Luis Morales

Stream data processing allows you to act on data in real time. Real-time data analytics can help you have on-time and optimized responses while improving the overall customer experience.

Data streaming workloads often require data in the stream to be enriched via external sources (such as databases or other data streams). Pre-loading of reference data provides low latency and high throughput. However, this pattern may not be suitable for certain types of workloads.

This post… Mehr anzeigen

Stream data processing allows you to act on data in real time. Real-time data analytics can help you have on-time and optimized responses while improving the overall customer experience.

Data streaming workloads often require data in the stream to be enriched via external sources (such as databases or other data streams). Pre-loading of reference data provides low latency and high throughput. However, this pattern may not be suitable for certain types of workloads.

This post covers how you can implement data enrichment for real-time streaming events with Apache Flink and how you can optimize performance. To compare the performance of the enrichment patterns, we ran performance testing based on synthetic data. The result of this test is useful as a general reference. It’s important to note that the actual performance for your Flink workload will depend on various and different factors, such as API latency, throughput, size of the event, and cache hit ratio. Weniger anzeigen

Building a streaming data solution requires thorough testing at the scale it will operate in a production environment. Streaming applications operating at scale often handle large volumes of up to GBs per second, and it’s challenging for developers to simulate high-traffic Amazon Kinesis-based applications to generate such load easily.

This post describes how to use Locust, a modern load testing framework, to conduct large-scale load testing for a more comprehensive evaluation of the… Mehr anzeigen

Building a streaming data solution requires thorough testing at the scale it will operate in a production environment. Streaming applications operating at scale often handle large volumes of up to GBs per second, and it’s challenging for developers to simulate high-traffic Amazon Kinesis-based applications to generate such load easily.

This post describes how to use Locust, a modern load testing framework, to conduct large-scale load testing for a more comprehensive evaluation of the streaming data solution. Weniger anzeigen

This blog post shows enterprise architects a mechanism to align decentralized architectural decision making with overall architecture goals