Feature Engineering at Scale with AWS Glue and SageMaker

Feature engineering at scale is crucial for building robust machine learning (ML) models, especially when dealing with large and complex datasets. AWS provides powerful tools—AWS Glue for data integration and Amazon SageMaker for ML—that can be combined to streamline and scale feature engineering workflows.

Here's an overview of how to perform Feature Engineering at Scale using AWS Glue and SageMaker:

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🔹 1. Why Use AWS Glue and SageMaker Together?

• AWS Glue: A serverless ETL service used to extract, clean, and transform data from multiple sources.

• Amazon SageMaker: A fully managed ML service with built-in tools for model building, training, and deployment.

• Integration Advantage: Glue handles large-scale data prep, and SageMaker uses that cleaned data for feature engineering and modeling.

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🔹 2. Workflow Overview

1. Ingest and Clean Data with AWS Glue

2. Transform and Store Features

3. Feature Store Management with SageMaker Feature Store

4. Train Models with Processed Features

5. Deploy and Monitor

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🔹 3. Step-by-Step Breakdown

🧩 Step 1: Data Ingestion & Transformation (AWS Glue)

• Use Glue Crawlers to automatically discover schema.

• Write Glue Jobs (PySpark) to clean and preprocess data.

• Example: Handle missing values, normalize text, or calculate aggregates.

python
# Sample Glue PySpark code
import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session

# Load data
df = glueContext.create_dynamic_frame.from_catalog(database="mydb", table_name="raw_sales")

# Transform
transformed_df = df.toDF().withColumn("log_sales", log("sales"))

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🧩 Step 2: Store Transformed Features

• Save the transformed data in Amazon S3 in Parquet/ORC format.

• Optionally use Glue Data Catalog to register the output table for querying.

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🧩 Step 3: SageMaker Feature Store

• Use SageMaker Feature Store to manage and reuse features across models.

• Create an offline store for historical data and online store for real-time inference.

python
from sagemaker.feature_store.feature_group import FeatureGroup

feature_group = FeatureGroup(name="customer_features", sagemaker_session=sagemaker_session)

# Define schema and ingest features
feature_group.create(...)
feature_group.ingest(data_frame=transformed_df, max_workers=3, wait=True)

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🧩 Step 4: Model Training with SageMaker

• Load feature-engineered data from Feature Store or directly from S3.

• Train using built-in algorithms, custom Docker images, or notebooks.

python
from sagemaker.sklearn.estimator import SKLearn

sklearn = SKLearn(entry_point='train.py',
                  role=role,
                  instance_type='ml.m5.large')

sklearn.fit({'train': 's3://bucket/processed/train.csv'})

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🧩 Step 5: Deployment and Monitoring

• Deploy models via SageMaker Endpoint.

• Monitor performance using CloudWatch, and retrain as needed with updated features.

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🔹 4. Benefits of This Architecture

• Scalability: Process terabytes of data using Glue.

• Automation: Schedule ETL jobs and pipelines with triggers.

• Reusability: Centralized Feature Store avoids duplication.

• Integration: Seamless pipeline from raw data to deployed model.

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🔹 5. Bonus: Automate with AWS Step Functions

Combine Glue + SageMaker + S3 + Feature Store into a Step Functions pipeline to orchestrate the entire workflow automatically.

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