Business and Technical Background Business and Technical Background In today’s wave of digital transformation, enterprises are facing an explosive growth of massive data. Especially in critical scenarios such as data lake construction, BI analytics, and AI/ML data preparation, they require an efficient and scalable large-scale data storage solution. These scenarios often demand storage systems that can handle PB-to EB-scale data while supporting transactional operations to ensure consistency, atomicity, and isolation—thus preventing data corruption or loss. Against this backdrop, Apache Iceberg emerged as an advanced open-source data lake table format. It provides reliable metadata management, snapshot isolation, and schema evolution capabilities, and has been widely adopted by technology giants such as Netflix, Apple, and Adobe. Iceberg has now established itself as the leader in the data lake domain. According to industry reports, its adoption rate has been steadily increasing over the past few years, making it a de facto standard for building modern data infrastructure. Apache Iceberg de facto standard for building modern data infrastructure Despite Iceberg’s power, enterprises often encounter operational complexity, scalability challenges, and high maintenance overhead during deployment. This has created a strong demand for managed solutions to simplify operations. enterprises often encounter operational complexity, scalability challenges, and high maintenance overhead during deployment managed solutions At AWS re: Invent 2024, Amazon Web Services introduced S3 Tables, a feature that enhances the managed capabilities of Iceberg. S3 Tables This innovation allows users to build and manage Iceberg tables directly on Amazon S3, eliminating the need for additional infrastructure investment. It significantly reduces operational cost and complexity, while leveraging the cloud’s global availability, durability, and scalability to boost elasticity and performance. build and manage Iceberg tables directly on Amazon S3 Such a fully managed, cloud-native approach is particularly suitable for high-availability and seamless integration scenarios, enabling enterprises to enjoy a true cloud-native data lake experience and ensuring stability under high-concurrency workloads. fully managed, cloud-native approach In many business scenarios, data synchronization—especially Change Data Capture (CDC)—plays a crucial role. It captures real-time changes from source databases and syncs them to target systems such as data lakes or warehouses. data synchronization—especially Change Data Capture (CDC) Real-time synchronization is ideal for time-sensitive use cases, such as fraud detection on financial platforms, real-time inventory updates in retail, or instant sharing of patient records in healthcare, ensuring that decisions are made with the freshest data.Offline (batch) synchronization is ideal for non-real-time scenarios such as daily backups, historical archiving, or scheduled report generation, efficiently processing large data volumes without unnecessary resource consumption. Real-time synchronization is ideal for time-sensitive use cases, such as fraud detection on financial platforms, real-time inventory updates in retail, or instant sharing of patient records in healthcare, ensuring that decisions are made with the freshest data. Real-time synchronization Offline (batch) synchronization is ideal for non-real-time scenarios such as daily backups, historical archiving, or scheduled report generation, efficiently processing large data volumes without unnecessary resource consumption. Offline (batch) synchronization Through these mechanisms, enterprises can efficiently achieve both CDC ingestion and batch synchronization, meeting diverse needs from real-time analytics to offline processing. efficiently achieve both CDC ingestion and batch synchronization This article demonstrates how to use Apache SeaTunnel, a high-performance, distributed data integration tool, to integrate data into Amazon S3 Tables through Iceberg REST Catalog compatibility, enabling both real-time and batch data pipelines. how to use Apache SeaTunnel Amazon S3 Tables Iceberg REST Catalog compatibility Architecture and Core Components Architecture and Core Components Integration via Iceberg REST Catalog in SeaTunnel Integration via Iceberg REST Catalog in SeaTunnel Integration via Iceberg REST Catalog in SeaTunnel SeaTunnel natively supports Apache Iceberg REST Catalog, which provides a standardized interface for metadata read/write operations, simplifying client–catalog interaction. Apache Iceberg REST Catalog Through this REST Catalog compatibility, SeaTunnel can directly and seamlessly register output table metadata into the Iceberg Catalog, without requiring custom plugin development or manual metadata synchronization—laying a solid foundation