Optimizing Data Migration and Reconciliation for a Leading Accounting Firm: A Success Story with AWS Solutions

Introduction

Maintaining data consistency and integrity across systems is crucial for any organization. In today’s data-driven world, discrepancies between data sources can lead to inaccurate analyses, poor decision-making, and operational inefficiencies. These issues can further result in financial losses, diminished customer trust, and compliance risks. As organizations increasingly rely on vast amounts of data to drive strategic initiatives, ensuring that this data remains consistent, accurate, and reliable across various platforms and environments becomes paramount. Effective data reconciliation and migration processes not only safeguard the integrity of critical business information but also enhance the overall efficiency of data workflows. By implementing robust data management strategies and leveraging advanced tools and technologies, organizations can minimize errors, streamline operations, and make more informed decisions, ultimately driving business growth and success.

Problem Statement

In one of our client projects, we faced significant challenges with data reconciliation:

• Running reconciliation processes directly on MongoDB led to reduced efficiency of our MongoDB servers.
• The reconciliation flow was not automated, resulting in increased manual effort and delays.

To address these issues, we needed a solution that would offload the reconciliation process from MongoDB and automate the data workflow.

Objective

The objective of this blog is to share our experience and methodology in addressing the challenges of data reconciliation and migration from MongoDB to an analytical platform. We aim to demonstrate how leveraging various AWS services can efficiently and effectively manage these processes. Key aspects include:

• Leveraging AWS Services: Utilizing AWS Database Migration Service (AWS DMS), AWS Glue, Amazon EMR, Amazon EKS, and Apache Airflow.
• Efficient Data Management: Efficiently handling data extraction, transformation, and reconciliation processes.
• Minimizing Impact: Reducing the load on the MongoDB server to maintain its performance.
• Enhancing Data Integrity: Improving data consistency and integrity across systems.
• Automating Workflows: Automating data workflows to enhance operational efficiency.

This approach not only enhances data consistency and integrity but also automates workflows to improve overall operational efficiency.

Solution Approach

To overcome these challenges, we implemented a solution using a combination of AWS services and Apache Airflow. Here’s a step-by-step overview of our approach:

Step 1: Extracting Data from MongoDB with AWS DMS

To minimize the load on MongoDB, we utilized AWS DMS to extract data. DMS reads from MongoDB’s oplog (operation log), capturing all changes in real time:

• Reduced Load on MongoDB Server: By leveraging the oplog, primary database operations remain unaffected during data extraction.
• Real-time Data Capture: Changes are captured in real time, ensuring the migration process reflects the latest data updates.

Step 2: Storing Data in Parquet Format on S3

Whenever users modify data (insert, update, delete), AWS DMS’s Change Data Capture (CDC) task captures these changes and stores them in Amazon S3 in Parquet format:

• Efficient Data Compression and Encoding: Parquet’s columnar storage optimizes data compression and encoding, enhancing query performance.
• Seamless Data Capture: The CDC task ensures immediate capture and storage of any data modifications.

Step 3: Triggering AWS Glue Jobs with Apache Airflow

Apache Airflow played a pivotal role in automating our ETL (Extract, Transform, Load) workflow. Upon detecting new Parquet files in S3, an Airflow DAG (Directed Acyclic Graph) triggered an AWS Glue job:

• Automated Workflow: Airflow’s DAG monitored the S3 bucket for new data files and seamlessly triggered the corresponding Glue job.
• Timely Transformation: This automation ensured the timely execution of transformations as new data became available, maintaining data freshness.

Step 4: Transforming Data with AWS Glue

With data securely stored in S3, the next step involved transforming it using AWS Glue, a fully managed ETL service designed to prepare and transform data for analytics:

• Define Glue Job: We defined a specialized AWS Glue job to read Parquet files from S3. This job executed necessary transformations, leveraging Glue’s scalable infrastructure and optimized performance for data processing tasks.
• Optimize Storage with Iceberg Format: As part of the transformation process, data was structured and optimized using Iceberg format before being written back to S3. Iceberg is a table format tailored for large-scale analytic datasets, offering efficient storage management, improved query performance, and robust data versioning capabilities.

Step 5: Validating Data with EMR and Running Reconciliation

Validation of transformed data was conducted using Amazon EMR, a cloud-based big data platform managed through Amazon EKS (Elastic Kubernetes Service):

• Deploy EMR Service on EKS: We deployed a service on EKS to manage EMR clusters and installed necessary packages for data validation.
• Validation Scripts: Using Apache Spark on EMR, we executed validation scripts to ensure the accuracy and consistency of transformed data against the source data in MongoDB.
• Reconciliation Process: This step involved comparing datasets to identify and resolve any discrepancies, ensuring data integrity across platforms.

Benefits of This Approach

• Enhanced Data Consistency: Leveraging AWS DMS for real-time data capture and Glue for efficient transformation ensured consistent data across systems.
• Scalability and Flexibility: AWS services like Glue, EMR, and EKS provided a scalable solution for handling large datasets and complex analytical workflows.
• Automation and Efficiency: Apache Airflow automated workflow management, reducing manual intervention and ensuring timely data processing.
• Cost Optimization: By leveraging serverless and managed services, the project optimized costs associated with infrastructure maintenance and operations.

Conclusion

This project showcased the effectiveness of integrating AWS services and Apache Airflow for seamless data migration, transformation, and reconciliation. By leveraging AWS DMS for initial data extraction, storing data in optimized formats on S3, automating transformations with AWS Glue, and validating with Amazon EMR on EKS, we ensured robust data integrity and consistency while minimizing the operational overhead on MongoDB. This comprehensive approach not only streamlined our data processes but also significantly enhanced the reliability and efficiency of our data management operations.

Final Thoughts

Data reconciliation is a crucial, ongoing process for organizations managing large volumes of data across various platforms. AWS’s suite of tools, combined with Apache Airflow, offers scalable and efficient solutions for maintaining data consistency and reliability. Here’s a summary of the key steps and their benefits:

• Data Extraction with AWS DMS: Seamlessly extract data from MongoDB to AWS.
• Storing Data in Parquet Format on S3: Optimize data storage for cost-effectiveness and performance.
• Automating ETL Workflow with Apache Airflow: Streamline and automate data workflows to reduce manual intervention.
• Transforming Data with AWS Glue and Iceberg Format: Ensure efficient and flexible data transformation processes.
• Validating and Reconciling Data with EMR on EKS: Guarantee data integrity and consistency across platforms.

By following this approach, organizations can streamline their data operations, enhance analytical capabilities, and ensure data remains accurate and actionable across various systems and platforms. This methodology not only improves operational efficiency but also maximizes the value derived from data assets.