From DuckDB to Apache Spark: Enhancing MigrateIO for Scalable and Efficient Data Migrations

When it comes to complex data #migrations, managing time and resources is a significant challenge. To address this, we developed #MigratIO, a scalable solution designed to simplify the migration process.

To overcome this we chose Apache #Spark as it's in-memory processing capabilities significantly accelerate data processing and reduce the latency that MigrateIO aims to achieve. Additionally, Spark's scalability and speed made it the ideal choice to handle the increasing data loads while maintaining high performance.

Our transition from DuckDB to Apache Spark has been a journey of growth and learning to the product and team but this switch wasn't without its challenges. Unlike DuckDB, integrating Spark allowed us to capitalise on our existing expertise in advanced technologies like Kubernetes and Argo, enhancing our ability to deploy Spark within the Spark Operator seamlessly and efficiently.

Another challenge was making sure our developers fully understood how Spark’s parallel distributed processing works and avoid mistakes. For example, if data isn't partitioned correctly or if intermediate results aren't cached, Spark jobs can unintentionally revert to a single-node operation, which defeats our goal of saving time and resources.

Despite that, by leveraging Spark, MigrateIO has greatly reduced processing times. For instance, we were able to transform 6 million transactions into the required format for our load systems, cutting the processing time from around 5 hours with DuckDB to just 30 minutes with Spark. By integrating Spark, MigrateIO’s capability was further enhanced to manage extensive migrations effectively, ensuring that businesses can smoothly transition their data using the product, regardless of size.

Furthermore, MigrateIO utilised the power of Argo for event-driven job submission and made the ETL processes asynchronous, which greatly improved the products efficiency. Even though now we offer the power of Spark distributed processing, we’ve given users the option to choose between DuckDB and Spark based on their data size and the costs involved.

One important thing to note is that MigrateIO as a product supports loading data to various targets, including SQL and NoSQL databases, #Kafka streams, S3 locations and APIs. This flexibility has not only improved our product’s capabilities but also helped our developers expand their skills.