Traditional SQL Stored Procedure to Spark Conversion using AWS Glue

Introduction

As enterprises modernize their data infrastructure, they often encounter legacy SQL stored procedures deeply embedded in their data processing workflows. These stored procedures, while effective in traditional databases, can become bottlenecks when handling big data workloads. Transitioning to Spark-based processing using AWS Glue, Data Catalog, and Athena can provide scalability, cost-efficiency, and flexibility in handling large datasets. This article explores how to migrate SQL stored procedures to Spark on AWS Glue, leveraging AWS Data Catalog and querying with Athena.

Why Migrate from SQL Stored Procedures?

SQL stored procedures are tightly coupled with traditional relational databases, which can lead to:

• Scalability Limitations – RDBMS performance declines as data volume increases.
• High Licensing Costs – Commercial database licenses can be expensive for large workloads.
• Rigid Architectures – Hard to integrate with modern cloud data lakes and analytics tools.

AWS Glue and Spark enable distributed processing, making them ideal for handling large-scale ETL operations with better performance and cost benefits.

Migration Approach

Step 1: Analyze Existing SQL Stored Procedure

• Identify business logic, transformations, and dependencies.
• Extract SQL queries and procedural logic (loops, conditions, joins).

Step 2: Design the Spark-based Alternative

• Convert SQL transformations to PySpark DataFrame operations.
• Leverage AWS Glue for ETL workflows and Data Catalog for metadata management.
• Store transformed data in Amazon S3, making it accessible via Athena.

Step 3: Implement in AWS Glue

• Create an AWS Glue Job in PySpark.
• Read data from Amazon S3 or relational databases using AWS Glue Crawlers and Data Catalog.
• Transform data using Spark functions.
• Write processed data back to Amazon S3 in Parquet format for optimized querying.

Step 4: Query with Athena

• Use Amazon Athena to query the processed data from S3.
• Define partitions and optimize performance using AWS Glue Data Catalog.
• Validate results against the original stored procedure output.

Example Conversion

Traditional SQL Stored Procedure:

CREATE PROCEDURE ProcessSalesData()
BEGIN
    -- Step 1: Aggregate Sales Data
    INSERT INTO ProcessedSales
    SELECT region, SUM(sales_amount) AS total_sales
    FROM RawSales
    GROUP BY region;
    
    -- Step 2: Identify High Revenue Regions
    INSERT INTO HighRevenueRegions
    SELECT region FROM ProcessedSales WHERE total_sales > 100000;
END;        

Equivalent Spark-based AWS Glue Job in PySpark:

from pyspark.sql import SparkSession
from awsglue.context import GlueContext
from awsglue.dynamicframe import DynamicFrame

spark = SparkSession.builder.appName("SalesProcessing").getOrCreate()
glueContext = GlueContext(spark)

# Load Data from AWS Glue Data Catalog
df = glueContext.create_dynamic_frame.from_catalog(database="sales_db", table_name="raw_sales").toDF()

# Step 1: Aggregate Sales Data
processed_sales = df.groupBy("region").sum("sales_amount").withColumnRenamed("sum(sales_amount)", "total_sales")

# Step 2: Identify High Revenue Regions
high_revenue_regions = processed_sales.filter("total_sales > 100000")

# Save results back to S3
processed_sales.write.parquet("s3://your-bucket/processed_sales/")
high_revenue_regions.write.parquet("s3://your-bucket/high_revenue_regions/")        

Reference Architecture

Components:

1. AWS Glue Data Catalog – Metadata store for structured data.
2. AWS Glue Job (PySpark) – Performs ETL operations.
3. Amazon S3 – Stores raw and processed data.
4. Amazon Athena – Queries processed data.

Key Benefits

• Scalability – AWS Glue’s serverless Spark processing can handle large datasets efficiently.
• Cost Savings – Pay-as-you-go model reduces database licensing costs.
• Flexibility – Data is stored in an open format (Parquet/ORC) and queried on demand using Athena.

Conclusion

Migrating SQL stored procedures to Spark on AWS Glue modernizes data processing workflows, providing scalability, flexibility, and cost-effectiveness. With AWS Glue Data Catalog and Athena, organizations can efficiently manage metadata and execute queries without maintaining traditional databases.

Are you considering migrating your SQL stored procedures to AWS Glue and Spark? Let’s discuss in the comments!

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