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Nov 4, 2026Read Time: 9 min Nov 4, 2026Read Time: 9 min Most modern organizations run their business across two estates. The operational estate handles a business's day-to-day needs: databases, microservices, real-time applications, and event streams like Kafka. The analytical estate runs complex, large-scale aggregations across historical data, typically stored in a data warehouse or lakehouse.
Data streaming applications continuously process incoming data, much like a never-ending query against a database. Unlike traditional database queries where you request data one time and receive a single response, streaming data applications constantly receive new data in real time. This introduces some complexity, particularly around error handling. This post discusses the strategies for handling errors in Apache Flink applications.
Amazon Kinesis Data Firehose provides a convenient way to reliably load streaming data into data lakes, data stores, and analytics services. It can capture, transform, and deliver streaming data to Amazon Simple Storage Service (Amazon S3) , Amazon Redshift , Amazon Elasticsearch Service , generic HTTP endpoints, and service providers like Datadog, New Relic, MongoDB, and Splunk.
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