![]() ![]() And don't worry if you have sensitive / personal data - we'll cover that too. In the next post we'll explore how to subset your existing database for testing purposes. Synth: Synth has a small learning curve, but to create realistic testing data at scale it reduces most of the manual labour.Postgres generate_series: This method scales better than manual insertion - but if you care about the contents of your data and have foreign key constraints you'll need to write quite a bit of bespoke SQL by hand. ![]() If your needs are basic it's the path of least effort to creating a working dataset. Manual Insertion: Is ok to get you started.We explored 3 different ways to generate data. Taking a look at the company table: contact_id A workspace is just a directory in your filesystem that tell Synth that this is where you are going to be storing configuration: The first step to use Synth is to create a workspace. To install the synth binary refer to the installation page. Synth uses declarative configuration files (just JSON don't worry) to define how data should be generated. Keep consistent data across multiple tables with data integrity support. Create, save, and use your own data generators. Fine-tune your data generation process with several distribution modes. It has integration with Postgres, so you won't need to write any SQL. With the Data Generation feature, you will be able to: Create vast amounts of realistic test data effortlessly. Synth is an open-source project designed to solve the problem of creating realistic testing data. We could define functions ourselves to generate names / phone numbers / emails etc, but why re-invent the wheel? Using a data generator like Synth # text in your phone column actually being a phone number) we need to get more sophisticated. If you care about your data being semantically correct (i.e. First of all, every company has exactly 1 contact, and more importantly the actual data looks completely useless. You can spend many nights playing with these parameters, and obtain different databases that can produce trends and exceptions you can analyze for your demos.We generated 100 companies and contacts here, the types are correct, but the output is underwhelming. The transactions generated in the Sales table are not entirely random: there are parameters used to control the distribution of transactions over products, customers, stores, and time. You can customize all these files and produce any content you want. We added stores and customers using other files obtained by random data generation services. The content of the database is based on the Microsoft Contoso sample database. You must be a C# developer to partake in this game. There is a Visual Studio solution with a C# project running in. Hint To avoid generating test files, you can pass the -no-spec flag, as follows. Customize the C# code of the tool to implement new features. Generate Data Transfer Objects (or inputs for GraphQL applications) to.You must alter configuration files and the PowerShell scripts. Download the executable, customize the parameters, and run the scripts to control the content of the database in terms of volume and data distribution. You get a backup that you can restore on a SQL Server or Azure SQL instance. This sample data is referenced in Azure Cosmos DB code samples. Download an existing, ready-to-use Contoso database. This repository hosts sample data used by Azure Cosmos DB documentation.You can use the content available in different ways: We have now cleaned up the code and produced minimal documentation to share the tool and make it available for free in an open-source project on GitHub: Contoso Data Generator. We also wanted to be able to control the content so that different trends in the data present for specific countries or stores could be available, thus allowing us to recreate specific situations we wanted to demo in future articles and videos.įor these reasons, our latest investment is a tool we created that is able to generate different versions of the Contoso database used for the course. When working on the Mastering Tabular video course, we needed a sample database with more data than what we had at our disposal in existing sample databases. ![]()
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