Wanna know how dummy and mock data can be created for your data science projects?

BigData is vital, we know that. Most of the time, there just isn’t enough data to do the tasks we need to do. The applications need to be tested with generated or dummy data that closely resembles the actual data which will be used during production.

It’s really hard to come up with data sets that possess uniqueness, diversity, and mass. Further still, data that has been manually prepared is prone to human error.

Let’s break it down:

Problem Statement:

When you create data manually, these are the problems you face:

  • Too many hours needed to create realistic data
  • Quality of the data is hard to maintain
  • Removing duplicate records and incorrect records from the data
  • Need to create scripts / needs coding
  • Not enough data produced to any machine learning models.
  • Can’t Ensure 100% secrecy and privacy.
  • Labeling data consistently.
  • Assembling information from several sources, frequently in different formats.

Use Cases

We’ve provided a list of the most typical applications of synthetic data across many sectors, departments, and business units.

  • Healthcare – Patient data, Clinical trials
  • Agriculture – Machine learning model 
  • Financial Services – Fraud Identification, Customer Analytics
  • Manufacturing – Quality Assurance
  • Automotive and Robotics – Self-driving cars, Autonomous robots
  • Security – Training data for video surveillance
  • Social Media – Testing content filtering systems


Our team has researched and come up with these 16 websites that generate the best realistic synthetic data that you can use in your testing:

  1. Faker
  2. Mockaroo
  3. GenerateData
  4. JSON Schema Faker
  5. FakeStoreAPI
  6.  Mock Turtle
  7. Avo iTDM – Intelligent Test Data Management
  10. EMS Data Generator
  11. Informatica Test Data Management
  12. Double
  13. Upscene – Advanced Data Generator
  14. CA Test Data Manager
  15. Solix EDMS
  16. SAP Test Data Migration Server

1. Faker

Faker is a great tool for generating mock data. There are several Faker data libraries available for different programming languages (e.g. PHP, Perl, Ruby, Node JS) to create mock data. The one we are looking at is specifically for Python. 

Faker is a Python library that helps you produce fake data. This library can easily be installed using the following command:

pip install Faker

After installation, the Faker class object is instantiated. Mock data of different types can be formed now by calling methods of the Faker class object.

Screenshot of Faker from GutHub Repository


  • A fake dataset can be produced using the open-source Python programme known as Python Faker in order to test applications, launch databases, and preserve user anonymity.
  • Fixtures provide data for testing and a broad range of value types. In addition, faker’s fixture is provided by the pytest plugin which can be used in tests. 

Link: https://github.com/joke2k/faker

2. Mockaroo

Mockaroo makes it really easy, simple, and quick to get randomly generated test data on some specifications.

The best thing about this platform is that no programming is needed and it can easily be downloaded in different formats (e.g. SQL, JSON, CSV, XML). The test data can be loaded directly into the test environment.

Once you sign in, you have the option to make and save schemas that can be reused in upcoming projects.

Screenshot of Mockaroo Homepage


  • Create your own mock APIs.
  • Offers a variety of data kinds, including state, city, nation, street address, latitude, and more.
  • The URLs, response, and error states are all under your control.
  • Numerous mocking libraries are provided for every language and platform.
  • You can download test data that was generated at random and load it right into your testing environment.

Link: https://www.mockaroo.com/

3. GenerateData

If you want to generate large amounts of customized data then GenerateData is what you’re looking for. It is an open-source tool that is available, for free, on the internet and provides an easy-to-use interface. Like Mockaroo, it has a quick start feature to generate diverse formats with various data types.

Screenshot of GenerateData (v4)

5,000 records can be saved at a time by giving a small payment of $20 in order to meet the requirement of generating mock data that is greater than 100 rows per run.


  • This utility for creating random data is available under a GNU license and is completely functioning.
  • It gives programmers the ability to create their own data types to produce unique kinds of random data.
  • You can install new country plugins that provide postal or zip code formats as well as city and area names.

