Using fake data can be useful during system development, testing, and demos, mainly because it avoids using real identities, full names, real credit card numbers or Social Security numbers, etc. while using "Lorem ipsum" strings and random numbers is not a realistic enough to be meaningful. Alternatively, one could use cloned production data, except that it generally does not exist for new applications, plus you would still have to mask or substitute sensitive data to avoid disclosing any personally identifiable information.

1. Hackolade Tutorial
Tutorial – Generate Synthetic / Mock data
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2. Synthetic / Mock data can be really useful
• During system development, testing and demos
• Avoids using real identities – but still representative for the use case
• Useful for exploring edge cases that lack real data
• Useful for identifying model bias
• Manual creation of fake data is too cumbersome, especially at scale
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3. Two step process
1. One-time setup for each model: each attribute assigned a “faker”
function that will generate a contextually realistic sample
• Using Faker.JS
• See https://fakerjs.dev/guide/ for a great guide
• Copy syntax into the property
• Examples: faker.person.firstName("female"),
faker.helpers.fake('{{person.firstName}} {{person.lastName}}’),
'${faker.company.name()}, ${faker.company.suffixes()}'
2. At generation time, you define the parameters of the run
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4. Examples
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5. Preview results
• In JSON Data preview pane
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6. Generate test data in bulk
• Hackolade can generate
• JSON documents for any model entity
• records or documents to be inserted in database instances, where applicable
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7. Insert documents / records in a database instance
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8. Reading material
• See Hackolade online documentation
• The Hackolade Blog
• This excellent new book:
MongoDB Data Modeling & Schema Design
• Many of the principles in the book are related to
query-driven modeling based on access patterns
• Hackolade’s on social media: LinkedIn page, Twitter page
• Download Hackolade Studio for free
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9. Questions?
Answers!
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