Extract, Transform, Load Example: S3

In today's big data landscape, extracting, transforming, and loading (ETL) data has become an essential process for various industries. One common use case involves reading files from a data source, such as Amazon S3, and capturing the input file name in the ETL job. In this article, we will explore how to achieve this use case using Spark's DataFrames.

Crawling S3 Input Path

The first step is to crawl the S3 input path using Glue Crawler. This process involves creating a Glue Data Catalog table and specifying the S3 path as the data source. The crawler will then extract the files from the specified path and store them in the Glue Data Catalog.

Loading Files to S3 Bucket Prefix

Once the files are crawled, we need to load them into an S3 bucket prefix. This can be achieved using AWS Glue's built-in functionality. By specifying the S3 bucket prefix as the target location, Glue will upload the files to the specified prefix.

Creating Multiple JSON Files

The next step is to create multiple JSON files, each containing an individual JSON record. To achieve this, we can use Spark's DataFrames and its `withColumn` method to add a new column to the DataFrame containing the file name of every data record.

Glue ETL Script

The following Glue ETL script was used to achieve this use case:

```python

import awsglue.utils

from awsglue.dynamicframe import DynamicFrame

from awsglue.context import GlideContext

args = getResolvedOptions(sys.argv, '')

datasource0 = GlideContext.create_dynamic_om_catalog(database='default', table_name='', transformation_ctx='datasource0')

```

Using Spark's DataFrames

As Glue executes Spark jobs in the backend, we can leverage the use of Spark's DataFrames to handle Glue's limitations. However, there are certain use cases that cannot currently be achieved by Glue and its DynamicFrame implementations.

In this article, we demonstrated how to extract, transform, and load data from S3 using Spark's DataFrames. We also highlighted the importance of crawling S3 input paths and loading files to an S3 bucket prefix. By leveraging AWS Glue's built-in functionality and Spark's DataFrames, we can achieve complex ETL use cases that cannot be handled by traditional ETL tools.

* Use Spark's DataFrames to add a new column containing the file name of every data record.

* Crawl S3 input paths using Glue Crawler.

* Load files to an S3 bucket prefix using AWS Glue's built-in functionality.

* Leverage AWS Glue's ETL platform and its various templates to achieve complex use cases.

By following these best practices, you can ensure a successful ETL process and extract valuable insights from your big data.

Extract transform load example s3