Link: https://generatedata.com/

4. JSON Schema Faker

JSON is one of the most widely used formats for storing and sending data objects. Therefore, it would be advantageous to generate the hypothetical data and the JSON schema that describes the data structure.

Screenshot of JSON Schema Faker Tool


  • User interface is available for defining schema. 
  • You can choose from and build upon the list of Examples that has already been generated for you rather than manually building the schema.

Link: https://json-schema-faker.js.org/

5. FakerStoreAPI

By this point, you’ve probably encountered a decent amount of generic mock data (i.e., Loren Ipsum) types. Here is where FakeStoreAPI makes a change.


  • Pseudo-real data can be created for usage in e-commerce or shopping use cases without executing server-side code with freely available online REST API. 
  • The mock data will be very useful for applications that call for retail-related data in JSON format, such as products, carts, users, and login tokens.
  • Mock data can be produced or customized by just a few lines of code for the API call.

Link: https://fakestoreapi.com/

6. Mock Turtle

Want to generate fake data in a JSON schema? give Mock Turtle a shot.

The tool simulates a JSON tree structure, and with each click, the schema is directly updated. 

Large data sets and nested structures can be generated without any cost alongside JSON schema parsing with this tool.

Screenshot of Mock Turtle Website


  • They concentrate on a JSON generator with a GUI that imitates a JSON tree structure.
  • The data type can be changed, nodes can be added or deleted, all with the click of a button.
  • They offer numerous popular data types right out of the box.
  • If you have a JSON scheme from which you want to generate test data, just import the schema and a GUI tree will be created for you.

Link: https://mockturtle.net/

7. Avo iTDM – Intelligent Test Data Management

With only a few clicks, you can create test data that is similar to production data using the test data management software, Avo iTDM. You may speed up testing and be certain that the results will be of greater quality if reliable, useful, and relevant data is readily available. iTDM enables you to locate non-compliant data in test settings and stay up with ever changing data protection laws. Additionally, it enables you to produce and deliver relevant data later on.

                      Screenshot of Avo iTDM – Intelligent Test Data Management


  •  Data discovery: Identifies and processes personally identifiable information automatically (PII).
  •  Secures sensitive data for PII compliance through data obfuscation.
  •  Provisioning of data.
  •  Does not require developing a single line of code to produce synthetic data
  • Supports open architecture and has plug-and-play custom module technology.

Link: https://avoautomation.ai/


The synthetic data generator used by MOSTLY AI is AI-driven, and each generated dataset includes a QA report.The generator can produce statistically and structurally identical synthetic copies of the original data after receiving a data sample.The drawback is that training the algorithm requires a sample dataset.The benefit is that you can upload some production data and create as little or as much of its synthetic version as you need, saving you the time and effort of manually assembling a production-like dataset. In contrast to data masking, the resulting synthetic data is representational and maintains the usefulness of the original data.

Screenshot of MOSTLY AI


  • Support for DB2, MySQL, Oracle, and PostgreSQL.
  • Provides connections via AWS, GCP, and Azure.
  • Business regulations are always kept.
  • Free for daily production of up to 100K rows.
  • Synthesize entire databases with referential integrity, ensuring that they are fully GDPR compliant and private with protection for rare categories.
  • It’s simple to up- and down-sample data.

Link: https://mostly.ai/


Obtaining the proper test data at the appropriate time is made simpler by DATPROF.You can produce synthetic data and disguise your test data with DATPROF Privacy. Software teams can still use representative test data while keeping your customer information secure.

Screenshot of DATPROF Features and Capabilities


  • Keep data attributes.
  • High efficiency with big data sets.
  • Consistent behavior over numerous applications and databases.
  • Synthetic data generators are built-in.
  • Provides for CI/CD integration (Continuous Integration vs. Continuous Delivery).
  • Control and update all of your test data environments from a single platform.

Link: https://www.datprof.com/

10. EMS Data Generator

EMS Data Generator is used to generate test data for MySQL database tables. It enables you to simultaneously add test data to a MySQL database table.

