Features
Decoupled compute and storage on S3
Real-time streaming ingestion via Kafka
Change data capture via Flink and Debezium
Built-in Python sandbox for ML workflows
Vector and full-text search
BI tool integrations (Metabase, Grafana, Tableau, Superset, Redash)
dbt and Airbyte ELT integration
JDBC/SQLAlchemy driver for Python, Go, Java, Node.js, Rust
Multi-modal analytics: BI, search, AI, geo
SageMaker AI for full ML lifecycle (build, train, deploy)
SageMaker Unified Studio for analytics and AI development
Amazon SageMaker Catalog for data and AI governance
Lakehouse architecture unifying S3, Redshift, and federated sources
Zero-ETL integrations for near real-time data ingestion
HyperPod for distributed training of large models
JumpStart for pre-built models and solutions
MLOps tools for model management and versioning
Amazon Q Developer for natural language productivity
Federated query across third-party data sources
Fine-grained access controls and permissions
Built-in data quality monitoring and lineage
Apache Iceberg compatibility for open table formats
Bedrock integration for generative AI applications
Web Search on Bedrock AgentCore for grounded agent responses