for automation and architectural decoupling in data lakes. SeaTunnel can directly and seamlessly register output table metadata into the Iceberg Catalog Cloud-Native Data Lake Capability: S3 Tables + REST Endpoint Cloud-Native Data Lake Capability: S3 Tables + REST Endpoint Cloud-Native Data Lake Capability: S3 Tables + REST Endpoint With the launch of S3 Tables, AWS now provides a built-in Iceberg REST Catalog Endpoint. built-in Iceberg REST Catalog Endpoint SeaTunnel can connect directly to S3 Tables—no modification required—to write batch or streaming data into Iceberg tables hosted on S3. Metadata and schema management are handled via the S3 Tables REST Endpoint. SeaTunnel can connect directly to S3 Tables—no modification required—to write batch or streaming data into Iceberg tables hosted on S3. Metadata and schema management are handled via the S3 Tables REST Endpoint. SeaTunnel can connect directly to S3 Tables This native integration greatly reduces the cost and complexity of cloud data lake adoption, enabling a serverless, cloud-native architecture where management and query layers are standardized, agile, and easily evolvable. serverless, cloud-native architecture Unified Data and Catalog Flow: Supporting CDC and Batch Synchronization Unified Data and Catalog Flow: Supporting CDC and Batch Synchronization Unified Data and Catalog Flow: Supporting CDC and Batch Synchronization As shown in the diagram, SeaTunnel serves as the data integration hub. Whether ingesting from databases (OLTP/OLAP), S3 partitions, or streaming CDC data, all data first enters SeaTunnel. SeaTunnel serves as the data integration hub Then, through SeaTunnel’s Iceberg Sink, data is written—either in real time or batch mode—into S3 Table Buckets. Meanwhile, Iceberg metadata is instantly registered in the Data Catalog service (e.g., Lake Formation) via the REST Catalog, ensuring one-stop coordination of business tables, metadata, and access control. SeaTunnel’s Iceberg Sink S3 Table Buckets Data Catalog service (e.g., Lake Formation) In CDC use cases, database changes are captured with low latency, maintaining data freshness. For batch ingestion or historical archiving, data is efficiently loaded into S3 Tables and managed under a unified Catalog, supporting hybrid data lakehouse queries. low latency hybrid data lakehouse queries In summary, the core innovation of this architecture lies in the standardization of data and metadata flow through the Iceberg REST Catalog, the cloud-native managed deployment enabled by AWS S3 Tables’ REST Endpoint, and SeaTunnel’s real-time + batch integration capabilities, delivering a one-stop, high-efficiency, and flexible data lake solution. core innovation standardization of data and metadata flow cloud-native managed deployment real-time + batch integration capabilities Data Integration Demo Data Integration Demo 1. Batch Data Integration 1. Batch Data Integration Use SeaTunnel’s FakeSource to test batch writes into S3 Tables.Edit the SeaTunnel configuration file with the Iceberg Sink configured for REST Catalog and AWS authentication: Use SeaTunnel’s FakeSource to test batch writes into S3 Tables. FakeSource Edit the SeaTunnel configuration file with the Iceberg Sink configured for REST Catalog and AWS authentication: env {
   parallelism = 1
   job.mode = "BATCH"
}
source {
   FakeSource {
     parallelism = 1
     result_table_name = "fake"
     row.num = 100
     schema = {
       fields {
         id = "int"
         name = "string"
         age = "int"
         email = "string"
       }
     }
   }
}
sink {
  Iceberg {
    catalog_name = "s3_tables_catalog"
    namespace = "s3_tables_catalog"
    table = "user_data"
    iceberg.catalog.config = {
      type: "rest"
      warehouse: "arn:aws:s3tables:<Region>:<accountID>:bucket/<bucketname>"
      uri: "https://s3tables.<Region>.amazonaws.com/iceberg"
      rest.sigv4-enabled: "true"
      rest.signing-name: "s3tables"
      rest.signing-region: "<Region>"
    }
  }
} env {
   parallelism = 1
   job.mode = "BATCH"
}
source {
   FakeSource {
     parallelism = 1
     result_table_name = "fake"
     row.num = 100
     schema = {
       fields {
         id = "int"
         name = "string"
         age = "int"
         email = "string"
       }
     }
   }
}
sink {
  Iceberg {
    catalog_name = "s3_tables_catalog"
    namespace = "s3_tables_catalog"
    table = "user_data"
    iceberg.catalog.config = {
      type: "rest"
      warehouse: "arn:aws:s3tables:<Region>:<accountID>:bucket/<bucketname>"
      uri: "https://s3tables.<Region>.amazonaws.com/iceberg"
      rest.sigv4-enabled: "true"
      rest.signing-name: "s3tables"
      rest.signing-region: "<Region>"
    }
  }
