A Renewable Industry Customer's Journey with Amazon DynamoDB

Overview

A leading renewable energy company based in Czechia operates a suite of microservices to manage its operations efficiently. As the company expanded, it faced challenges related to latency, scalability, and infrastructure costs.

In this case study, we explore how this renewable industry customer harnessed Amazon DynamoDB to address these issues and enhance its renewable energy services.

The Challenge

This customer's microservices architecture initially relied on a monolithic setup, which hindered operational agility and scalability.

Additionally, the company required a solution to gather sensor data from its heat pumps. This data is then utilized to derive insights aimed at optimizing the heat pump’s operation.

The company needed a solution that would allow it to:

1. Reduce Latency: Provide a low-latency experience for its customers.
2. Optimize Costs: Minimize annual infrastructure expenses.
3. Scale Seamlessly: Handle millions of queries per second as customer demand increased.
4. Analytics and Reporting: Store heat pump sensor data and perform meaningful analytics.

The Solution: Amazon DynamoDB

The customer opted for Amazon DynamoDB, a fully managed NoSQL database service provided by Amazon Web Services (AWS). DynamoDB is specifically designed to handle high-performance applications at any scale. The data stored in DynamoDB serves as the foundation for valuable business intelligence (BI) insights.

Microservices Architecture: The company operates tens of microservices utilizing the architecture described below, with EventstoreDB serving as a third-party database for event sourcing.

Heat Pump Monitoring: To monitor heat pumps in real-time, the following architecture was designed:

1. The sensor uses the AWS IoT Device SDK to connect to AWS IoT Core in the cloud and publish sensor data.
2. AWS IoT Core receives the messages and persists them to an Amazon DynamoDB Table which has DynamoDB Streams enabled.
3. DynamoDB Streams capture item-level modifications (inserts, updates, and deletes) in the DynamoDB table.
4. These changes (data) are replicated to Amazon Kinesis data stream in near-real time which processes the data and sends it to Kinesis Data Firehose.
5. Kinesis Data Firehose consumes the records from the data stream and delivers the data to Amazon Redshift for analysis.
6. Amazon QuickSight is then used to analyze and visualize the data from the Redshift table.

Results

The adoption of DynamoDB yielded impressive outcomes:

1. 20% Reduced Median Latency: Sending messages became faster, enhancing user satisfaction.
2. Optimized Costs: Significant savings on infrastructure expenses.
3. Reliable Infrastructure: DynamoDB’s operational reliability ensured uninterrupted service for users.
4. Real-time heat pump sensor data monitoring and valuable business intelligence (BI) insights.

Conclusion

This success story demonstrates the power of Amazon DynamoDB in supporting microservices at scale. Whether you’re a startup or an established enterprise, DynamoDB offers the flexibility, performance, and cost-effectiveness needed to thrive in today’s competitive landscape.