Designing a Scalable, Pod-like Data Lake on AWS for On-Premises Data
A Modular Approach to Data Modernization
Migrating data from diverse on-premises systems, especially proprietary environments like SAS and traditional databases, to the cloud can be complex. This document outlines a scalable, modern data lake architecture on Amazon Web Services (AWS) that simplifies this process through a “pod-like” modular design.
By leveraging serverless AWS native services like Amazon S3, AWS Glue, and AWS Lake Formation, this architecture ensures flexibility, security, and cost-efficiency. The modular “pod” concept allows for the agile addition of new data sources and processing workflows, translating abstract architecture principles into a tangible, high-performing data platform.
1. The Core: The Medallion Architecture on Amazon S3
Amazon S3 forms the backbone of this data lake, providing unparalleled scalability and durability. To manage the data lifecycle effectively, the architecture employs the Medallion Architecture (Bronze, Silver, Gold zones):
| Data Lake Zone | Purpose | Key Characteristics | Recommended File Format |
|---|---|---|---|
| Raw Zone (Bronze) | Initial landing zone; store data in its original, unaltered format. | Schema-on-read; Immutable; Single source of truth for raw data. | Original format (e.g., CSV, JSON, .sas7bdat) |
| Standardized Zone (Silver) | Cleanse, validate, transform, and standardize data. | Schema-on-write (enforced); Conformed enterprise view. | Apache Parquet (with Snappy compression) |
| Analytics-Ready Zone (Gold) | Highly refined, aggregated, business-specific data. | Optimized for specific query patterns; Denormalized for BI/ML. | Apache Parquet (with Snappy compression) |
Storage Best Practices
- Partitioning: Use a single, full date column (e.g.,
dt=YYYY-MM-DD) for time-series data instead of nested partitions to optimize query performance and reduce S3 API costs. - Columnar Formats: The conversion to Apache Parquet or ORC in the Silver/Gold zones is critical for cost savings and performance (Parquet can offer up to 99.7% cost savings compared to CSV for queries).
- Combine Small Files: Implement a process to merge numerous small files into larger, optimally sized objects (128MB to 1GB) during the Bronze-to-Silver transition to improve query engine efficiency.
2. Ingestion Strategy for On-Premises Sources
The “pod-like” design excels by creating distinct, reusable ingestion patterns for different source types, all delivering data to the S3 Raw Zone in its original format.
Databases & Change Data Capture (CDC)
- Tool: AWS Database Migration Service (DMS)
- Use Case: Migrating relational (and some NoSQL) databases. DMS supports both full load migration and ongoing replication (CDC) to capture changes from transaction logs and keep the data lake synchronized.
- Helper Tool: AWS Schema Conversion Tool (SCT) assists in heterogeneous migrations by analyzing and converting source database schemas.
SAS and File-Based Data
- Initial Bulk Transfer Tool: AWS DataSync is the recommended service for efficient, secure, and automated transfer of large volumes of files, including
.sas7bdatfiles, from on-premises file shares (NFS/SMB) to the S3 Raw Zone. - Conversion Tool: AWS Glue Spark jobs are used once the
.sas7bdatfiles are in the Raw Zone to convert them into the optimized Apache Parquet format for the Silver and Gold layers, liberating the data from the proprietary format.
3. Data Processing and Governance
Serverless ETL with AWS Glue
AWS Glue is the primary, serverless ETL service for the data lake. Glue ETL jobs (Spark or Python Shell) handle the core data transformations:
- Cleansing & Standardization: Applying business rules, handling missing values, and enforcing schema consistency.
- File Format Conversion: Converting to Parquet and implementing optimal partitioning.
Centralized Metadata and Governance
- AWS Glue Data Catalog: A managed, persistent metadata store that acts as the single source of truth for data lake assets. AWS Glue Crawlers automatically infer and catalog the schema of data stored in S3.
- AWS Lake Formation: Provides a centralized governance layer. It integrates with the Glue Data Catalog to manage data access, security, and compliance, enabling ACID transaction capabilities on S3 data (Governed Tables) for applying CDC and handling deletion requests (e.g., GDPR/CCPA).
The Well-Architected Advantage
The modular “pod” architecture inherently aligns with the AWS Well-Architected Framework, driving significant business value:
- Operational Excellence: Achieved through automation of “pod” deployment and management.
- Cost Optimization: Enabled by decoupling storage and compute, leveraging serverless services (Glue, DMS), and intelligent S3 storage tiering.
- Performance Efficiency: Delivered through optimized file formats (Parquet), effective partitioning, and right-sizing resources for each specific “pod” task.
This design provides a future-proof foundation, ready to scale and evolve with the organization’s growing analytical needs while maintaining a strong security and governance posture.