Data Transformation & Feature Engineering

• Use Data Wrangler for visual EDA that exports into a pipeline
• Use Glue DataBrew for no-code cleaning with reusable recipes
• Use AWS Glue for serverless, scripted, repeatable Spark ETL
• Choose EMR over Glue only when you need cluster control
• Glue FindMatches deduplicates records that are imperfect matches
• One-hot encode low-cardinality nominal categories
• Use ordinal or label encoding only when a real rank exists
• High-cardinality categoricals need binary, hashing, or embeddings
• Standardize with the mean and variance, normalize to a fixed range
• Skip scaling for tree-based models
• Impute missing values with a statistic learned from training data
• Detect outliers with IQR or standard-deviation rules, then cap or transform
• Feature splitting and binning expose hidden structure
• Feature Store online store serves the latest record at ms latency
• Feature Store offline store keeps full history in S3 for training
• Write both Feature Store stores to kill train-serve skew
• A feature group needs a record identifier and an event time
• Use Ground Truth to label data, choosing one of three workforces
• Ground Truth automated data labeling uses active learning to cut cost
• Transform streaming records with Lambda for per-record work
• Data Wrangler can export an engineered flow straight into Feature Store
• SageMaker Processing reads and writes only under /opt/ml/processing/
• SageMaker Processing exposes job and cluster config in /opt/ml/config JSON files
• ShardedByS3Key splits objects across instances; FullyReplicated copies all data to each
• Use a framework processor for built-in libraries and ScriptProcessor for a custom container
• Glue job bookmarks with transformation_ctx process only new data incrementally
• Glue DynamicFrames encode mixed-type fields as choice types, resolved with resolveChoice
• Glue ETL updates the Data Catalog itself with enableUpdateCatalog + UPDATE_IN_DATABASE
• Use Apache Iceberg for the Feature Store offline store to get time-travel and compaction
• Backfill the offline store directly with the Feature Store Spark connector, target_stores=OfflineStore
• Data Wrangler custom transforms should use PySpark (not pandas) for large datasets
• Data Wrangler Quick Model rates predictive power; Target Leakage flags features unavailable at prediction time
• DataBrew recipes use named steps for imputation, scaling, and encoding

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