Screenshot of EMS Data Generator


  • The resulting data is saved and edited in a SQL script.
  • Data types like SET, ENUM, GEOMETRY types, etc. are supported by this software.
  • The generated data’s preview is available.
  • For each field type, it offers a large range of generated parameters.
  • You can enter NULL values while using the EMS Data Generator.
  • As a list of values for data generation, the results of a SQL query are useful.

Link: https://www.sqlmanager.net/

11. Informatica Test Data Management

The greatest test data generation tool with automated data connectivity and test data generation capabilities is Informatica Test Data Management.

Screenshot of  Informatica Test Data Management


  • By disguising original data with altered information, this programme automatically locates data spots for consistent masking across databases.
  • Applications packaged with Informatica are supported, ensuring application integrity and accelerating deployments.
  • To become more effective at software testing, testers can store, exchange, enhance, and reuse test datasets.
  • Compliance reporting is provided along with monitoring.


12. Doble

Doble is a tool for managing test data that also handles data conversion, test plan development, and “historic” file conversion. For field testing and regulatory reporting, it guarantees accurate, consistent data sets.

Screenshot of  Doble


  • For a variety of test data, data management solutions are available, including T-Doble Software, SFRA (Sweep Frequency Response Analysis), and DTA (Domestic Tariff Area).
  • It enables you to select the options required for your business.
  • You can use it to arrange data across divisions, regions, and departments.

Link: https://www.doble.com/product/test-data-management/

13. Upscene – Advanced Data Generator

Upscene generates test data for your database tables. You can use it to construct complicated data across various connected tables.

Screenshot of  Upscene – Advanced Data Generator


  • It produces accurate data that appears to be real.
  • Numerous data types are supported by this tool, including date and time, integers, binary, and boolean.

Link: https://www.upscene.com/advanced_data_generator/

14. CA Test Data Manager

CA Test Data Manager can be used to mask, subset, find, edit, and manage data. You can use it to store data centrally as a reusable asset. 

Screenshot of  CA Test Data Manager


  • It offers dynamic self-service forms that can be used to locate, browse, examine, and monitor test data.
  • Identification of personally identifiable information is simple (PII).
  • It is able to produce synthetic test data.
  • You can use it to make virtual replicas of test data.
  • With the use of this technology, you can centrally store data as a reusable resource.

Link: https://www.ca.com/us/products/ca-test-data-manager.html

15. Solix EDMS

One of the well-known test data generation tools is Solix EDMS. For particular tests, it can extract specified transactional sets of business items.

Screenshot of  Solix EDMS Homepage


  • It assists you in repeatedly defining and utilizing application metadata and subset generation policies.
  • It provides a variety of test data creation rules to produce subsets with all the properties of production data.
  • It offers a variety of subset operations, such as remove, insert, and truncate.
  • Your infrastructure costs will be greatly decreased, and needless security concerns will be removed.

Link:  https://www.solix.com/data-management-solutions/test-data-management/

16. SAP Test Data Migration Server

With the aid of the SAP Test Data Migration Server, you can test, develop, and train your systems using actual SAP business data. By decreasing the time needed to manage data in development and test systems, it increases efficiency.

Screenshot of  SAP Test Data Migration Server Homepage


  • You can transfer and extract data for testing.
  • Through frequent delivery of up-to-date data, it optimizes testing, training, and development processes.
  • Transfer data across data centers that are not linked.
  • It enables you to cut back on infrastructure and costs.
  • By encrypting sensitive production data, this technology complies with data privacy requirements.

Link: https://help.sap.com/docs/SAP_TEST_DATA_MIGRATION_SERVER


In conclusion, using synthetic data is an effective and affordable solution to solve your problem, regardless of whether you need to access or share private data, supplement a limited source of data, or reduce biases in datasets..For data analysis and training AI models, research has demonstrated that synthetic data can be just as good as or even better than real-world data.It is very simple to create with the correct tools, making it quick and affordable data augmentation option.