How does Atlas handle real-time data streams and batch processing tasks?

Atlas offers functionality for real-time stream processing and batch processing tasks. For real-time stream processing, Atlas can use stream processing engines like Apache Kafka to receive and process real-time data streams. Users can handle real-time data by configuring data stream connections and transformations, storing processing results in databases or data warehouses.

For batch processing tasks, Atlas supports using batch processing engines like Apache Spark to handle large-scale data batch tasks. Users can write Spark jobs to process bulk data and store the results in a database or data warehouse.

Atlas also offers task scheduling and monitoring functions, allowing users to manage and monitor real-time data stream processing and batch processing tasks through the Atlas interface or API. Users can view the status of tasks, monitor performance metrics, and perform tasks scheduling and retry operations. Atlas also provides an alert function, allowing users to set alert rules to receive notifications when tasks encounter anomalies or performance degradation.