Managing Schema Evolution in AWS Glue Data Catalog

Introduction

In the fast-paced world of cloud-based data lakes, where new data streams arrive every second, the structure of data, its schema, is rarely static. Fields are added, removed, renamed, or shifted as business needs change. This phenomenon is known as schema evolution, and if not handled properly, it can break your ETL jobs, corrupt downstream analytics, or expose your platform to data quality issues.

This is where AWS Glue Data Catalog becomes invaluable. In this detailed blog post, we will dive deep into managing schema evolution effectively using AWS Glue’s capabilities, ensuring your data pipelines remain resilient, automated, and analytics-ready.

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Understanding the Challenge: Schema Evolution in Data Lakes

Let’s consider a classic use case. You’re ingesting transactional data from an e-commerce platform into Amazon S3, partitioned by year, month, and day. Over time, the application team introduces new fields like promo_code and decides to change the data type of the discount from int to float.

In a traditional RDBMS environment, schema changes are tightly controlled and handled via migrations. But in a schema-on-read architecture like a data lake, especially when using semi-structured formats (e.g., JSON or Parquet), these schema changes can silently propagate through your pipeline.

Without a proper metadata management strategy:

Query engines like Amazon Athena may fail due to mismatched schemas
ETL jobs might process incorrect or incomplete data
Business reports could become unreliable

That’s why managing schema evolution is not optional, and it’s foundational.

AWS Glue Data Catalog

The AWS Glue Data Catalog is a fully managed, centralized metadata store that acts as the brain of your data lake. It allows you to register metadata about your datasets, track schema versions, and share table definitions across services like Amazon Athena, Amazon Redshift Spectrum, Amazon EMR, and Lake Formation.

Key capabilities of the AWS Glue Data Catalog that relate to schema management include:

Table and column-level metadata storage
Partitioning information
Schema versioning and rollback support
Integration with AWS Glue Crawlers, Athena, and Lake Formation

It is tightly integrated with AWS Glue ETL jobs, enabling seamless schema-aware transformations at scale.

Automating Schema Detection with AWS Glue Crawlers

AWS Glue Crawlers are serverless components that scan your data, extract metadata, and populate the Glue Data Catalog. They can detect column names, types, and partition structures from various file formats such as Parquet, JSON, ORC, and CSV.

glue

When new data arrives in Amazon S3 with a changed schema, say, a new column is added, the next crawler run compares the updated schema with the existing one. Based on the schema change policy you configure, AWS Glue decides whether to:

Update the catalog with the new schema,
Ignore the changes, or
Log them for review.

This mechanism is critical for automating schema evolution but must be carefully controlled. Over-eager updates can cause inconsistent schema versions across partitions, especially in open table formats like Hive-compatible Amazon S3 directories.

Handling Schema Evolution in ETL Jobs

While Crawlers automate metadata updates, your ETL jobs must still gracefully handle schema drift. This is especially true when:

Optional fields are sometimes missing
Data types differ across partitions (e.g., timestamp vs. string)
Columns need transformation or casting

With AWS Glue Spark jobs (PySpark or Scala), you can programmatically inspect the schema and apply dynamic transformations.

Here’s an example in PySpark:

from awsglue.dynamicframe import DynamicFrame
from pyspark.sql.functions import col

# Read from Glue Catalog
dyf = glueContext.create_dynamic_frame.from_catalog(database="sales_db", table_name="orders")

# Convert to DataFrame for flexibility
df = dyf.toDF()

# Handle missing 'promo_code' column
if 'promo_code' not in df.columns:
    df = df.withColumn("promo_code", lit(None).cast("string"))

# Ensure 'discount' is float
df = df.withColumn("discount", col("discount").cast("float"))

# Back to dynamic frame
dyf_transformed = DynamicFrame.fromDF(df, glueContext, "dyf_transformed")

from awsglue.dynamicframe import DynamicFrame

from pyspark.sql.functions import col

# Read from Glue Catalog

dyf = glueContext.create_dynamic_frame.from_catalog(database="sales_db", table_name="orders")

# Convert to DataFrame for flexibility

df = dyf.toDF()

# Handle missing 'promo_code' column

if 'promo_code' not in df.columns:

df = df.withColumn("promo_code", lit(None).cast("string"))

# Ensure 'discount' is float

df = df.withColumn("discount", col("discount").cast("float"))

# Back to dynamic frame

dyf_transformed = DynamicFrame.fromDF(df, glueContext, "dyf_transformed")

This method ensures your ETL logic doesn’t break when the schema evolves gradually over time.

Version Control and Rollbacks in Schema Evolution

Every time a schema is updated, manually or via crawler, AWS Glue stores a new version of the schema. This versioning system is powerful for:

Auditing who changed what and when
Debugging failed jobs due to unexpected changes
Rolling back to a known good schema if needed

You can view schema version history directly in the AWS Console or via the Boto3 SDK.

glue2

Example using AWS CLI

aws glue get-schema-version-history --schema-id SchemaName=my-schema-name

1	aws glue get-schema-version-history --schema-id SchemaName=my-schema-name

This approach makes schema management traceable and reversible, essential for data governance and compliance in enterprise environments.

Real-World Example: Evolving Sales Data Pipeline

Let’s say your pipeline processes sales orders. Initially, the data looks like this:

{
  "order_id": 1234,
  "product_id": "X01",
  "quantity": 2,
  "discount": 10
}

{

"order_id": 1234,

"product_id": "X01",

"quantity": 2,

"discount": 10

}

Later, you add a new promo_code field, and the discount changes from an integer to a float:

{
  "order_id": 1235,
  "product_id": "X02",
  "quantity": 1,
  "discount": 5.5,
  "promo_code": "NEWYEAR50"
}

{

"order_id": 1235,

"product_id": "X02",

"quantity": 1,

"discount": 5.5,

"promo_code": "NEWYEAR50"

}

If you:

Use Parquet format
Configure the crawler to UPDATE_IN_DATABASE
Use casting logic in Glue jobs

Then your ETL will handle this evolution without issue.

But if you:

Use JSON or CSV
Have no schema validation
Depend solely on Athena queries

Then schema evolution will likely break your pipeline, resulting in failed queries or data corruption.

Conclusion

Managing schema evolution in AWS Glue is not just about detecting changes but also about building resilience into your data platform. You can create pipelines that adapt to changing data without compromising data quality or performance using the AWS Glue Data Catalog, crawlers, versioning, and dynamic transformations.

With the right practices and tools from AWS Glue, you can tame even the most unpredictable data sources and deliver consistent, analytics-ready datasets to your users.

Drop a query if you have any questions regarding AWS Glue and we will get back to you quickly.

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