![]() ![]() The solution in this article builds a SQL Server database, fills it with sample data using SQL Data Generator, extracts the data into a JSON data file, using FOR JSON, and then drops the database. Maybe you need a sample data file to test a new web service. ![]() However, what if instead of a SQL Server database full of fake data, you need a JSON file? Perhaps you need to run some tests in MongoDB, or Azure Cosmos DB. SQL Data Generator is adept at filling SQL Server databases with ‘spoof’ data, for use during development and testing activities. He is a regular contributor to Simple Talk and SQLServerCentral. Phil Factor (real name withheld to protect the guilty), aka Database Mole, has 30 years of experience with database-intensive applications.ĭespite having once been shouted at by a furious Bill Gates at an exhibition in the early 1980s, he has remained resolutely anonymous throughout his career. Each category contains many sub-categories and options.īefore you start, you should familiarize yourself with the possibilities by consulting this guide.This is a guest post from Phil Factor. name, address, animal, company, commerce, date, database, finance, git, image, internet, phone, vehicle, etc. Faker provides a wide range of data categories, e.g. Hackolade leverages an open-source library for this feature: FakerJS, a generator of fake data based on static string input. each time you need to generate test data, you define the parameters of the runĪssign a Faker function to data model attributes.one-time setup for each model: you must associate each attribute with a function to get a contextually realistic sample.Generating mock test data is a 2-step process: And you can set the desired locale so the data elements are localized for better contextual meaning. City and streets names for example are randomly composed from elements that mimic real names. With Hackolade, you can generate first names and last names that look real but are not, and the same for company names, product names and descriptions, street addresses, phone numbers, credit card numbers, commit messages, IP addresses, UUIDs, image names, URLs, etc.ĭata generated here may be fake, but it has the expected format and contains meaningful values. The solution is to use Hackolade Studio to generate mock data, i.e. Moreover, manually generating fake data takes time and slows down the testing process, particularly if large volumes are required. 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.Īccording to ThoughtWorks TechRadar 27: "Synthetic data is also useful for exploring edge cases that lack real data or for identifying model bias." while using "Lorem ipsum" strings and random numbers is not a realistic enough to be meaningful. 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. Hackolade Studio allows you to generate fake but realistic data for your data models. Generate Schema Error during Couchbase reverse-engineering.Document types for Couchbase are not discovered as expected.Access issues when doing reverse-engineering of Couchbase.Server timeout during read query at consistency LOCAL_ONE.SSH-RSA key rejected with message "no mutual signature algorithm".MongoDB error not master and slaveOk=false.Managing multiple license keys and seats.Windows access denied error during upgrade.Professional Edition deployment options.Infer Primary Keys and Foreign Key Relationships.Suggest denormalization of a SQL schema.Identity Provider SSO (external browser).Migration to enhanced custom properties. ![]() Attribute boxes in hierarchical schema view.Benefits of data modeling apply to NoSQL and Agile.NoSQL databases, storage formats, REST APIs.Pre-populate new entities using snippets. ![]()
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