Master ETL on AWS with Glue DynamicFrames: A Beginner’s Guide

In the world of big data, extract-transform-load (ETL) operations are vital to move and process data across platforms.

Is AWS Glue ETL or ELT?

AWS Glue is a serverless ETL service that makes it easy to prepare and transform data for analytics.

Dynamic frames in Glue

One of the key features of AWS Glue is the use of DynamicFrames—a powerful abstraction designed to simplify data transformation and schema handling.

We’ll explore what DynamicFrames are, how they differ from Spark DataFrames, and how you can use them to build a scalable, schema-flexible ETL pipeline on AWS.

📌 What are DynamicFrames in Glue?

A DynamicFrame is a distributed table that supports nested data structures and is built on top of Apache Spark.

Unlike Spark’s DataFrames, which enforce a schema, DynamicFrames are schema-relaxed, which makes them ideal for semi-structured or evolving data (like JSON or Parquet).

Key benefits:

• Handles schema inconsistencies and evolution gracefully
• Offers transformation functions optimized for AWS Glue
• Integrates well with AWS Glue crawlers and the Data Catalog

🛠️ Setting Up an ETL Pipeline

Let’s walk through an example ETL process that:

1. Reads data from Amazon S3
2. Cleans/transforms the data using DynamicFrame operations
3. Writes the output to Amazon Redshift or back to S3

1️⃣ Create a Crawler to Catalog Your Data

Use an AWS Glue crawler to crawl your source data (e.g., CSV or JSON files in S3) and create a table in the AWS Glue Data Catalog. This allows your ETL job to reference the table directly using a database and table name.

2️⃣ Define the Glue Job

You can use AWS Glue Studio or a script-based job (Python shell or PySpark). Here’s a PySpark example using DynamicFrames:

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.dynamicframe import DynamicFrame

# Get job arguments
args = getResolvedOptions(sys.argv, ['JOB_NAME'])

# Initialize Spark and Glue contexts
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session

# Create DynamicFrame from the Data Catalog
datasource = glueContext.create_dynamic_frame.from_catalog(
    database="your_database",
    table_name="your_table",
    transformation_ctx="datasource"
)

# Apply transformation - drop null fields
transformed = DropNullFields.apply(frame=datasource)

# Rename a field
transformed = RenameField.apply(
    frame=transformed,
    old_name="old_column_name",
    new_name="new_column_name"
)

# Convert to DataFrame if needed
df = transformed.toDF()

# Optional: Filter records using Spark
df_filtered = df.filter(df["status"] == "active")

# Convert back to DynamicFrame
final_dynamic_frame = DynamicFrame.fromDF(df_filtered, glueContext, "final_dynamic_frame")

# Write back to S3 in Parquet format
glueContext.write_dynamic_frame.from_options(
    frame=final_dynamic_frame,
    connection_type="s3",
    connection_options={"path": "s3://your-bucket/cleaned-data/"},
    format="parquet"
)

🔄 DynamicFrame vs DataFrame: When to Use What?

Feature	DynamicFrame	Spark DataFrame
Schema enforcement	Loose (schema-relaxed)	Strict
Suitable for nested data	✅ Yes	✅ Yes
AWS Glue transformations	✅ Optimized	❌ Not available
Spark transformations	❌ Not directly	✅ Fully supported

Use DynamicFrames when ingesting raw or semi-structured data, and convert to DataFrames when you need advanced Spark transformations (e.g., joins, aggregations).

Recommended book: AWS Glue ETL

🧩 Tips for Working with DynamicFrames

• Use .printSchema() to inspect inferred schema.
• Use .resolveChoice() to handle ambiguous column types.
• Use .apply_mapping() to rename and cast types.
• Convert to DataFrame for fine-grained control, then back to DynamicFrame for AWS Glue output.

✅ Conclusion

AWS Glue DynamicFrames are a powerful abstraction that simplify ETL pipelines for semi-structured data.

By combining the flexibility of Spark with AWS Glue’s automation and serverless architecture, you can build scalable, cost-efficient ETL workflows.

Whether you’re migrating logs, transforming JSON data, or preparing datasets for analytics, DynamicFrames offer a developer-friendly way to handle schema evolution and perform complex data transformations with ease.