Users can invoke a pipeline through a variety of channels (UI, gcloud, REST) by referring to the spec file. Metadata specification files referencing the GCR image path and parameters details will be created and stored in Google Cloud Storage. Data Generator will quickly insert thousands of records customized to the unique shape of your Salesforce org.
Whatever you call it, you need it to demo or test. Flex templates package Dataflow pipeline code, including application dependencies, as Docker images and stage the images in Google Container Registry (GCR). Instantly create thousands of mock records for any object or field. Many I/Os don’t implement ValueProvider Interface, which is essential to supporting runtime parametersįlex templates overcome these limitations. However, traditional templates have certain limitations: In the initial version of templates (called traditional templates), pipelines were staged on Google Cloud Storage and could be launched from the Google Cloud Console, the gcloud command-line tool or other cloud-native Google Cloud services such as Cloud Scheduler or Cloud Functions. The primary goal of Dataflow templates is to package Dataflow pipelines in the form of reusable artifacts that can be run in various channels (UI / CLI / REST API) and be used by different teams.
There are 30+ data types, from names and locations to logos and fake credit card. Flex Templatesīefore diving into the details of the Streaming Data Generator template’s functionality, let’s explore Dataflow templates at a very high level: The mock data generator is intended to help you generate realistic data.
#DATA GENERATOR FAKE HOW TO#
In this blog post, we will briefly discuss the use cases and how to use the template. We are excited to announce the launch of a new Dataflow Flex template called Streaming Data Generator that is capable of publishing unlimited high-volume JSON messages to a Google Cloud Pub/Sub topic. Having learned that this is a very common need which helps IT teams to validate system resilience during evaluations and migrations to new platforms, we decided to build a pipeline that eliminates the heavy lifting and makes synthetic data generation easier. Generating synthetic data at a very high queries per second (QPS) is a challenging task that forces developers to build and launch multiple instances of a complex multi-threaded application.
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