# Part 2: Data Integration Platforms’ Initial & Resync Time Benchmark **Published:** Mar 10, 2026 **Author:** Makoto Saito **Category:** Replication resync ## Summary This blog presents the resync times of all key players in the industry and you can choose the platform based on your requirement. Read on... ![Part 2: Data Integration Platforms’ Initial & Resync Time Benchmark](https://cdn.buttercms.com/iBuU5YmoRJK7Zno1RzOf) ## Content In Part 1 of this database replication resync time benchmark study, we discussed why minimizing your database replication resync times is of upmost importance when building mission-critical data products. In this Part 2, we share the breakdown of the tests that were carried out and the detailed results for each platform.  The six platforms that we benchmarked for their CDC database replication resync times were: * Integrate.io * Fivetran * Hevo * Matillion * Rivery * Estuary Benchmarks are all about making choices: What kind of data will I use? How much? What kind of setup? How you make these choices matters a lot: Change the configuration options in the data platforms or the structure of your data and the fastest platform can become the slowest. We’ve tried to make these choices in a way that represents a typical data user, so that the results will be useful to the kind of company that uses the data replication platforms mentioned in this benchmark study. **Table of Contents** * What Data Did We Use For Testing? * How Did We Carry Out the Comparison? * Results * How Much Has Performance Improved? * How does the Faster Sync Feature Work? * Conclusion * FAQs ## What Data Did We Use For Testing? Our goal was to measure the data replication resync times under realistic conditions. We simulated common customer scenarios for initial data loads for the following two sets of databases. 1. Small Database: Type: AWS MySQL Aurora Size: 10 million rows, [16 columns] Connectivity Method: Direct 2. Type: AWS MySQL Aurora 3. Size: 10 million rows, [16 columns] 4. Connectivity Method: Direct 5. Large Database: Type: AWS MySQL Aurora Size: 300 million rows, [7 columns] Connectivity Method: Direct 6. Type: AWS MySQL Aurora 7. Size: 300 million rows, [7 columns] 8. Connectivity Method: Direct ## How Did We Carry Out the Comparison? We benchmarked the six data replication platforms to compare initial database sync and resync times using the two sample databases. ### Test Setup and Configuration * Source Database: AWS MySQL Aurora * Target Data Warehouse: Snowflake on AWS * Replication Method: Direct connectivity for both small and large datasets. * Measurement: Initial sync times were recorded for each platform under identical conditions. ## Results Initial Sync Times Integrate.io Fivetran Hevo Matillion Rivery Estuary Small Database 3 minutes 44 seconds 5 min & 29 sec 22 min 5 min 21 min & 42 sec 42 min Large Database 19 minutes 17 seconds 49 min & 39 sec 10 hours & 47 min 39 min 13 hours, 42 min & 53 sec 3 hours ### Integrate.io ### Hevo ## ### Fivetran ## ## ## ### Matillion ## ### Rivery ### Estuary ### The complete log history which is used for calculating the time taken for the sync is attached below: ## Conclusion   Data pipeline platforms have revolutionized the way businesses centralize and leverage their data for downstream applications such as data products, analytics, and machine learning. As data integration becomes more critical, choosing the right platform based on your use cases and requirements is key.  Our recent benchmark comparison shows that Integrate.io excels in delivering the fastest initial sync and resync times when replicating database data to a data warehouse. This is critical for companies building business-critical data products being powered with data from their data warehouse and have strict SLAs for data uptime.  To discuss your data uptime needs and learn more about the industry's fastest initial sync and resync times, you can schedule a time to speak with one of our Solution Engineers [here](https://www.integrate.io/demo/) or get started with a free trial. ## Tags data replication initial sync, English, replication resync ## Categories Replication resync