无服务器数据管道架构

Architecture

无服务器数据管道架构图，展示 Kinesis 摄取、Lambda 转换函数、S3 数据湖存储区（原始和精选）、Glue 目录注册以及 Athena 查询引擎为 QuickSight 仪表板和 SageMaker ML 模型提供数据。该模板模拟从数据摄取到转换、编目和分析的完整无服务器 ETL 管道，无需管理任何基础设施。适合构建经济高效分析平台的数据团队。

下载 .fz 文件

完整 FlowZap 代码

Ingestion { # Data Ingestion
n1: rectangle label:"Kinesis Data Stream"
n2: rectangle label:"API Gateway Endpoint"
n3: rectangle label:"IoT Core MQTT"
n4: rectangle label:"Kinesis Firehose"
n1.handle(right) -> n4.handle(left) [label="Stream"]
n2.handle(right) -> n4.handle(left) [label="Batch"]
n3.handle(right) -> n4.handle(left) [label="Device Data"]
n4.handle(bottom) -> Transform.n5.handle(top) [label="Buffer + Deliver"]
}
Transform { # Serverless Transform
n5: rectangle label:"Lambda Transform Function"
n6: rectangle label:"Validate Schema"
n7: diamond label:"Valid Record?"
n8: rectangle label:"Enrich with Reference Data"
n9: rectangle label:"Route to Error Bucket"
n10: rectangle label:"Partition by Date"
n5.handle(right) -> n6.handle(left)
n6.handle(right) -> n7.handle(left)
n7.handle(right) -> n8.handle(left) [label="Valid"]
n7.handle(bottom) -> n9.handle(top) [label="Invalid"]
n8.handle(right) -> n10.handle(left) [label="Enriched"]
n10.handle(bottom) -> Storage.n11.handle(top) [label="Partitioned"]
}
Storage { # Data Lake Storage
n11: rectangle label:"S3 Raw Zone"
n12: rectangle label:"S3 Curated Zone"
n13: rectangle label:"Glue Catalog"
n14: rectangle label:"Athena Query Engine"
n11.handle(right) -> n12.handle(left) [label="Glue ETL Job"]
n12.handle(right) -> n13.handle(left) [label="Register Schema"]
n13.handle(right) -> n14.handle(left) [label="Queryable"]
}
Analytics { # Analytics Layer
n15: rectangle label:"QuickSight Dashboard"
n16: rectangle label:"SageMaker ML Model"
n17: rectangle label:"Redshift Data Warehouse"
n14.handle(bottom) -> Analytics.n15.handle(top) [label="Visualize"]
n14.handle(bottom) -> Analytics.n16.handle(top) [label="Train Model"]
n14.handle(bottom) -> Analytics.n17.handle(top) [label="COPY INTO"]
}

为什么需要这个工作流？

Traditional ETL pipelines require provisioning Spark clusters, managing job schedulers, and paying for always-on infrastructure. A serverless data pipeline uses managed services for every stage—ingestion, transformation, cataloging, and querying—eliminating infrastructure management while maintaining enterprise-grade data processing capabilities.

工作原理

Step 1: Data streams from Kinesis, API Gateway, and IoT Core are buffered by Kinesis Firehose.
Step 2: Lambda transform functions validate schemas, enrich records, and filter invalid data.
Step 3: Transformed data lands in S3 raw zone, then is processed by Glue ETL into the curated zone.
Step 4: The Glue Data Catalog registers schemas for all datasets.
Step 5: Athena provides serverless SQL queries over the cataloged data.
Step 6: QuickSight dashboards and SageMaker models consume the curated data for analytics and ML.

替代方案

Self-managed Spark/Hadoop clusters offer more control but require significant DevOps investment. Snowflake or Databricks provide managed alternatives with different pricing models. This template shows the AWS-native serverless data pipeline.

Key Facts

Template Name	无服务器数据管道架构
Category	Architecture
Steps	6 workflow steps
Format	FlowZap Code (.fz file)