Building a Scalable Data Pipeline: Automating Data Approval and Integration with AWS

Modern industries often require seamless workflows to process, validate, and integrate real-world data from devices and user-submitted forms. This article outlines an automated data approval and integration architecture using AWS, designed to streamline data processing, ensure accuracy, and integrate approved data into analytics-ready systems.

Architecture Overview

This system automates the end-to-end lifecycle of data handling, starting from ingestion to final integration into a Redshift database. The architecture supports approval workflows for analytical chemists, ensuring that only validated and high-quality data reaches the data warehouse. The key components include:

• Data Ingestion Layer: Handles incoming files from devices and SharePoint forms.
• Processing and Approval Layer: Processes, validates, and routes files through approval workflows.
• Integration Layer: Stores approved data in an S3 bucket, transforms it, and integrates it into Redshift for analysis.

Data Sources Integration

The workflow involves two main data sources:

• Instrument Log/Reading Files: These are raw data files sent to specific S3 folders categorized by data type and processing needs.
• SharePoint Submission Forms: Metadata and complementary information synced with S3 via AWS AppFlow to ensure all necessary details for calculations are available.

AWS Services Architecture

The architecture leverages AWS services to automate, process, and manage the data approval lifecycle effectively:

• Amazon S3: Serves as the storage backbone, segregating data into folders. Encryption and access controls ensure data security.
• AWS Lambda: Executes event-driven processing tasks, including routing, validation, and transformation of files.
• Amazon SNS: Sends approval or disapproval notifications to analytical chemists.
• Amazon Redshift: Acts as the final repository for approved and transformed data, enabling advanced analytics.
• AWS API Gateway: Used to create Approval and disapproval links and to connect SNS triggers with lambda.
• AWS AppFlow: To Securely integrate apps and easily automate data flows and to get the most up-to-date information from the Apps, in this case, SharePoint.

Data Flow Process

Ingestion: Raw data files from devices are uploaded to specific S3 folders. SharePoint forms are synchronized with S3 using AWS AppFlow, providing supplementary metadata.

Decision Logic: The DeciderLambda function inspects new files in the S3 bucket and routes them to the appropriate processing pipeline. Here is the implemented decision logic:

• External Processing: Files categorized as “external”
• Internal Processing: Files categorized as “internal”
• Y Processing: Files in Y
• Move to Prod bucket: Files in “Z1” or “Z2”

Processing: Each processing Lambda function generates two output files:

• Calculation File: Contains computed results.
• Processing File: Contains metadata and processing details.

Approval Workflow: The system triggers an SNS notification to the analytical chemist with “Approve” and “Disapprove” options.

• Upon approval: The Data_Converter Lambda transforms the files and stores them in the production S3 bucket in a Redshift-compatible format. Data is ingested into Redshift for analytics.
• Upon disapproval: The SNS_Approval_Handler Lambda sends an email to the system owner with error details for resolution.

Error Handling and Monitoring

Error Handling: Validation errors and failed calculations are caught by dedicated Lambda functions. Notifications are sent to the system owner via Amazon SNS, with detailed logs for debugging.

Monitoring: Amazon CloudWatch tracks pipeline performance and alerts for anomalies, ensuring system reliability.

Data Access and Security

Data Security: All data is encrypted at rest and in transit using AWS KMS. Role-based access control (RBAC) ensures proper authentication and authorization.

Data Accessibility: Approved data in Redshift is available for business analytics, enabling decision-making and reporting.

Best Practices Implementation

Efficiency: Serverless architecture eliminates infrastructure management, reducing costs and complexity.

Scalability: AWS services like S3 and Lambda scale automatically with data volume.

Data Integrity: Structured approval workflows ensure only high-quality data is integrated into Redshift.

Resilience: Automated notifications and monitoring systems ensure rapid response to errors.

Conclusion

This architecture demonstrates how AWS services can streamline data processing, approval, and integration workflows. By automating calculations and approvals, it reduces manual effort, ensures data accuracy, and delivers analytics-ready data for informed decision-making. This system exemplifies how organizations can use AWS to build efficient and scalable solutions for complex data handling scenarios.