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Build Data Ingestion Pipelines Implementation Plan

Overview​

This document details the strategy, design, and implementation steps for the Build data ingestion pipelines section of the ShieldCraft AI checklist. The goal is to create a modular, cloud-native, and reproducible data ingestion architecture using AWS CDK and best-in-class open-source tools.

Guiding Question​

How do we design ingestion pipelines that are modular, reproducible, secure, and ready for enterprise scale?

Definition of Done​

All ingestion infrastructure and code is defined as code, parameterized for environments, and supports secure, governed, and observable data flows from source to lake.

Key Technologies​

Implementation Strategy​

1. Infrastructure as Code (AWS CDK)​

  • All resources (MSK, S3, Glue, Lambda, networking, IAM, etc.) are defined in AWS CDK for full reproducibility and automation.
  • Parameterize for dev/staging/prod environments.

2. Amazon MSK (Kafka) Cluster​

  • Use aws-cdk-lib.aws_msk to define the cluster (Standard or Serverless).
  • Place brokers in private subnets, restrict access via security groups.
  • Enable encryption, logging, and IAM authentication.
  • Use a Lambda-backed custom resource to automate topic creation.
  • Export broker endpoints and topic names for downstream use.

3. Airbyte Integration​

  • Deploy Airbyte in ECS/EKS (or managed Airbyte Cloud).
  • Use exported MSK endpoints and topics for source/target connectors.
  • Parameterize connector configs for different data sources.

4. AWS Lambda for Event-Driven Processing​

  • Define Lambda functions (in CDK) to consume from MSK topics.
  • Use IAM roles and VPC config for secure access.
  • Implement pre-processing, filtering, and routing logic.

5. Amazon OpenSearch Ingestion​

  • Use Kafka Connect or Lambda to forward data from MSK to OpenSearch.
  • Configure index mappings and retention policies.

6. AWS Glue for ETL​

  • Define Glue streaming and batch jobs in CDK.
  • Process data from MSK topics and write to S3.
  • Use Glue Data Catalog for schema management.

7. Amazon S3 Data Lake​

  • Store all raw and processed data in S3 buckets.
  • Apply Lake Formation governance and access controls.

8. Data Quality & Governance​

  • Integrate Great Expectations/Deequ for automated data quality checks.
  • Enforce privacy, retention, and compliance policies via Lake Formation and Glue.

Next Steps​

  • Scaffold the CDK stack for MSK, S3, networking, and IAM.
  • Implement Lambda-backed custom resource for Kafka topic creation.
  • Define standard topic names/configs and parameterize for environments.
  • Document integration patterns for Airbyte, Lambda, Glue, and OpenSearch.
  • Add governance, data quality, and compliance automation.