@Jeff Born (J&CLT-ATL) - Thanks for the question and using MS Q&A platform.
Real-time data ingestion is a common requirement in modern data architectures. There are several patterns and technologies that can be used to achieve this goal. Here are some examples:
- Event-driven architecture: In this pattern, microservices publish events to a message broker (such as Azure Service Bus or Azure Event Hubs) whenever they update or insert data. Other microservices or data processing pipelines can subscribe to these events and process them in real-time. This pattern is highly scalable and decouples the microservices from each other.
- Change Data Capture (CDC): CDC is a technique that captures changes made to a database and makes them available in real-time. CDC can be used to capture changes made to a database table and publish them to a message broker or a data processing pipeline. This pattern is useful when you need to capture changes made to a database in real-time.
- Stream processing: Stream processing is a technique that processes data in real-time as it flows through a system. Stream processing engines (such as Azure Stream Analytics or Apache Kafka) can be used to process data streams and generate real-time insights. This pattern is useful when you need to process data in real-time and generate real-time insights.
Regarding your specific use case, it seems like you are currently using a batch processing approach to process data in the data lake. This approach can be slow and expensive, especially if you need to process data in real-time.
One alternative approach is to use a stream processing engine (such as Azure Stream Analytics) to process data in real-time as it flows through the system. Stream Analytics supports various input formats (including Parquet, JSON, and CSV) and can output data to various destinations (including Azure Storage, Azure Cosmos DB, and Azure SQL Database).
To handle updates and inserts based on a primary key, you can use Stream Analytics' "merge" feature. This feature allows you to merge incoming data with existing data based on a primary key. If the primary key already exists in the output, the incoming data will update the existing data. If the primary key does not exist in the output, the incoming data will be inserted as a new record.
In summary, there are several patterns and technologies that can be used to achieve real-time data ingestion. Stream processing is a popular approach that can be used to process data in real-time and generate real-time insights. Azure Stream Analytics is a powerful tool that supports various input formats and output destinations and can handle updates and inserts based on.
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