Uploaded February 2025 | Updated September 2026, 1 week ago
Over the years, developers have asked for guidance on designing applications with Azure Cosmos DB, from implementing common NoSQL patterns to leveraging Cosmos DB’s unique capabilities. To make these insights more accessible, we created Azure Cosmos DB Design Patterns—a GitHub repository with sample implementations to help developers solve real-world architectural challenges.
In this episode, Mark Brown and Jasmine Greenaway explore the Data Binning pattern, also known as the Windowing Pattern. This approach is ideal for handling high-frequency data, such as telemetry from IoT devices, by aggregating raw data into time-based bins. Learn how this technique reduces storage costs, improves query performance, and simplifies analytics by grouping data into meaningful intervals. See a real-world example of how data binning helps manage temperature sensor readings across thousands of hotel rooms, allowing quick anomaly detection and real-time decision-making.
github.com/Azure-Samples/cosmos-db-design-patterns/tree/main/data-binning
🔗 Explore all design patterns: https://aka.ms/AzureCosmosDB/DesignPatterns
🔗 Find all the videos in this series - https://aka.ms/AzureCosmosDB/DesignPatternsVids
#AzureCosmosDB #NoSQL #DataBinning
Useful links:
• Azure Cosmos DB Samples Gallery - https://aka.ms/AzureCosmosDB/Gallery
• Try Azure Cosmos DB for free - https://aka.ms/trycosmosdb
• Azure Cosmos DB TV - https://aka.ms/AzureCosmosDBLiveTV
• Subscribe to Azure Cosmos DB on YouTube - youtube.com/AzureCosmosDB
• Follow Azure Cosmos DB on X - twitter.com/AzureCosmosDB
• Follow Azure Cosmos DB on LinkedIn - linkedin.com/company/azure-cosmos-db
Over the years, developers have asked for guidance on designing applications with Azure Cosmos DB, from implementing common NoSQL patterns to leveraging Cosmos DB’s unique capabilities. To make these insights more accessible, we created Azure Cosmos DB Design Patterns—a GitHub repository with sample implementations to help developers solve real-world architectural challenges.
In this episode, Mark Brown and Jasmine Greenaway explore the Data Binning pattern, also known as the Windowing Pattern. This approach is ideal for handling high-frequency data, such as telemetry from IoT devices, by aggregating raw data into time-based bins. Learn how this technique reduces storage costs, improves query performance, and simplifies analytics by grouping data into meaningful intervals. See a real-world example of how data binning helps manage temperature sensor readings across thousands of hotel rooms, allowing quick anomaly detection and real-time decision-making.
github.com/Azure-Samples/cosmos-db-design-patterns/tree/main/data-binning
🔗 Explore all design patterns: https://aka.ms/AzureCosmosDB/DesignPatterns
🔗 Find all the videos in this series - https://aka.ms/AzureCosmosDB/DesignPatternsVids
#AzureCosmosDB #NoSQL #DataBinning
Useful links:
• Azure Cosmos DB Samples Gallery - https://aka.ms/AzureCosmosDB/Gallery
• Try Azure Cosmos DB for free - https://aka.ms/trycosmosdb
• Azure Cosmos DB TV - https://aka.ms/AzureCosmosDBLiveTV
• Subscribe to Azure Cosmos DB on YouTube - youtube.com/AzureCosmosDB
• Follow Azure Cosmos DB on X - twitter.com/AzureCosmosDB
• Follow Azure Cosmos DB on LinkedIn - linkedin.com/company/azure-cosmos-db
