In today’s digital landscape, data is at the core of informed decision-making. Organizations rely on robust data platforms to ingest, process, store, and analyze large volumes of data in real-time.

At Peritos Solutions, building scalable and secure data platforms is at the heart of what we do. Recently, we implemented a powerful data platform using AWS CloudFormation to streamline data ingestion, transformation, and storage while ensuring the highest levels of security and accessibility. Here’s how we structured and deployed this solution by dividing the platform into core components and data-specific modules.

Why AWS CloudFormation for Data Platform Deployment?

AWS CloudFormation allows us to define infrastructure as code, making it easier to manage and maintain resources consistently across multiple environments. By organizing our deployment into separate repositories for core components and data-specific resources, we achieved a modular setup that can be scaled and customized as needed.

Breaking Down the Data Platform

We divided the AWS CloudFormation setup into two main repositories:

• Core Components: Contains the foundational infrastructure that powers the data platform.
• Data Source Specific Components: Contains resources tailored to each specific data source, enabling flexibility and easy management of multiple data pipelines.

Core Components:

The core repository consists of key foundational components that form the backbone of the data platform. Here’s a closer look at the folders and stacks in the repository:

• KMS (Key Management Service):
  • KMS Redshift: Templates for Redshift encryption to secure data at rest.
  • KMS Secrets Manager: Configures KMS integration with Secrets Manager for managing sensitive data.
  • KMS S3: Secures S3 data storage with KMS encryption.
  • KMS Query Editor V2: Integrates KMS with Redshift Query Editor for secure data querying.
• IAM (Identity and Access Management): Manages IAM roles and policies with least-privilege access, ensuring that every component in the platform has the required permissions, and nothing more.
• Lake Formation: AWS Lake Formation manages data access and security for the S3 data lake. With LF-tags and permissions, we set up persona-based access control, securing data at the database, table, and even column level.
• Amazon Redshift: Deploys a Redshift cluster as the main data warehouse, providing fast analytical processing and integration with Redshift Spectrum to query data directly from S3.
• S3 (Simple Storage Service): Acts as the foundation of the data lake, with buckets structured into “Landing,” “Raw,” and “Transformed” layers, organized to support data ingestion and transformation workflows.
• VPC (Virtual Private Cloud): Establishes a secure networking layer, isolating data resources within a private network to ensure internal communication and eliminate exposure to the public internet.

Each component can be deployed independently, allowing for modular updates and maintenance.

Data Source Specific Components: Coregs Repository

The data source-specific repository provides the flexibility to manage and configure data pipelines tailored to individual data sources. Here’s an overview of the key components in this repository:

• CodeBuild: Automates the building and testing of data pipelines, ensuring that code deployments are consistent and error-free.
• Glue: AWS Glue automates data ingestion, transformation, and cataloging. The Glue directory includes templates for configuring connections, databases, and permissions tailored to each data source.
• Lambda: AWS Lambda functions automate and orchestrate data pipeline tasks, such as triggering ETL jobs, monitoring data processing, and updating metadata.
• Notifications: Manages alerts for data processing events, providing visibility into pipeline performance.
• Secret Manager: Manages sensitive data such as database credentials securely, integrating with Lambda and Glue to access data securely.

How It All Comes Together: Data Flow and Orchestration

The data platform orchestrates data processing and management through AWS Step Functions. Here’s a step-by-step overview of the data flow from ingestion to consumption:

1. Data Ingestion and Storage: Data is collected from multiple sources, including files, databases, IoT devices, and external servers, and ingested into the S3 Landing bucket. AWS Lambda functions trigger data ingestion processes based on scheduled events or data uploads.
2. Data Transformation and Cataloging: AWS Glue performs ETL transformations, converting data into optimized formats like Parquet. Glue Crawlers identify and catalog the data, adding it to the Glue Data Catalog, where it becomes searchable and accessible to users.
3. Data Governance and Access Control: Lake Formation governs data access based on LF-tags, allowing granular control at database, table, and even column levels. This setup allows data stewards to manage permissions for various user roles, ensuring secure, least-privilege access.
4. Data Storage in Redshift for Analytics: Transformed data is loaded into Redshift, where it is readily available for analytical queries. Redshift Spectrum allows users to query data in S3 directly without needing to load it into Redshift, enabling cost-effective, flexible analysis.
5. Data Consumption: Users and applications can access the data through SQL clients, Redshift Spectrum, or analytics tools, connecting securely via JDBC and other supported connections.

Security Best Practices

Security is integrated at every level of the data platform:

• Data Encryption: KMS keys encrypt all data at rest across Redshift, S3, and Secrets Manager.
• Private Networking: All resources are deployed within a private VPC, ensuring that communication happens only within the network.
• Least Privilege Access: IAM and Lake Formation policies enforce strict access control, giving users only the access they need.
• Cross-Account Access: Secure cross-account access is implemented using VPC endpoints and Transit Gateway Attachments, allowing access only to trusted accounts.

Automation and Monitoring with CloudWatch

AWS CloudWatch monitors and logs data pipeline activities, providing insights into performance and alerting on failures. Step Functions orchestrate the pipeline, executing tasks sequentially and capturing metrics for each task, enabling proactive monitoring and maintenance.

Benefits of Using AWS CloudFormation for Data Platform Deployment

Using CloudFormation enabled us to:

• Ensure Consistency: By defining infrastructure as code, resources were deployed consistently across all environments (development, staging, production).
• Enable Modularity: Separate repositories for core and data source-specific components allowed easy management, scaling, and customization.
• Improve Security: Role-based access control, encryption, and private networking ensured the highest levels of data security.
• Streamline Automation: With Lambda and Step Functions, we automated ETL tasks, data cataloging, and alerting, reducing the need for manual intervention.

Conclusion

Our AWS CloudFormation-powered data platform provides a scalable, secure, and flexible solution to manage data ingestion, transformation, and storage. By structuring the platform into core and data-specific repositories, we achieved a modular setup that can adapt to evolving business needs.

At Peritos Solutions, we help organizations design and deploy cloud-native data platforms tailored to their specific needs. If you’re ready to unlock the potential of AWS for your data platform, reach out to us to learn how we can help.