With Mockingbird, you can define a data schema in JSON, set the data generation frequency, and start streaming mock data based on your schema to any HTTP-enabled streaming endpoint. Today, we introduce Mockingbird, a flexible, FOSS mock data generator to stream data to both Tinybird and other destinations. Mockingbird: A free, open source mock data generator In that case, you need a simple, serverless tool to define both your schema and generation frequency with absolute precision. You might still be evaluating the service and don’t want to use your own data yet, or you don’t want to use real data in development environments, or perhaps you’re still building the rest of your streaming pipeline. Whether you’re streaming from Kafka, importing from BigQuery, or just uploading a simple CSV file, Tinybird gives you the power to capture events and dimensions from those sources, query and enrich them with SQL, and publish your queries as low-latency, parameterized HTTP APIs to power your applications.īut there are loads of reasons why you might want to use mock data. In Tinybird, you can ingest data from a number of different sources. Public data sets and APIs can be helpful for prototyping, but you don't have any control over the data schema and how often data is generated. That said, I have no control over the schema and the data frequency, and if I’m building a project unrelated to carbon intensity, Wikipedia pages, stock markets, or crypto, these aren’t ideal. These APIs and data sets are super helpful, and I’m grateful for those who maintain them. There are plenty of public APIs that you can use to build demo applications or stress test infrastructure if you don’t need a data stream that follows your own custom schema or timing. You need to be able to generate a constant stream of data that emulates the real world, both in terms of data schema and generation frequency. In the batch world, it’s common to simply upload a massive CSV file, but this doesn’t work for real-time and streaming applications. This tool is in beta version, if you find a bug, please let me know in the comments.When you build a data project, you often need some source of non-production data that you can use to develop against, find edge cases, and test performance at scale. The basic generator is easier to use, but does not allow you to generate complex data.It is ideal for generating CSV data that you want to integrate into a database.This tool also provides an API to generate data. This generator can generate a variety of data types, including names, addresses, email addresses. These generators are a bit complicated to use, you have to be comfortable with this type of data. This type of data that approximates real data helps to find bugs more easily.Also, if you have to give a presentation, using realistic data can help understanding.Īdvanced test data generators in JSON and XML format allow to generate complex data with sub-objects / tags. This powerful tool is 100% online and allows you to quickly generate realistic test data (datasets). We often need test data to validate that our applications respect the functional rules, and also that they hold the load with a large volume of data.
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