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9 changes: 8 additions & 1 deletion index.rst
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:maxdepth: 1
:caption: Prepare data

sagemaker-datawrangler/index
sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing_outputs
sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing_outputs

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sagemaker-clarify/index
scientific_details_of_algorithms/index
aws_marketplace/index



.. toctree::
:maxdepth: 1
:caption: Community examples

contrib/index
contrib/index





41 changes: 41 additions & 0 deletions sagemaker-datawrangler/README.md
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![Amazon SageMaker Data Wrangler](https://github.com/aws/amazon-sagemaker-examples/raw/main/_static/sagemaker-banner.png)

# Amazon SageMaker Data Wrangler Examples

Example flows that demonstrate how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler.

## :books: Background

[Amazon SageMaker Data Wrangler](https://aws.amazon.com/sagemaker/data-wrangler/) reduces the time it takes to aggregate and prepare data for ML. From a single interface in SageMaker Studio, you can import data from Amazon S3, Amazon Athena, Amazon Redshift, AWS Lake Formation, and Amazon SageMaker Feature Store, and in just a few clicks SageMaker Data Wrangler will automatically load, aggregate, and display the raw data. It will then make conversion recommendations based on the source data, transform the data into new features, validate the features, and provide visualizations with recommendations on how to remove common sources of error such as incorrect labels. Once your data is prepared, you can build fully automated ML workflows with Amazon SageMaker Pipelines or import that data into Amazon SageMaker Feature Store.



The [SageMaker example notebooks](https://sagemaker-examples.readthedocs.io/en/latest/) are Jupyter notebooks that demonstrate the usage of Amazon SageMaker.

## :hammer_and_wrench: Setup

Amazon SageMaker Data Wrangler is a feature in Amazon SageMaker Studio. Use this section to learn how to access and get started using Data Wrangler. Do the following:

* Complete each step in [Prerequisites](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-getting-started.html#data-wrangler-getting-started-prerequisite).

* Follow the procedure in [Access Data Wrangler](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-getting-started.html#data-wrangler-getting-started-access) to start using Data Wrangler.




## :notebook: Examples

### **[Tabular DataFlow](tabular-dataflow/README.md)**

This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Tabular dataset.

### **[Timeseries DataFlow](timeseries-dataflow/readme.md)**

This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Timeseries dataset.

### **[Joined DataFlow](joined-dataflow/readme.md)**

This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Joined dataset.



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## Import Flow

Each of the example has a flow file available which you can directly import to expedite the process or validate the flow.

Here are the steps to import the flow

* Download the flow file

* In Sagemaker Studio, drag and drop the flow file or use the upload button to browse the flow and upload

![uploadflow](/uploadflow.png)
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Amazon SageMaker Data Wrangler
=======================================

These example flows demonstrates how to aggregate and prepare data for
Machine Learning using Amazon SageMaker Data Wrangler.


------------------

`Amazon SageMaker Data
Wrangler <https://aws.amazon.com/sagemaker/data-wrangler/>`__ reduces
the time it takes to aggregate and prepare data for ML. From a single
interface in SageMaker Studio, you can import data from Amazon S3,
Amazon Athena, Amazon Redshift, AWS Lake Formation, and Amazon SageMaker
Feature Store, and in just a few clicks SageMaker Data Wrangler will
automatically load, aggregate, and display the raw data. It will then
make conversion recommendations based on the source data, transform the
data into new features, validate the features, and provide
visualizations with recommendations on how to remove common sources of
error such as incorrect labels. Once your data is prepared, you can
build fully automated ML workflows with Amazon SageMaker Pipelines or
import that data into Amazon SageMaker Feature Store.

The `SageMaker example
notebooks <https://sagemaker-examples.readthedocs.io/en/latest/>`__ are
Jupyter notebooks that demonstrate the usage of Amazon SageMaker.

Setup
-------------------------

Amazon SageMaker Data Wrangler is a feature in Amazon SageMaker Studio.
Use this section to learn how to access and get started using Data
Wrangler. Do the following:

- Complete each step in
`Prerequisites <https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-getting-started.html#data-wrangler-getting-started-prerequisite>`__.

- Follow the procedure in `Access Data
Wrangler <https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-getting-started.html#data-wrangler-getting-started-access>`__
to start using Data Wrangler.

Examples
-------------------

Tabular Dataflow
---------------------------

.. toctree::
:maxdepth: 1

tabular-dataflow/index

Timeseries Dataflow
----------------------------

.. toctree::
:maxdepth: 1

timeseries-dataflow/index

Joined Dataflow
----------------------------

.. toctree::
:maxdepth: 1

joined-dataflow/index
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