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Currently all the operations are supported in a running, local installed version of Data Factory. If you want to use the Data Factory API, you will have to download the Data Factory Runtime.

Data Factory Runtime — Is a Java API that helps you to generate random data for your applications.
Easily create random date and collections data in your application through this Java API by using the Data Factory runtime.
Sample Data Factory API Quick Start Guide:


In this example we will use random data to generate a training dataset for a custom model.
Build a random data sample, which will produce a fixed size of random numbers.

Create the Random Data sample in the Data Factory

Select the Fixed number of rows from the dataset.

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In Data Factory, you can create reusable test data that can be repeatedly used throughout your application.
Data Factory is designed to simplify the development and testing process for large-scale data pipelines by using a metadata-driven approach to configure tasks and contexts.
Data Factory Runtime
The Data Factory runtime is responsible for hosting your data pipeline tasks on an Amazon Web Services (AWS) instance.
It can also run any UI for interacting with your pipeline in an AWS web browser.
The Data Factory runtime is delivered as a Java application, accessible as a web application in the AWS Web Portal and inside a Docker container.
Data Factory provides an API that can be used to interact with the data pipeline’s underlying runtime.
The following topics help you understand how to use the Data Factory API, data pipeline metadata, and data pipeline runtime:

Data Factory API
If you have a basic knowledge of Java, you can use the Data Factory API to create new pipelines, retrieve pipelines and run pipelines.
The API is simple to use and is designed to run a pipeline’s tasks in parallel.
The API also provides metadata-driven, task-driven, and template-driven techniques to create your own pipelines.
Data Factory Metadata
Data Factory’s metadata layer is a lightweight layer of metadata.
Data Factory metadata help you describe your pipeline’s configuration and input data.
The metadata layer contains the following:

Function-specific metadata
Function-specific metadata is available for Amazon Cognito User Pools, Amazon Cognito Identity, Amazon Elastic Compute Cloud (Amazon EC2), Amazon S3, and Google Cloud Storage.
This metadata can be used to create different types of tasks in your pipeline.
Input parameter metadata
Data Factory provides input parameter metadata for topics such as Amazon Cognito User Pool, Amazon Cognito Identity, S3, and Cloud Data Sets.
Input parameter metadata helps you define variables with attributes, such as name, type, and default value.
The metadata is also used as a reference to customize your template to the specific input data.
Metadata file
The metadata file is used to access your tasks and inputs in your pipeline’s runtime.
The metadata file consists of a list of metadata names, identifiers, and descriptions.
Refer to the ‘How To’ section below for instructions on how to create your own metadata file.
Runtime-specific metadata
Data Factory runtime metadata are available for Amazon EC2, S3, and Google Cloud Storage.
Runtime-specific metadata

What’s New In Data Factory?

Data Factory is a service that automatically generates large amounts of
data for your use. It’s fully managed by Azure for you, so your samples
never run out, and there’s no expensive hardware to purchase or lease.

Data Factory is a self-service service, and we strongly encourage you
to use it.
Data Factory sample data is generated either:

By continuously running a background task and injecting a random seed
to have a consistent collection of random values.
By continuously running a background task and maintaining a counter value
in a database table.

Data Factory uses various Azure services like SQL Data Warehouse and Azure SQL Data Warehouse for data collection.
Data Factory has three data collection types to help you get started:

Continuous: the data collection runs as long as the subscription remains active.
One-off: the data collection is done once, at the time you provision a data collection.
Random: the data collection generates a large number of values, and can be used for testing, sample data, or similar scenarios.

Today we present the Continuous Data Collection option that runs continuously, 24 hours a day and 7 days a week, resulting in a large amount of random data.
Data Factory Continuous Data Collection
Data Factory Continuous Data Collection is an option for Data Factory. When you choose this option, Data Factory will automatically run a background task to collect random data every day at specific time.
So, imagine you have data that you want to randomly sample from.
Data Factory Continuous Data Collection runs for 14 days, and records the last 7 days of collected data in a database table.
Then the Continuous Data Collection service will keep collecting data until 14 days elapsed.



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