Data Foundation with Apache Iceberg

Key Components: The architecture integrates Apache Iceberg with compute engines such as Spark, Flink, and Kafka. Metadata management is handled by Hive, AWS Glue, and Unity Catalog, while query engines include Trino, Athena, and SparkSQL. Storage is provided by Amazon S3, and orchestration is managed through Airflow and AWS Step Functions.

Integration Points: Key integrations include AWS Glue for metadata management, Amazon S3 for scalable and secure storage, AWS Lake Formation for data governance, AWS Athena for serverless querying, and AWS Step Functions for orchestrating data pipelines.

Supported AWS Services: The platform supports a broad set of AWS services including Amazon S3, AWS Glue, AWS Lake Formation, Amazon Athena, Amazon EMR (including EMR on EKS), Amazon Kinesis, AWS Lambda, AWS Step Functions, and AWS IAM for security and access control.

Use Cases: Typical use cases involve enabling open data lakehouse architectures, migrating from legacy data lakes or proprietary platforms, unifying real-time and batch data, and enforcing metadata-driven governance and access control policies.

Customer Pain Points Addressed: The solution addresses challenges such as vendor lock-in and lack of interoperability, inflexible schema evolution and data versioning, inefficient separation of batch and streaming workloads, and insufficient governance and security mechanisms.

Industry-Specific Applications: Industry applications include financial services for regulatory compliance and fraud detection, retail for real-time inventory and recommendation systems, healthcare for secure and governed patient data analytics, and manufacturing for predictive maintenance and supply chain optimization.