} Run the SeaTunnel job locally: Run the SeaTunnel job locally: ./bin/seatunnel.sh --config batch.conf -m local ./bin/seatunnel.sh --config batch.conf -m local Check the job logs: Check the job logs: View the table in the S3 Tables bucket and query it using Athena: View the table in the S3 Tables bucket and query it using Athena: S3 Tables bucket Athena 2. Real-Time CDC Data Integration 2. Real-Time CDC Data Integration Use MySQL CDC source to test streaming data ingestion into S3 Tables.Edit the SeaTunnel configuration file as follows: Use MySQL CDC source to test streaming data ingestion into S3 Tables. MySQL CDC source Edit the SeaTunnel configuration file as follows: env {
  parallelism = 1
  job.mode = "STREAMING"
  checkpoint.interval = 5000
}
source {
  MySQL-CDC {
    parallelism = 1
    result_table_name = "users"
    server-id = 1234
    hostname = "database-1.{your_RDS}.ap-east-1.rds.amazonaws.com"
    port = 3306
    username = ""
    password = ""
    database-names = ["test_st"]
    table-names = ["test_st.users"]
    base-url = "jdbc:mysql://database-1.{your_RDS}.ap-east-1.rds.amazonaws.com:3306/test_st"
    startup.mode = "initial"
  }
}
sink {
  Iceberg {
    catalog_name = "s3_tables_catalog"
    namespace = "s3_tables_catalog"
    table = "user_data"
    iceberg.catalog.config = {
      type: "rest"
      warehouse: "arn:aws:s3tables:<Region>:<accountID>:bucket/<bucketname>"
      uri: "https://s3tables.<Region>.amazonaws.com/iceberg"
      rest.sigv4-enabled: "true"
      rest.signing-name: "s3tables"
      rest.signing-region: "<Region>"
    }
  }
} env {
  parallelism = 1
  job.mode = "STREAMING"
  checkpoint.interval = 5000
}
source {
  MySQL-CDC {
    parallelism = 1
    result_table_name = "users"
    server-id = 1234
    hostname = "database-1.{your_RDS}.ap-east-1.rds.amazonaws.com"
    port = 3306
    username = ""
    password = ""
    database-names = ["test_st"]
    table-names = ["test_st.users"]
    base-url = "jdbc:mysql://database-1.{your_RDS}.ap-east-1.rds.amazonaws.com:3306/test_st"
    startup.mode = "initial"
  }
}
sink {
  Iceberg {
    catalog_name = "s3_tables_catalog"
    namespace = "s3_tables_catalog"
    table = "user_data"
    iceberg.catalog.config = {
      type: "rest"
      warehouse: "arn:aws:s3tables:<Region>:<accountID>:bucket/<bucketname>"
      uri: "https://s3tables.<Region>.amazonaws.com/iceberg"
      rest.sigv4-enabled: "true"
      rest.signing-name: "s3tables"
      rest.signing-region: "<Region>"
    }
  }
} Run the SeaTunnel job: Run the SeaTunnel job: ./bin/seatunnel.sh --config streaming.conf -m local ./bin/seatunnel.sh --config streaming.conf -m local Conclusion and Outlook Conclusion and Outlook With the deep integration between Apache SeaTunnel, Apache Iceberg, and AWS S3 Tables, enterprise data lake architectures are entering a new era of flexibility and scalability. Apache SeaTunnel Apache Iceberg AWS S3 Tables In production environments, monitoring measures can be introduced by integrating Prometheus and Grafana for real-time metrics (including job status, throughput, and error logs), enabling proactive issue detection and rapid response. monitoring measures Prometheus and Grafana Additionally, using Kubernetes or Docker Swarm for elastic deployment, enterprises can achieve auto-scaling and failover of SeaTunnel jobs, supporting dynamic resource allocation (e.g., load-based pod scaling). Kubernetes or Docker Swarm for elastic deployment auto-scaling and failover dynamic resource allocation (e.g., load-based pod scaling) This ensures the stability and high availability of ETL workflows while minimizing manual intervention and efficiently handling data surges. Moreover, by leveraging AWS’s advanced services such as Athena for querying and Glue Crawler for automated schema discovery, organizations can further optimize Iceberg table performance. Athena Glue Crawler For example, enabling S3 Intelligent-Tiering can lower storage costs, while integrating with Lake Formation strengthens data governance and access control. S3 Intelligent-Tiering Lake Formation These optimizations make data lakes more elastic and powerful for BI analytics and AI/ML data preparation—supporting low-latency queries on PB-scale datasets and efficient model training. low-latency queries on PB-scale datasets efficient model training Note: Certain AWS generative AI-related services mentioned above are currently available in AWS’s overseas regions. AWS China (operated by Sinnet and NWCD) provides localized cloud services—please refer to the AWS China official site for details. Note: Certain AWS generative AI-related services mentioned above are currently available in AWS’s overseas regions. AWS China (operated by Sinnet and NWCD) provides localized cloud services—please refer to the AWS China official site for details.

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Building a Cloud-Native Data Lake: Integrating Apache SeaTunnel with AWS S3 Tables and Iceberg REST

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