Migrating and Reconciling Data with AWS DMS, Glue, and EMR

Data reconciliation is a critical process in ensuring data consistency and integrity across various systems. Recently, we embarked on a project where we utilized AWS Database Migration Service (AWS DMS), AWS Glue, and Amazon EMR to efficiently migrate, transform, and reconcile data from MongoDB without directly hitting the MongoDB server. Here’s a detailed overview of our approach:

Step 1: Extracting Data from MongoDB with AWS DMS

The first step involved extracting data from MongoDB. Instead of querying MongoDB directly, which can put a load on the server, we used AWS DMS to pull data. AWS DMS allows you to capture changes from MongoDB’s oplog, which records all operations that modify the data. This approach has several advantages:

• Reduced Load on MongoDB Server: By reading from the oplog, the primary database operations remain unaffected.
• Real-time Data Capture: Changes are captured in real-time, ensuring the migration process is up-to-date.

Step 2: Storing Data in Parquet Format on S3

Once the data was captured by AWS DMS, it was stored in Amazon S3 in Parquet format. Parquet is a columnar storage file format optimized for analytical queries. It provides efficient data compression and encoding schemes, which leads to better performance.

Step 3: Transforming Data with AWS Glue

With the data in S3, the next step was to transform it using AWS Glue. AWS Glue is a fully managed ETL (extract, transform, load) service that makes it easy to prepare and transform data for analytics. The transformation process involved converting the data from Parquet format to Iceberg format. Apache Iceberg is an open table format for huge analytic datasets, which helps in handling petabyte-scale data.

• Define Glue Job: We defined an AWS Glue job to read the Parquet files from S3, perform necessary transformations, and write the output in Iceberg format back to S3.
• Optimize Storage: Iceberg format improves query performance and manages large datasets efficiently.

Step 4: Validating Data with EMR and Running Reconciliation

After transforming the data, the next crucial step was to validate it. We used Amazon EMR (Elastic MapReduce) for this purpose. EMR is a cloud big data platform that provides a managed Hadoop framework, making it easy to process large amounts of data quickly and cost-effectively.

• Setup EMR Cluster: We set up an EMR cluster to process the transformed data.
• Run Validation Scripts: Using Apache Spark on EMR, we ran validation scripts to ensure the transformed data matched the source data in MongoDB.
• Reconciliation Process: The reconciliation process involved comparing the data and identifying any discrepancies. This step was crucial to ensure data integrity and consistency.

Benefits of This Approach

1. No Direct Hit to MongoDB: By leveraging the oplog, the MongoDB server was not directly queried, reducing the load and avoiding potential performance issues.
2. Efficient Data Handling: Using Parquet and Iceberg formats improved data processing efficiency and storage optimization.
3. Scalability and Flexibility: AWS services like DMS, Glue, and EMR provided a scalable and flexible solution for handling large datasets.
4. Reduced Time and Cost: The entire process was streamlined, reducing the time required for data reconciliation and minimizing costs associated with data migration and processing.

Conclusion

This project demonstrated how AWS services can be effectively combined to migrate, transform, and reconcile data from MongoDB. By using AWS DMS to read from the oplog, storing data in optimized formats on S3, transforming data with AWS Glue, and validating it using EMR, we were able to ensure data integrity and consistency while minimizing the impact on the MongoDB server. This approach not only improved performance but also made the entire data reconciliation process more efficient and reliable.

Final Thoughts

Data reconciliation is a continuous process, and leveraging AWS's suite of tools makes it both manageable and scalable. Here’s a recap of the key steps and their benefits:

1. Using AWS DMS to Capture Data:
2. Storing Data in Parquet Format on S3:
3. Transforming Data with AWS Glue:
4. Validating and Reconciling Data with EMR:

By following this method, we can achieve a streamlined and efficient data reconciliation process, reducing the risk of data discrepancies and ensuring data remains consistent and reliable across platforms. This approach is especially beneficial for organizations dealing with large volumes of data and requiring real-time data synchronization and validation.