The Power of Unified Data Analysis
In today’s data-driven landscape, organizations need seamless access to their data across different storage systems to drive insights and gain competitive advantages. Amazon SageMaker Lakehouse now offers enhanced capabilities through integration with Amazon S3 Tables—the first cloud object store with built-in Apache Iceberg support.
This integration provides unified access to S3 Tables, general purpose Amazon S3 buckets, Amazon Redshift data warehouses, and other data sources like Amazon DynamoDB or PostgreSQL. Users can query, analyze, and join data using Redshift, Amazon Athena, Amazon EMR, and AWS Glue, while enjoying the flexibility to use SQL or Spark-based tools of their choice.
Why Unified Data Access Matters
Consider a retail company that initially stored customer sales and churn data in a data warehouse for business intelligence reporting. As the business expands, they face the challenge of managing various data sources and exponential data growth. By building a data lake using Apache Iceberg, they can store new data types such as customer reviews and social media interactions.
While this enables personalized marketing campaigns, data spread across data lakes and warehouses creates inefficiencies:
- Requires specialized connectors
- Necessitates managing multiple access policies
- Often leads to costly data duplication
SageMaker Lakehouse solves these challenges by providing secure, centralized management of data across sources with fine-grained permissions consistently applied across all analytics engines.
Real-World Implementation Example
Example Retail Corp wants to understand customer behavior across thousands of touchpoints for millions of customers. Their data administrator, Alice, implements S3 Tables with Iceberg transactional capability to handle billions of streaming customer interactions, while maintaining the durability and performance of S3.
Alice supports a team including:
- Bob (Data Analyst) – Builds daily customer interaction reports
- Charlie (BI Analyst) – Creates interactive dashboards for sales teams
- Doug (Data Engineer) – Develops ML forecasting models
By implementing SageMaker Lakehouse, each team member can work with their preferred tools—Bob with Athena, Charlie with QuickSight and Redshift, and Doug with Apache Spark—while accessing a unified data source.
Implementation Steps
The implementation consists of four key steps:
1. Creating a table bucket with S3 Tables for customer details and enabling integration with AWS analytics services
2. Publishing existing Redshift data warehouse tables to the AWS Glue Data Catalog for unified access
3. Creating a SageMaker Unified Studio project with appropriate user access
4. Onboarding S3 Tables and Redshift tables to SageMaker Unified Studio with proper permissions through Lake Formation
Benefits in Action
Once implemented, the benefits become immediately apparent:
The data analyst can join data across S3 Tables and Redshift in Athena, enabling comprehensive customer churn analysis for leadership reports.
The BI analyst can query Redshift data with fine-grained column-level permissions, creating QuickSight dashboards for thousands of sales team members.
The data engineer can leverage Spark SQL to process data from both sources, building powerful forecasting models for customer growth and retention.
Security and Governance
The solution doesn’t compromise on security. Using AWS Lake Formation, Alice defines fine-grained permissions that control who can access which data. For example, she grants full table access to S3 Tables data but restricts access to only specific columns in the Redshift data warehouse, ensuring sensitive information remains protected.
This approach allows each user to access only the data they need while providing enough flexibility to create meaningful analytics and ML models that drive business insights.
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