DynamicFrames vs. DataFrames: Mastering Data Structures in AWS Glue
Maialen | April 12, 2025In the world of AWS Glue, developers often find themselves at a crossroads when writing PySpark ETL jobs. Should you stick to the native Apache Spark DataFrame, or should you embrace the AWS-proprietary Glue DynamicFrame?
While they may look similar on the surface (both represent distributed collections of data) they are optimized for different scenarios in the data pipeline. Understanding when to use which is the key to building resilient, cost-effective, and scalable ETL processes.
The Contenders
Apache Spark DataFrame
The Spark DataFrame is the industry standard. It is a distributed collection of data organized into named columns, conceptually equivalent to a table in a relational database.
- Engine: Powered by the Catalyst Optimizer, making it incredibly efficient for complex computations.
- Schema: Requires a fixed schema. If a row doesn’t fit the defined schema during a read operation, Spark typically drops the row or throws an error (depending on the
mode).
AWS Glue DynamicFrame
The DynamicFrame is an abstraction specific to AWS Glue. It is designed to handle messy, semi-structured data (like nested JSON) where schemas might be inconsistent or evolving.
- Engine: It doesn’t use the Catalyst Optimizer directly for transformations but offers specialized Glue transforms.
- Schema: It is “self-describing”. It does not enforce a rigid schema on every row immediately. This allows it to hold heterogeneous data in the same dataset without crashing.
Key Advantages
Advantages of DynamicFrames
- Schema Flexibility: The “killer feature” of DynamicFrames is the ability to handle unexpected changes. If a column appears as an
Integerin one row and aStringin another, a DataFrame would likely fail. A DynamicFrame creates aChoicetype, preserving both values for you to resolve later. - No Upfront Schema Required: You don’t need to specify the schema when reading data; Glue infers it on the fly, which is ideal for “Schema-on-Read” architectures.
- Advanced Glue Transforms: Access to specialized transforms like
ResolveChoice(to clean up mixed types),Unbox(to parse string-encoded JSON into a proper struct), andRelationalize(to flatten nested structures). - Catalog Integration: Native integration with the AWS Glue Data Catalog makes reading from and writing to governed tables seamless.
Advantages of Spark DataFrames
- Performance: Spark DataFrames are significantly faster for heavy transformations (aggregations, joins, window functions) because the Catalyst Optimizer generates an optimized physical execution plan.
- Rich Ecosystem: You have access to the full breadth of the Spark SQL library, MLlib for machine learning, and standard SQL syntax.
- Portability: Code written with DataFrames is portable. You can run it on EMR, Databricks, or a local Spark cluster. DynamicFrames lock you into AWS Glue.
Best Practices: When to Use Which?
The most effective AWS Glue jobs often use both. Here is the breakdown of use cases:
Use DynamicFrames When:
- Ingesting Data (Extract): When reading raw data from S3, especially JSON or XML where the structure might change or is highly nested.
- Handling “Dirty” Data: When you expect type mismatches (e.g., a “User ID” that is sometimes
123and sometimes"123A"). You can use the.resolveChoice()method to fix this explicitly. - Writing Data (Load): DynamicFrames handle connections to JDBC targets (like Redshift or RDS) and Glue Catalog tables very robustly, handling error retries and connection properties automatically.
Use Spark DataFrames When:
- Heavy Processing (Transform): Once your data is cleaned and flattened, convert it to a DataFrame for aggregation, complex filtering, or joining multiple datasets. The performance gain is substantial.
- Using SQL: If you prefer writing SQL queries (
spark.sql("SELECT * FROM...")), you must convert to a DataFrame/View first. - Feature Engineering: If you are preparing data for machine learning models using Spark MLlib.
The “Sandwich” Pattern
For most production-grade ETL jobs, the hybrid approach, often called the “Sandwich Pattern”, is the gold standard:
- Read as DynamicFrame: Load raw data using Glue’s readers to handle schema evolution safely.
- Convert to DataFrame: Use the
.toDF()method to convert the data. - Process efficiently: Perform your heavy-lifting (joins, group bys) using native Spark power.
- Convert back to DynamicFrame: Use
DynamicFrame.fromDF()to switch back. - Write as DynamicFrame: Use Glue’s writers to commit the data to the Data Catalog or S3.
Conclusion
Neither structure is strictly “better”; they are tools optimized for different stages of the pipeline. DynamicFrames prioritize data integrity and flexibility during ingestion and egress, while Spark DataFrames prioritize computational speed and analytical depth.
By mastering the toggle between .toDF() and .fromDF(), you can harness the flexibility of AWS Glue without sacrificing the raw performance of Apache Spark.