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Unify streaming and analytical data with Amazon Data Firehose and Amazon SageMaker Lakehouse
- To bridge the gap between streaming data and analytical workloads, organizations can leverage Amazon Data Firehose and Amazon SageMaker Lakehouse.
- Streaming data enables real-time insights and dynamic responses crucial for applications needing immediate, adaptable responses.
- Amazon Data Firehose simplifies streaming data delivery to various data platforms with automatic scaling and real-time delivery.
- Amazon SageMaker Lakehouse unifies data sources, providing flexibility and access to Iceberg-compatible tools for analytics.
- By using SageMaker Lakehouse, organizations can combine Iceberg's capabilities with cloud scalability for improved analytics workflows.
- The integration removes barriers between data storage and ML processes, allowing direct work with Iceberg tables.
- The solution demonstrates creating Iceberg tables in SageMaker Unified Studio and streaming data via Firehose for collaboration across teams.
- AWS CloudFormation templates help with setting up resources for Firehose to deliver streaming data to Iceberg tables.
- Prerequisites include an AWS account, SageMaker Unified Studio domain, and creation of a demo project for this walkthrough.
- Steps involve creating Iceberg tables, deploying necessary resources, setting up a Firehose stream, and generating and querying streaming data.
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