AWS Glue

Operates without requiring users to provision, manage, or scale servers, automatically handling infrastructure for data integration workloads.

Provides a centralized, persistent metadata repository that stores schemas, table definitions, and locations of data across AWS, on-premises, and other clouds, making data easily discoverable.

Utilizes "crawlers" to automatically connect to data sources, infer schemas, and populate the Data Catalog, including detecting partitions and schema evolution.

Offers a drag-and-drop graphical interface for visually building, running, and monitoring ETL workflows without extensive coding.

Allows data engineers to write and customize ETL scripts using Apache Spark (PySpark or Scala) in a flexible development environment.

Features AI-powered assistance for generating ETL code from natural language descriptions, modernizing Apache Spark jobs (e.g., upgrading versions), and accelerating Spark troubleshooting with intelligent diagnostics and root cause analysis.

Automatically scales compute resources to process data from gigabytes to petabytes, adapting to workload demands without manual intervention.

Minimizes administrative overhead by providing serverless data pipelines with built-in scheduling, monitoring, and auto-scaling.

Connects to over 100 diverse data sources, including various AWS services (S3, RDS, Redshift, DynamoDB, Kinesis, MSK), on-premises databases (MySQL, Oracle, SQL Server, PostgreSQL), and SaaS applications (Salesforce, SAP).

Supports various data processing patterns, including Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT), for batch, micro-batch, and streaming workloads.

Enables data engineers and scientists to interactively explore, experiment with, and prepare data using their preferred Integrated Development Environments (IDEs) or notebooks, powered by a scalable Apache Spark backend.

Seamlessly integrates data preparation and ETL capabilities directly into the Amazon SageMaker environment, including SageMaker Unified Studio and Visual ETL.

Provides detailed API usage metrics for the Data Catalog in CloudWatch, allowing users to monitor, troubleshoot, and optimize their lakehouse runtime API usage.

Supports full read and write (DML operations like CREATE, ALTER, DELETE, UPDATE, MERGE INTO) access for AWS Glue 5.0 Apache Spark jobs on AWS Lake Formation registered tables when the job role has full table access.

Offers managed compaction, sort, and z-order compaction for Apache Iceberg tables in Amazon S3 via the AWS Glue Data Catalog, automatically removing old data files and improving query performance.

AWS Glue Studio now supports more compressed file types (LZ4, SNAPPY, DEFLATE, etc.), Excel files as sources, and XML/Tableau Hyper files as targets, along with the option to specify the number of output files (including single file output).

Streamlines the scheduling of Visual ETL flows and queries directly from the SageMaker interface using Amazon EventBridge Scheduler.

Supports "history mode" for zero-ETL integrations with eight third-party SaaS applications and various AWS databases, allowing tracking of historical data changes for analytics without traditional ETL processes.

Provides ML-powered transformations like FindMatches for record deduplication and entity resolution.

Allows setting up time-based (cron expressions) or event-based triggers for ETL jobs and orchestrating complex workflows.

Provides tools and capabilities to clean and prepare data, including handling missing values, standardizing column names, and converting data types.

Permits profiling of datasets for increased organization and classification, both manually and with machine learning.

Integrates with AWS IAM and Lake Formation for fine-grained access control, data encryption, and auditing.

Provides a dedicated environment for developing and testing ETL scripts interactively using notebooks.