Case Study: Ambyint Leverages Etlworks for Real-Time Data Streaming and Integration

Ambyint Powers Real-Time Data Integration with MongoDB, Snowflake, and MQTT Brokers Using Etlworks.

Introduction

Ambyint, a leader in production optimization and artificial intelligence for the oil and gas industry, uses Etlworks to seamlessly integrate real-time data across diverse systems. By leveraging Etlworks’ capabilities, Ambyint synchronizes Change Data Capture (CDC) data from multiple MongoDB databases into Snowflake in real-time and connects to various MQTT brokers (v3.x and 5.x) to stream device data continuously. This setup ensures that critical data flows efficiently, empowering Ambyint to deliver actionable insights faster.

The Challenge

Ambyint faced several challenges in their data integration workflows:

Real-Time CDC: Needed a reliable solution to replicate data from multiple MongoDB databases into Snowflake in real-time.

Device Data Streaming: Required robust connectivity to multiple MQTT brokers (v3.x and 5.x) to process and stream IoT device data continuously.

Scalability: Needed a platform capable of handling high-frequency data streams and large data volumes without compromising performance.

Diverse Ecosystem: Integration workflows had to support a mix of databases, cloud platforms, and IoT protocols.

Why Etlworks

Ambyint chose Etlworks for its unmatched ability to handle complex, real-time integration workflows:

Real-Time CDC Support: Enabled seamless synchronization of MongoDB data into Snowflake with minimal latency.

MQTT Connectivity: Provided reliable integration with multiple MQTT brokers, supporting both v3.x and v5.x protocols.

Scalable Architecture: Handled high-frequency data streams and large data volumes efficiently.

Comprehensive Flexibility: Supported a wide range of protocols, databases, and cloud platforms, meeting all of Ambyint’s integration needs.

Ease of Use: Simplified the implementation of complex workflows, reducing the time to go live.

The Solution

With Etlworks, Ambyint implemented a powerful and efficient data integration framework:

MongoDB to Snowflake CDC Pipelines: Built real-time pipelines to replicate data changes from MongoDB to Snowflake, ensuring up-to-date insights.

MQTT Broker Integration: Connected multiple MQTT brokers to stream IoT device data into Ambyint’s systems for real-time processing.

Scalable and Reliable Workflows: Ensured consistent performance under high data loads, with built-in fault tolerance and recovery mechanisms.

Unified Data Ecosystem: Integrated diverse sources and protocols into a cohesive data pipeline.

Results

Seamless Real-Time Integration: Enabled real-time synchronization of MongoDB data with Snowflake, ensuring accurate and timely insights.

Continuous IoT Data Streaming: Delivered reliable connectivity to MQTT brokers, processing IoT device data in real-time.

Enhanced Performance: Handled high-frequency data streams and large datasets without performance degradation.

Scalable Operations: Supported Ambyint’s growing data integration needs with ease and reliability.

Improved Efficiency: Reduced the time and effort required to build and maintain complex integration workflows.

Key Takeaways

Real-Time CDC Pipelines: Enabled low-latency data synchronization from MongoDB to Snowflake.

IoT Device Streaming: Provided reliable integration with MQTT brokers to process real-time device data.

Scalable and Reliable: Delivered consistent performance, even under high data loads.

Unified Ecosystem: Integrated diverse data sources and protocols into seamless workflows.

Time Efficiency: Simplified implementation and maintenance of complex pipelines.

Ready to tackle your most complex data challenges? Discover how Etlworks can transform your data integration workflows. Start your free trial today or request a demo.