Introduction

Imagine you're working at one of the biggest enterprise multitenant SaaS platform. Everything is moving at lightning speed—new data is pouring in, orders are flowing from multiple channels, and your team is constantly iterating on new features. But there's one problem: your data infrastructure, built on MongoDB, is becoming a bottleneck.

While MongoDB serves well for operational data, it's struggling to handle the complexity of modern data analytics, aggregations, and transformations. Running advanced queries or performing complex analytics is becoming increasingly difficult.

To stay competitive, your team is planning a migration to an HTAP (Hybrid Transactional/Analytical Processing) SQL database—one that can seamlessly support both OLTP (Online Transaction Processing) and OLAP (Online Analytical Processing) workloads. The challenge? Zero downtime is non-negotiable. Disrupting customer operations or compromising data integrity during migration simply isn't an option.

Keep reading to discover how I solved this challenge and ensured a seamless migration based on my experience.

Researching Market Tools

There are numerous tools for ETL, and readily available options like Airbyte can help move data between databases. However, in this case, you're migrating from NoSQL to SQL, including data transformations to normalize the data. After some analysis, I decided to go with Apache Spark.

Apache Spark is known for its speed, scalability, and ability to handle massive datasets. But the real question is: how do you leverage Spark to ensure a fast, efficient ETL process that migrates your data without disrupting business operations?

Design and Architecture

To achieve zero downtime migration, I broke down the task into two key components:

1. Migration Phase: Moving the initial bulk of data to the new system.
2. Live Sync Phase: Ensuring continuous synchronization between the old and new systems during and after the migration. By designing these two phases carefully and coupling them with a timeline approach, I ensured a seamless, uninterrupted transition.

Migration Phase: Lifting the Bulk Data
The Migration Phase focuses on transferring large volumes of historical data from the source database to the target system efficiently. The goal is to move the majority of the data in one go, with minimal impact on ongoing business operations.

During this phase, Spark handles the heavy lifting. Instead of performing the migration sequentially—something that could take days or weeks for large datasets—Spark divides the data into smaller chunks called partitions. Each partition is processed in parallel across multiple worker nodes in the Spark cluster, speeding up the migration process significantly.

Live Sync Phase: Syncing the Continuous Data
While the Migration Phase focuses on transferring bulk historical data, the Live Sync Phase ensures that any ongoing changes to the data in the source database are continuously reflected in the target database in real-time. This phase keeps both systems in sync during migration and handles inserts, updates, and deletes without downtime.

By relying on real-time data processing, I ensured that new data was continuously migrated, without interrupting business operations.

Parallelism—The Secret to Speed and Efficiency

Level 1: Spark’s Native Parallelism

At the heart of Spark’s power is its ability to partition and process data in parallel across multiple nodes. By splitting large datasets into smaller partitions, Spark handles each partition independently, dramatically speeding up the entire ETL process. Spark’s built-in parallelism handles the distribution of data automatically, ensuring optimal performance without micromanaging the system.

Level 2: Custom Parallelism—Scaling with Multiple Spark Clusters

If the workload is too large for a single cluster to handle efficiently, custom parallelism comes into play. By running multiple Spark clusters in parallel, I was able to distribute the workload across different clusters, each processing a subset of the data. This horizontal scaling significantly improved performance, allowing me to manage even larger datasets across distributed environments.

In this setup, each Spark cluster operates independently, but they are orchestrated to maintain a smooth and efficient migration process. By customizing parallelism, I was able to maximize available resources, ensuring no single cluster became overwhelmed.

Monitoring and Safeguarding the Migration

To ensure a smooth, error-free migration, I designed read, write operations, and data transformations as individual action classes, each adhering to the Single Responsibility Principle. This modular approach made it easy to manage and extend the migration pipeline. I used Finite State Automata (FSA) to track the various stages of the migration, from data extraction to transformation and loading, ensuring that each step was executed in sequence and errors could be quickly pinpointed.

Error tracking was integrated into every action class, with granular logging to capture failures at specific points, making it easy to troubleshoot and recover. I also implemented regular checkpoints, which allowed me to resume the migration from the last successful point in case of failure, minimizing downtime and reprocessing. Additionally, I continuously monitored performance to track execution times, resource usage, and error rates, helping to optimize the pipeline and ensure everything ran smoothly throughout the migration.

Conclusion

As I reflect on the journey, it’s clear: Apache Spark was the true hero of the migration. From enabling efficient ETL processes with parallelism to ensuring zero downtime migration, Spark transformed the way our company handles data.

With Spark, I’ve learned that scalability, speed, and reliability don’t have to be mutually exclusive. Most importantly, I’ve unlocked the ability to migrate and transform data without ever missing a beat.

Are you ready to revolutionize your ETL workflows with Spark? Whether you're migrating data or building a real-time data pipeline, Spark’s power can help you achieve the performance, scalability, and reliability your business needs.

Feel free to share your experiences, ask questions, or let me know how you've used Apache Spark for your ETL processes. Let’s continue the conversation!