AWS Glue Features

Data Governance (3)

User Access Management: Allows administrators to assign role-based user access for specific data sets
Dynamic Data Masking: Hides and masks sensitive data automatically based on user permissions
Data Lineage: Provides historical insights into original data sources and transformations made to data sets

Search: Offers simple search capabilities to discover specific data sets
Data Quality and Cleansing: Allows users and administrators to easily clean data to maintain quality and integrity
Data Transformation: Converts data formats of source data into the format required for the reporting system without mistakes
Data Modeling: Tools to (re)structure data in a manner that allows extracting insights quickly and accurately

Commenting: Allows users to comment on data sets to help future users better interact and interpret the data
Profiling and Classification: Permits profiling of data sets for increased organization, both by users and machine learning
Business and Data Glossary: Creates a business glossary for faster understanding by the average business user
Metadata Management: Indexes metadata descriptions for easier searching and enhanced insights

Breadth of Data Sources: Provides a wide range of possible data connections, including cloud applications, on-premise databases, and big data distributions, among others
Ease of Data Connectivity: Allows businesses to easily connect to any data source
API Connectivity: Offers API connections for cloud-based applications and data sources

Profiling and Classification: Permits profiling of data sets for increased organization, both by users and machine learning
Metadata Management: Indexes metadata descriptions for easier searching and enhanced insights
Data Modeling: Tools to (re)structure data in a manner that enables quick and accurate insight extraction
Data Joining: Allows self-service joining of tables
Data Blending: Provides the ability to combine data sources into one data set
Data Quality and Cleansing: Allows users and administrators to easily clean data to maintain quality and integrity
Data Sharing: Offers collaborative functionality for sharing queries and data sets
Data Governance: Ensures user access management, data lineage, and data encryption

Breadth of Integrations: Provides a wide range of possible integrations, including analytics, data integration, master data management, and data science tools
Ease of Integrations: Allows businesses to easily integrate with analytics, data integration, master data management, and data science tools
Data Workflows: Operationalizes data workflows to easily scale repeatable preparation needs

Database Management: Supports for managing different types of databases and integration methods.
Access Control: Gives admistrators control over data-related privilages and accessability
Data Security: Allows users to secure access to data. Provides additional support for security practices like IP whitelisting, attack mitigation, and data encryption.

Architecture: Creates integrations and allows management of connected contains.
Provisioning: Allows administrators to organize components of the database to maintain instance efficiency.
Orchestration: Allows admisistrators to manage and organize storage, networks, and datacenters.
API / Integrations: Application Programming Interface Specification for how the application communicates with other software. APIs typically enable integration of data, logic, objects, etc. with other software applications.

Data Visualization: Processes data and represents interpretations in a variety of graphic formats.
Application Performance: Functions when integrated with multiple forms of external applications.
Data Modeling: Tools to (re)structure data in a manner that allows extracting insights quickly and accurately.

Reporting: View ETL process data via reports and visualizations like charts and graphs.
Auditing: Record ETL historical data for auditing and potential data correction needs.
Real-Time Analytics: Track data and system backups, replication, and failover events.
Solution Integration: Integrate with other management, resilience, or backup solutions
Infrastructure Compatibility: Be compatible with a variety of cloud and/or virtual infrastructure.
Monitoring: Monitor replication environment via GUI

Extraction: Extract data from the designated source(s) like relational databases, JSON files, and XML files.
Transformation: Cleanse and re-format extracted data to the needed target format.
Loading: Load reformatted data into target database, data warehouse, or other storage location.
Automation: Arrange ETL processes to occur automatically on needed time schedule (e.g., daily, weekly, monthly).
Scalability: Capable of scaling processing power up or down based on ETL volume.

System Failover: Provide as-before failover capabilities for cloud environments.
Pay by Usage: Services are offered under a pay-as-you-go or utilization-based purchase model.
Service Provision: Offer disaster recovery capabilities as a third-party provider.