How do you handle batch and stream data in AWS?

In AWS, batch and stream data are handled with complementary services. For batch workloads, AWS Batch or AWS Glue can orchestrate large, scheduled jobs that pull data from S3, transform it, and write results back to S3 or Redshift. These services automatically provision compute resources, support job queues, and integrate with IAM for fine‑grained access. For streaming data, Amazon Kinesis Data Streams or Amazon Managed Streaming for Apache Kafka (MSK) ingest real‑time events. Consumers such as Kinesis Data Analytics, Lambda, or custom EC2 workers process records on the fly. To bridge batch and stream, you can use Kinesis Data Firehose to deliver streaming data directly into S3, Redshift, or Elasticsearch, where it can be queried or scheduled for batch processing. Monitoring is achieved with CloudWatch metrics and alarms, while data durability is ensured by Kinesis’s multi‑AZ replication and S3’s versioning. This architecture scales horizontally, decouples producers and consumers, and allows you to process data at the speed it arrives while also supporting large‑scale batch transformations.