In an Enterprise Data Lake scenario, there is a requirement for integrating complex event and streaming architectures with Azure data services resources, such as petabyte scale, big data-Hadoop equivalent file system repositories (Azure Data Lake Store), and a NoSQL multi-modeled, globally-scaled databases such as Azure Cosmos DB, and utilizing serverless configuration streams in a response to events (Azure Functions).
This chapter focuses on:
- Designing and managing data sources from Azure reference events (Blob storage)
- Enhancing interactive events with Azure Stream Analytics data sinks such as Azure Data Lake and Azure Cosmos DB
- How to elevate serverless architecture configuration using Azure Functions
In a complex event processing scenarios, a fully managed event execution engine like Azure Stream Analytics offers very low latency along with support for consistent, historical, or long-term data, and static data such as an event input...