Ingest data with structured streaming
Ingestion with Structured Streaming in Azure Databricks enables real-time data processing by integrating with sources like Azure Event Hubs, Azure IoT Hub, and Apache Kafka. This approach provides a unified framework for batch and streaming data processing through Apache Spark. This capability supports autoscaling and dynamic resource allocation, ensuring performance and cost efficiency, while built-in connectors simplify pipeline setup. Advanced analytics and machine learning integration allow for real-time data analysis and model inference.
Real-time data processing:
Streamlined data ingestion: Azure Databricks allows integration with various data sources such as Azure Event Hubs, Azure IoT Hub, and Apache Kafka. This capability ensures real-time data ingestion for continuous and timely data processing.
Unified data processing: The Structured Streaming API in Azure Databricks unifies batch and streaming data processing under a single framework, enabling developers to write simple, efficient, and maintainable code for both batch and real-time data workloads.
Scalable and reliable: Azure Databricks is designed to scale and process large volumes of streaming data, using Apache Spark’s capabilities for fault tolerance to maintain data integrity and consistency across various scenarios.
Advanced features and flexibility:
Auto-scaling and dynamic resource allocation: Azure Databricks provides autoscaling and dynamic resource allocation, which automatically adjusts resources based on the workload, aiming to improve performance and cost efficiency during varying levels of demand.
Built-in connectors and support: The platform comes with built-in connectors for various data sources and sinks, including Azure Blob Storage, Azure Data Lake Storage, and SQL databases. This broad support simplifies the data pipeline setup and management.
Advanced analytics and machine learning integration: Structured Streaming in Azure Databricks seamlessly integrates with advanced analytics and machine learning libraries. This integration allows for real-time data analysis and the deployment of machine learning models to infer and act on streaming data.
Operational efficiency and monitoring:
End-to-end monitoring and management: Azure Databricks provides comprehensive monitoring and management tools to oversee streaming jobs. These tools include dashboards, alerts, and logging capabilities to ensure the smooth operation of data pipelines.
Stateful streaming: With features like stateful streaming, developers can maintain state information across micro-batches, which is essential for use cases like windowed aggregations, sessioning, and real-time analytics.
Simplified pipeline development: The platform's user-friendly interface and collaborative environment facilitate the development, testing, and deployment of streaming data pipelines. This collaborative approach enhances productivity and speeds up the development lifecycle.