To that end, it is available as a headless library, a CLI, and a UI. It is designed to be flexible and work in a variety of different scenarios. Mockingbird is licensed under the Apache 2.0 license, and the source is publicly available on GitHub. With today’s launch, two Destinations are supported: Mockingbird is free and open source, and new destinations can be added by anyone, including the community and other vendors, by creating new Destination plugins. So, we’ve chosen not to limit the destinations of your data to just Tinybird. We originally built Mockingbird to help us create demos for Tinybird, but we understood we couldn’t be the only ones who needed a better source of mock data. 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. 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.
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