Azure Cosmos DB to MongoDB Atlas Migration

Migrating 20 TB of Semi-Structured Data from Azure Cosmos DB to MongoDB Atlas

Client Overview

The client, a major player in the phone and communications technology industry, was facing high operational costs and fragmentation in their data management. With MongoDB Atlas already in use for other business cases, they sought to consolidate their data onto a single platform to reduce costs and improve operational efficiency.

Business Challenge

The client’s primary goal was to migrate 20 terabytes of semi-structured data from Azure Cosmos DB to MongoDB Atlas, driven by two key business objectives:

Cost Savings: Cosmos DB was becoming expensive to maintain, and consolidating data on MongoDB Atlas, already integrated into their infrastructure, promised significant savings.
Operational Efficiency: The client wanted to centralize their data storage to streamline operations and improve the performance of read/write operations across various business applications.

Technical Challenges

This migration presented several technical challenges:

Data Consistency: Ensuring data accuracy during migration was critical. I implemented data quality checks to validate data integrity and reported inconsistencies to the client for resolution.
Schema Differences: Cosmos DB and MongoDB Atlas handle semi-structured data differently. As part of the migration, I had to perform schema mapping and some data modeling to align data formats between the two systems.

Solution Overview

As the Senior Data Engineer leading the migration, I employed a structured, phased approach to ensure data was migrated seamlessly and efficiently:

One-off Data Load: Using Azure Data Factory (ADF), I created batch pipelines to handle the initial migration of all 20 TB of data across nearly 100 collections. Python scripts were employed to trigger the ADF pipelines, ensuring smooth automation of the migration process.
Incremental Load Strategy: Post-migration, an incremental load strategy was implemented to handle daily updates to the data. This strategy identified newly inserted, updated, or deleted records and synchronized them between Azure Cosmos DB and MongoDB Atlas using ADF pipelines.
Monitoring and Logging: Data migration and transformation processes were monitored using Azure Monitor, while custom logging was implemented in Python. OpenTelemetry was utilized for distributed tracing and deeper insights into the system’s performance.
Optimization: After the initial migration, I implemented optimizations to improve pipeline efficiency and minimize costs, ensuring the ongoing incremental loads ran with minimal resource overhead.

Business Impact

The successful migration delivered several business and technical benefits:

Improved Performance: The performance of read/write operations on MongoDB Atlas was significantly enhanced, allowing the client to handle higher volumes of requests without bottlenecks. MongoDB’s built-in scaling capabilities, set up by the DevOps team, ensured the system could handle the growing data load.
Cost Savings: By consolidating all data onto MongoDB Atlas, the client reduced their infrastructure and data management costs, benefiting from the more cost-effective pricing model of MongoDB.
Centralized Data Management: The client now benefits from centralized data management, with all their business-critical data residing in a single system, simplifying operations and improving accessibility for their teams.

Technology Stack

Source: Azure Cosmos DB (semi-structured data in collections and documents)
Target: MongoDB Atlas
Migration Tools: Azure Data Factory (ADF) for batch processing and pipeline orchestration
Automation: Python for triggering ADF pipelines and handling custom operations
Monitoring & Logging: Azure Monitor, Python logging, and OpenTelemetry for tracing

Conclusion

This migration project not only helped the client achieve their primary objectives of cost savings and improved performance but also laid the groundwork for future scalability. By centralizing their data into MongoDB Atlas, the client is now well-positioned to leverage their data more effectively and make data-driven decisions at scale.

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