WgXcQ

{{zip}} : US ZIP code.
{{zip}} ---> 69882

Importance of Test Data in Software Development

Test data is crucial for validating application functionality, performance, and security in software development. It enables developers to identify defects and ensure the system behaves as expected under various conditions. Without adequate test data, applications may encounter issues post-deployment, leading to user dissatisfaction and potential financial losses. Utilizing tools like SQL Test Data Generators allows for creating realistic and diverse datasets, facilitating thorough testing and robust software solutions.

Features of SQL Test Data Generators

Modern SQL Test Data Generators offer a range of features designed to streamline the data creation process. These tools can quickly generate large volumes of data, support various data types, and maintain referential integrity by recognizing foreign key relationships. Additionally, they often provide customization options, allowing users to define specific parameters and constraints to accurately mirror real-world scenarios. Such capabilities are essential for effective testing and development workflows.

Comparison of SQL Test Data Generation Tools

Several SQL Test Data Generation tools are available, each with unique functionalities. For instance, Redgate's SQL Data Generator is known for its user-friendly interface and rapid data generation capabilities. Mockaroo offers extensive customization options and supports multiple data formats, including CSV, JSON, and SQL. ApexSQL Generate provides a comprehensive suite of features for creating realistic test data and supports a wide range of SQL data types. Evaluating these tools based on project requirements can help you select the most suitable one.

Best Practices for Generating Test Data

When generating test data, it's essential to ensure that the data is realistic and diverse to simulate real-world scenarios effectively. Incorporating various data types, such as strings, numbers, dates, and Boolean values, can help achieve this. Maintaining data consistency, especially in databases with foreign key relationships, is crucial. Utilizing tools that support inter-column dependencies and allow for the customization of data generation rules can aid in creating meaningful test datasets.

Data Masking and Privacy Considerations

Incorporating data masking features in SQL Test Data Generators is vital for protecting sensitive information during testing. Data masking involves replacing real data with fictitious but realistic data, ensuring that confidential information remains secure. This practice is particularly important when using production data for testing, as it helps organizations comply with data protection regulations and maintain user privacy.

Integration with Continuous Integration/Continuous Deployment (CI/CD) Pipelines

Integrating SQL Test Data Generators into CI/CD pipelines can enhance automated testing processes. By automating test data generation, teams can ensure that each build is tested with fresh and relevant data, leading to more reliable test results. This integration supports continuous testing practices, enabling faster defect identification and more efficient development cycles.

Challenges in Test Data Generation

While test data generation is beneficial, it presents certain challenges. Ensuring data validity, maintaining referential integrity, and creating data that accurately reflects production environments can be complex tasks. Additionally, generating large datasets may require significant computational resources. Addressing these challenges involves selecting appropriate tools, defining clear data generation rules, and continuously validating the generated data against real-world scenarios.

Future Trends in Test Data Generation

The field of test data generation is evolving, with emerging trends focusing on enhancing data realism and automation. Advancements in artificial intelligence and machine learning enable the creation of more sophisticated data generation models that mimic complex data patterns observed in production environments. Additionally, there is a growing emphasis on integrating test data generation tools with other aspects of the software development lifecycle, promoting seamless workflows and more efficient testing processes.

What is Test Data? Why is it Important?

Dummy Test data is actually the input given to a software program. It represents data that affects or is affected by the execution of the specific softwar feature. Some data may be used for positive testing, typically to verify that a given set of input to a given function produces an expected result. Other data may be used for negative testing to test the ability of the program to handle unusual, extreme, exceptional, or unexpected input.

Software Testing Methodologies

• Black Box Testing - A software testing method in which the internal structure/design/implementation of the item being tested is not known to the tester. These tests can be functional or non-functional, though usually functional. Test design techniques include: Equivalence partitioning, Boundary Value Analysis, Cause Effect Graphing.
• White Box Testing - A software testing method in which the internal structure/design/implementation of the item being tested is known to the tester. Test design techniques include: Control flow testing, Data flow testing, Branch testing, Path testing.
• Gray Box Testing - A software testing method which is a combination of Black Box Testing method and White Box Testing method.
• Agile Testing - A method of software testing that follows the principles of agile software development.
• Ad Hoc Testing - A method of software testing without any planning and documentation.

What to Look for in Mobile Testing Tools?

How do you choose the best tool for creating test scripts? Begin by focusing on the following characteristics, which are critical for successful mobile app testing.

• Multiple scripting languages are supported. It's critical to have a variety of test creation choices.
  This involves having a selection of scripting languages to pick from. Naturally, your QA staff should be able to work with those with whom they are already acquainted. As a result, look for a tool that supports a variety of programming languages.                
• You are developing tests for a variety of mobile operating systems. Look for a program that allows you to conduct your tests on many mobile platforms. It would be ideal for including a code reuse option, so you wouldn't have to write separate test scripts for each platform.
• Test creation choices that don't require any scripting. It is critical to creating tests without coding. This feature of mobile testing tools is beneficial to folks who do not have programming expertise. You may also develop tests very quickly this way.
• CI/CD tools are seamlessly integrated. Make sure your mobile testing tool is simple to integrate with the technologies you already use for your CI/CD procedures.