# Match input data using a matching workflow
<a name="create-matching-workflow"></a>

A *matching workflow* is a data processing job that combines and compares data from different input sources and determines which records match based on different matching techniques. AWS Entity Resolution reads your data from your specified locations, finds matches between records, and assigns a [Match ID](glossary.md#match-id-defin) to each matched set of data.

The following diagram summarizes how to create a matching workflow.

![A summary of the four steps to create a matching workflow in AWS Entity Resolution](http://docs.aws.amazon.com/entityresolution/latest/userguide/images/HIW-Matching-Workflow.png)

**Topics**
+ [Matching workflow types](#matching-workflow-types)
+ [Data output options](#data-output-options)
+ [Matching workflow results](#matching-workflow-results)
+ [Creating a rule-based matching workflow](creating-matching-workflow-rule-based.md)
+ [Creating a machine learning-based matching workflow](create-matching-workflow-ml.md)
+ [Creating a provider service-based matching workflow](create-matching-workflow-provider.md)
+ [Editing a matching workflow](edit-matching-workflow.md)
+ [Deleting a matching workflow](delete-matching-workflow.md)
+ [Modifying or generating a Match ID for a rule-based matching workflow](generate-match-id.md)
+ [Looking up a Match ID for a rule-based matching workflow](find-match-id.md)
+ [Deleting records from a rule-based or ML-based matching workflow](delete-records.md)
+ [Troubleshooting matching workflows](troubleshooting.md)

## Matching workflow types
<a name="matching-workflow-types"></a>

AWS Entity Resolution supports three types of matching workflows: 

Rule-based matching  
Uses configurable rules to identify matching records based on exact or fuzzy matching of specified fields. You define the matching criteria, such as matching names that are spelled similarly or addresses that are formatted differently. 

Machine learning-based matching  
Uses machine learning models to identify similar records, even when the data has variations, errors, or missing fields. This approach can detect more complex matches than rule-based matching. 

Provider service-based matching  
Uses third-party data providers to enrich and validate your data before matching. This type of matching is not compatible with Connect Customer Customer Profiles output.

## Data output options
<a name="data-output-options"></a>

AWS Entity Resolution can write data output files to: 
+ An Amazon S3 location that you specify 
+ Connect Customer Customer Profiles (for customer data deduplication) 

**Important**  
Exporting to Connect Customer Customer Profiles is not compatible with provider-based matching. To export to Connect Customer Customer Profiles, you must use rule-based matching or machine learning-based matching.

You can use AWS Entity Resolution to hash output data if desired – helping you maintain control over your data. 

The following table shows the three types of matching workflows and their supported output destinations.


| Matching type | S3 output | Customer Profiles Output | 
| --- | --- | --- | 
| [rule-based](creating-matching-workflow-rule-based.md) | ![](http://docs.aws.amazon.com/entityresolution/latest/userguide/images/success_icon.png) Yes | ![](http://docs.aws.amazon.com/entityresolution/latest/userguide/images/success_icon.png) Yes | 
| [machine learning-based](create-matching-workflow-ml.md) | ![](http://docs.aws.amazon.com/entityresolution/latest/userguide/images/success_icon.png) Yes | ![](http://docs.aws.amazon.com/entityresolution/latest/userguide/images/success_icon.png) Yes | 
| [provider service-based](create-matching-workflow-provider.md) | ![](http://docs.aws.amazon.com/entityresolution/latest/userguide/images/success_icon.png) Yes | ![](http://docs.aws.amazon.com/entityresolution/latest/userguide/images/negative_icon.png)No | 

## Matching workflow results
<a name="matching-workflow-results"></a>

After you create and run a matching workflow, you can view the results in your specified S3 location or in Connect Customer Customer Profiles. Matching workflows generate IDs after the data is indexed.

A matching workflow can have multiple runs and the results (successes or errors) are written to a folder with the `jobId` as the name.

For each run for S3 output destinations:
+ The data output contains both a file for successful matches and a file for errors
+ Successful results are written to a `success` folder containing multiple files
+ Errors are written to an `error` folder with multiple fields

For each run for Connect Customer Customer Profiles output destinations:
+ Deduplicated customer records are sent directly to your Connect Customer instance
+ You can view your recent job history in the AWS Entity Resolution console
+ Existing profiles in Connect Customer are not included in the deduplication process

After you create and run a matching workflow, you can use the output of [rule-based matching](creating-matching-workflow-rule-based.md) or [machine learning (ML) matching](create-matching-workflow-ml.md) as an input to [provider service-based matching](create-matching-workflow-provider.md) or the other way around to meet your business needs. 

For example, to save provider subscription costs, you can first run [rule-based matching](creating-matching-workflow-rule-based.md) to find matches on your data. Then, you can send a subset of unmatched records to [provider service-based matching](create-matching-workflow-provider.md). Note that if you plan to export to Customer Profiles, you should use rule-based or machine learning-based matching only.

For more information about troubleshooting errors, see [Troubleshooting matching workflows](troubleshooting.md).