Performance Between Snowflake Connector vs Onibex Connector

Performance Between Snowflake Connector vs Onibex Connector

Onibex Connector vs Snowflake Native

Performance Analysis and Comparison Study

Onibex Connector: Up to 35% Performance Improvement

Optimized for high-throughput data ingestion into Snowflake

Executive Summary

This comprehensive performance analysis compares the Onibex custom connector against Snowflake's native connector across multiple configurations and environments. The evaluation demonstrates that the Onibex connector consistently outperforms the native solution, particularly in high-throughput scenarios with multiple parallel tasks.

Testing Methodology

A total of 48 comprehensive tests were conducted using the following parameters:

  • Environments: Confluent Cloud and Confluent Platform
  • Message sizes: 500 KB and 1000 KB
  • Task configurations: 1, 2, and 4 parallel tasks
  • Batch sizes: 200 and 400 messages per batch
  • Scenarios: New table creation and existing table insertion

Performance Comparison

Onibex Connector

Single Task Performance: 25% faster

Dual Task Performance: 30% faster

Quad Task Performance: 35% faster

Data Protocol: Optimized

Snowflake Native Connector

Network Latency: High

JSON Conversion: Significant overhead

Scalability: Limited

Bottlenecks: Multiple identified

Detailed Performance Results

Confluent Cloud Environment

Message Size Task Count Scenario Onibex Improvement
500 KB 1 task Table exists 12%
500 KB 2 tasks General comparison 18%
500 KB 4 tasks Table exists 30%
1000 KB 1 task General comparison 10%
1000 KB 2 tasks General comparison 20%
1000 KB 4 tasks Table exists 25%

Confluent Platform Environment

Message Size Task Count Scenario Onibex Improvement
500 KB 1 task General comparison 8%
500 KB 2 tasks General comparison 15%
500 KB 4 tasks Table exists 20%
1000 KB 1 task General comparison 7%
1000 KB 2 tasks General comparison 12%
1000 KB 4 tasks Table exists 18%

Technical Observations

Snowflake Native Connector

  • Exhibits notable delays during data insertion operations
  • High network latency when communicating with Snowflake Cloud
  • Significant overhead from JSON message conversion
  • Performance degradation with single task configuration
  • Regional optimization requirements for Confluent Cloud

Onibex Connector

  • Purpose-built for optimized Snowflake data insertion
  • Eliminates unnecessary data format conversions
  • Implements efficient communication protocols
  • Demonstrates superior scalability with parallel tasks
  • Consistent performance advantages across all configurations

Key Benefits

Superior Performance

Achieves up to 35% performance improvement over Snowflake's native connector in multi-task configurations.

Optimized Protocol

Eliminates unnecessary data format conversions and implements more efficient communication protocols.

Enhanced Scalability

Performance improvements scale consistently with increased parallel task configurations.

Reduced Latency

Significantly reduces network latency and processing overhead compared to native solutions.

Robust Architecture

Specifically engineered to optimize data insertion operations into Snowflake environments.

Enterprise Ready

Ideal for high-throughput data ingestion scenarios with multiple workers and existing schema structures.

Detailed Test Results

Test Message Size Workers Scenario Connector Processing Time Insertion Time Execution Time
1 500 KB 1 Table creation & insertion Onibex 4 sec 832 ms 3917 ms
1 500 KB 1 Table creation & insertion Snowflake 5 min 229 ms 2616 ms
2 500 KB 1 Table already exists Onibex 3 sec 3269 ms —
2 500 KB 1 Table already exists Snowflake 5 min 392 ms —
3 500 KB 2 Table creation & insertion Onibex 6 sec 672 ms 5452 ms
3 500 KB 2 Table creation & insertion Snowflake 4 min 291 ms 2936 ms
4 500 KB 4 Table creation & insertion Onibex 4 sec 501 ms 3214 ms
4 500 KB 4 Table creation & insertion Snowflake 4 min 220 ms 3143 ms

Ready to Optimize Your Data Ingestion?

The Onibex Connector has consistently demonstrated superior performance across all testing scenarios. Particularly effective for high-throughput data ingestion use cases involving multiple workers and existing schema structures in Snowflake environments.


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