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Dynamic vs. Static Frames in AWS Glue

   March 6, 2024
AWS Glue @ Freshers.in

AWS Glue, a fully managed extract, transform, and load (ETL) service, offers two distinct types of frames: dynamic and static. These frames serve different purposes and exhibit unique behaviors within the AWS Glue environment. In this article, we’ll explore the disparities between dynamic and static frames, providing comprehensive examples to illustrate their functionalities and use cases.

Understanding Dynamic and Static Frames in AWS Glue

Dynamic Frames

Dynamic frames in AWS Glue are representations of data that can change dynamically during the ETL process. They maintain schema flexibility, allowing the schema to evolve based on the data being processed. Dynamic frames are typically used when dealing with semi-structured or schema-less data formats, such as JSON or Avro.

Example of a dynamic frame in AWS Glue:

from awsglue.context import GlueContext
from pyspark.context import SparkContext
# Create a GlueContext
sc = SparkContext()
glueContext = GlueContext(sc)
# Read data as a dynamic frame
dynamic_frame = glueContext.create_dynamic_frame.from_catalog(database="database_name", table_name="table_name")

Static Frames

In contrast, static frames in AWS Glue maintain a fixed schema throughout the ETL process. The schema is defined upfront and remains unchanged, providing stability and predictability during data processing. Static frames are suitable for handling structured data formats, such as CSV or Parquet.

Example of a static frame in AWS Glue:

from pyspark.sql import SparkSession
# Create a SparkSession
spark = SparkSession.builder.appName("example").getOrCreate()
# Read data as a static frame
static_frame = spark.read.parquet("s3://bucket/path/to/data")

Key Differences and Use Cases

  1. Schema Flexibility:
    • Dynamic frames offer schema flexibility, allowing schemas to evolve based on the data.
    • Static frames maintain a fixed schema throughout the ETL process.
  2. Data Formats:
    • Dynamic frames are suitable for semi-structured or schema-less data formats.
    • Static frames are ideal for handling structured data formats with predefined schemas.
  3. Performance Considerations:
    • Dynamic frames may incur overhead due to schema inference and dynamic schema evolution.
    • Static frames offer better performance and optimization opportunities, as the schema is predefined.

Examples

Let’s consider practical examples to demonstrate the differences between dynamic and static frames in AWS Glue:

  1. Dynamic Frame Example:
    • Suppose you’re processing JSON data from various sources, each with its own schema evolution over time. Using dynamic frames allows you to handle this variability seamlessly.
  2. Static Frame Example:
    • If you’re dealing with structured data stored in a relational database or Parquet files with a fixed schema, static frames provide stability and performance benefits.
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