Building CQRS Views with Debezium, Kafka, Materialize, and Apache Pinot — Part 1
Building an incrementally updated materialized view that serves queries faster and scalable manner?
https://t.co/EauXS0lVPl
How @upstash is making Kafka serverless?
Per-request pricing, Kafka API compatibility, and integration with serverless FaaS platforms make Upstash Kafka unique in the event streaming space.
https://t.co/NaZFfC7YxP
How To Plan and Execute an Event-Driven API Project Within Your Organization?
How to leverage the API-first approach to plan and deliver an event-driven, asynchronous API project?
#AsyncAPI#API#EventDrivenAPI
https://t.co/KCl77wEEIG
Architecting a Kafka-centric Retail Analytics Platform — Part 2
The layered architecture of the platform, types of data that exist in the retail domain, and how that should be ingested into Kafka using its ecosystem components.
https://t.co/t80E5k1NFG
Architecting a Kafka-centric Retail Analytics Platform — Part 1
What is retail analytics and why does it matter to your business? How to build a Kafka-centric analytics platform that ingests and processes business data at scale?
https://t.co/aYV8vwpTQR
Managing Production-grade Asynchronous APIs
How you can utilize an API Management solution to manage asynchronous APIs at scale? What benefits you’ll gain?
#AsyncAPI#APIManagement#EdU
https://t.co/1xdNV5bLY3
Understanding Materialized Views — 3: Stream-Table Joins with CDC
Join a stream with a lookup table to enrich the content and produce a materialized view. Then use Debezium to synchronize the lookup table with source data.
https://t.co/LprTnzJrom
Understanding Materialized Views — Part 2
Leveraging stateful stream processing to maintain materialized views that are incrementally updated
https://t.co/WqBDHnY6zs
Understanding Materialized Views — Part 1
Learn the fundamentals of materialized views, how they reduce the cost of reading queries, and what options they offer to synchronize with source data.
https://t.co/gzVPHW4acf
Azure Service Bus Essentials — Message Settlement with Peek-Lock Mode
How does a consumer use the SDK client to complete, abandon, defer, or dead-letter a received message?
#azure
https://t.co/1PiqqMnCiy
Drain a message queue faster with parallel consumers. This post explains how the Competing Consumers pattern can be used to implement that.
#EventUtopia
https://t.co/TaFtznKnNk
Message Expiration Pattern Explained.
How can a sender indicate when a message should be considered stale and thus shouldn’t be processed?
https://t.co/pTAUoVgJAO
Sending and Receiving Custom Events with AWS EventBridge Schema Registry.
How to use Schema Registry to define the format of events used in your custom applications
#AWS
https://t.co/5saRjYKx3v
Building a Low-Latency Fitness Leaderboard with Apache Pinot.
Use Apache Pinot to ingest fitness band events from a Kafka topic and make them available for immediate querying from a leaderboard web app.
https://t.co/KgNs6jVjnE
Building a Real-time Sales Dashboard with WebSockets and Quarkus.
A simple example to show how to use #WebSockets with #Quarkus
https://t.co/rJDnIQu5qf