Normalization vs. Denormalization | Events and Event Streaming скачать в хорошем качестве

Normalization vs. Denormalization | Events and Event Streaming

https://cnfl.io/event-streams-module-4 | Relational databases often use some degree of normalization in the creation of their data models, whereas document databases often lean toward a more denormalized format. This module looks at the trade-offs between normalization and denormalization of the event data model. Complete list of video tutorials in our Designing Events and Event Streams course: 1) What are Events and Event Streams? Intro to Event Design 2) Fact vs Delta Event Types 3) Hands-On Exercise: Fact vs Delta Event Types 4) Normalization vs Denormalization 5) Hands-On Exercise: Normalization vs Denormalization 6) Single vs Multiple Event Streams 7) Single vs Multiple Event Streams 8) Terminating vs Non-Terminating Entities 9) Event Design and Event Streams Best Practices Use the promo code EVENTDESIGN101 to get $25 of free Confluent Cloud usage: https://cnfl.io/try-cloud-eventdesign101 Promo code details: https://cnfl.io/eventdesign101-details LEARN MORE ► Crossing the Streams: The New Streaming Foreign-Key Join Feature in Kafka Streams: https://www.confluent.io/blog/data-en... ► Reliable Microservices Data Exchange With the Outbox Pattern: https://debezium.io/blog/2019/02/19/r... ABOUT CONFLUENT Confluent is pioneering a fundamentally new category of data infrastructure focused on data in motion. Confluent’s cloud-native offering is the foundational platform for data in motion – designed to be the intelligent connective tissue enabling real-time data, from multiple sources, to constantly stream across the organization. With Confluent, organizations can meet the new business imperative of delivering rich, digital front-end customer experiences and transitioning to sophisticated, real-time, software-driven backend operations. To learn more, please visit www.confluent.io. #streamprocessing #microservices #apachekafka #kafka #confluent