# AWS Glue PySpark transforms reference
<a name="aws-glue-programming-python-transforms"></a>

AWS Glue provides the following built-in transforms that you can use in PySpark ETL operations. Your data passes from transform to transform in a data structure called a *DynamicFrame*, which is an extension to an Apache Spark SQL `DataFrame`. The `DynamicFrame` contains your data, and you reference its schema to process your data. 

Most of these transforms also exist as methods of the `DynamicFrame` class. For more information, see [DynamicFrame transforms ](aws-glue-api-crawler-pyspark-extensions-dynamic-frame.md#aws-glue-api-crawler-pyspark-extensions-dynamic-frame-_transforms).
+ [GlueTransform base class](aws-glue-api-crawler-pyspark-transforms-GlueTransform.md)
+ [ApplyMapping class](aws-glue-api-crawler-pyspark-transforms-ApplyMapping.md)
+ [DropFields class](aws-glue-api-crawler-pyspark-transforms-DropFields.md)
+ [DropNullFields class](aws-glue-api-crawler-pyspark-transforms-DropNullFields.md)
+ [ErrorsAsDynamicFrame class](aws-glue-api-crawler-pyspark-transforms-ErrorsAsDynamicFrame.md)
+ [EvaluateDataQuality class](aws-glue-api-crawler-pyspark-transforms-EvaluateDataQuality.md)
+ [FillMissingValues class](aws-glue-api-crawler-pyspark-transforms-fillmissingvalues.md)
+ [Filter class](aws-glue-api-crawler-pyspark-transforms-filter.md)
+ [FindIncrementalMatches class](aws-glue-api-crawler-pyspark-transforms-findincrementalmatches.md)
+ [FindMatches class](aws-glue-api-crawler-pyspark-transforms-findmatches.md)
+ [FlatMap class](aws-glue-api-crawler-pyspark-transforms-flat-map.md)
+ [Join class](aws-glue-api-crawler-pyspark-transforms-join.md)
+ [Map class](aws-glue-api-crawler-pyspark-transforms-map.md)
+ [MapToCollection class](aws-glue-api-crawler-pyspark-transforms-MapToCollection.md)
+ [mergeDynamicFrame](aws-glue-api-crawler-pyspark-extensions-dynamic-frame.md#aws-glue-api-crawler-pyspark-extensions-dynamic-frame-merge)
+ [Relationalize class](aws-glue-api-crawler-pyspark-transforms-Relationalize.md)
+ [RenameField class](aws-glue-api-crawler-pyspark-transforms-RenameField.md)
+ [ResolveChoice class](aws-glue-api-crawler-pyspark-transforms-ResolveChoice.md)
+ [SelectFields class](aws-glue-api-crawler-pyspark-transforms-SelectFields.md)
+ [SelectFromCollection class](aws-glue-api-crawler-pyspark-transforms-SelectFromCollection.md)
+ [Simplify\$1ddb\$1json class](aws-glue-api-crawler-pyspark-transforms-simplify-ddb-json.md)
+ [Spigot class](aws-glue-api-crawler-pyspark-transforms-spigot.md)
+ [SplitFields class](aws-glue-api-crawler-pyspark-transforms-SplitFields.md)
+ [SplitRows class](aws-glue-api-crawler-pyspark-transforms-SplitRows.md)
+ [Unbox class](aws-glue-api-crawler-pyspark-transforms-Unbox.md)
+ [UnnestFrame class](aws-glue-api-crawler-pyspark-transforms-UnnestFrame.md)

## Data integration transforms
<a name="aws-glue-programming-python-di-transforms"></a>

 For AWS Glue 4.0 and above, create or update job arguments with `key: --enable-glue-di-transforms, value: true`. 

 Example job script: 

```
from pyspark.context import SparkContext
        
from awsgluedi.transforms import *
sc = SparkContext()

input_df = spark.createDataFrame(
    [(5,), (0,), (-1,), (2,), (None,)],
    ["source_column"],
)

try:
    df_output = math_functions.IsEven.apply(
        data_frame=input_df,
        spark_context=sc,
        source_column="source_column",
        target_column="target_column",
        value=None,
        true_string="Even",
        false_string="Not even",
    )
    df_output.show()   
except:
    print("Unexpected Error happened ")
    raise
```

 Example Sessions using Notebooks 

```
%idle_timeout 2880
%glue_version 4.0
%worker_type G.1X
%number_of_workers 5
%region eu-west-1
```

```
%%configure
{
    "--enable-glue-di-transforms": "true"
}
```

```
from pyspark.context import SparkContext
from awsgluedi.transforms import *

sc = SparkContext()

input_df = spark.createDataFrame(
    [(5,), (0,), (-1,), (2,), (None,)],
    ["source_column"],
)

try:
    df_output = math_functions.IsEven.apply(
        data_frame=input_df,
        spark_context=sc,
        source_column="source_column",
        target_column="target_column",
        value=None,
        true_string="Even",
        false_string="Not even",
    )
    df_output.show()    
except:
    print("Unexpected Error happened ")
    raise
```

 Example Sessions using AWS CLI 

```
aws glue create-session --default-arguments "--enable-glue-di-transforms=true"
```

 DI transforms: 
+  [FlagDuplicatesInColumn class](aws-glue-api-pyspark-transforms-FlagDuplicatesInColumn.md) 
+  [FormatPhoneNumber class](aws-glue-api-pyspark-transforms-FormatPhoneNumber.md) 
+  [FormatCase class](aws-glue-api-pyspark-transforms-FormatCase.md) 
+  [FillWithMode class](aws-glue-api-pyspark-transforms-FillWithMode.md) 
+  [FlagDuplicateRows class](aws-glue-api-pyspark-transforms-FlagDuplicateRows.md) 
+  [RemoveDuplicates class](aws-glue-api-pyspark-transforms-RemoveDuplicates.md) 
+  [MonthName class](aws-glue-api-pyspark-transforms-MonthName.md) 
+  [IsEven class](aws-glue-api-pyspark-transforms-IsEven.md) 
+  [CryptographicHash class](aws-glue-api-pyspark-transforms-CryptographicHash.md) 
+  [Decrypt class](aws-glue-api-pyspark-transforms-Decrypt.md) 
+  [Encrypt class](aws-glue-api-pyspark-transforms-Encrypt.md) 
+  [IntToIp class](aws-glue-api-pyspark-transforms-IntToIp.md) 
+  [IpToInt class](aws-glue-api-pyspark-transforms-IpToInt.md) 

### Maven: Bundle the plugin with your Spark applications
<a name="aws-glue-programming-python-di-transforms-maven"></a>

 You can bundle the transforms dependency with your Spark applications and Spark distributions (version 3.3) by adding the plugin dependency in your Maven `pom.xml` while developing your Spark applications locally. 

```
<repositories>
   ...
    <repository>
        <id>aws-glue-etl-artifacts</id>
        <url>https://aws-glue-etl-artifacts.s3.amazonaws.com/release/ </url>
    </repository>
</repositories>
...
<dependency>
    <groupId>com.amazonaws</groupId>
    <artifactId>AWSGlueTransforms</artifactId>
    <version>4.0.0</version>
</dependency>
```

 You can alternatively download the binaries from AWS Glue Maven artifacts directly and include them in your Spark application as follows. 

```
#!/bin/bash
sudo wget -v https://aws-glue-etl-artifacts.s3.amazonaws.com/release/com/amazonaws/AWSGlueTransforms/4.0.0/AWSGlueTransforms-4.0.0.jar -P /usr/lib/spark/jars/
```