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8 matches found for 'event streaming'

Apache Kafka and Event Streaming

Introduction Apache Kafka is an open-source distributed event streaming platform. Traditional message brokers are based off of the JMS / AMQP standard. These message brokers focus on a pub/sub model where publishers write messages to a queue and the queue is consumed by subscribers.


Traditional Message Queues vs. Log-based Message Brokers

Traditional Message Queues Traditional message queues are based off of the JMS / AMQP standard. These message brokers focus on a pub/sub model where publishers write messages to a queue and the queue is consumed by subscribers.


What is DDD? What is CQRS?

Domain Driven Design DDD is an approach to developing software systems that is large and complex, and has ever-changing business rules. DDD captures the sweet spot between the business knowledge and the code.


Data stores in Software Architectures

Use Cases There are many ways to store your data. In this article we'll walk through some examples of data storage in common system designs. Reminder: There is no single best storage choice and they may vary heavily depending on things such as access patterns and scale.


Kefir.js - Reactive Javascript

Background Kefir.js is a Reactive Programming library for JavaScript inspired by Bacon.js and RxJS, with focus on high performance and low memory usage. Kefir works with objects called observables. observables could be two things; a stream, or a property (not to be confused with a Javascript object property) Streams A stream is a sequence of events made available over time.


AWS Lambda and other Maven projects

Background AWS Lambda is a FaaS (Function as a service) that is event-driven and serverless. It is termed event-driven due to how AWS Lambda functions are invoked - the event that triggers a AWS Lambda function can be of many different types in the AWS realm.


Python Essentials

Scopes Python has closures, similar to Javascript, since functions are first class objects. But unlike Javascript, there are some subtle gotchas in regards to working with function scopes. nonlocal vs.


AWS and MLOps

Machine Learning Development Lifecycle The lifecycle of the machine learning development process often follows these steps: 1. Data Collection In this step, we fetch data from various sources. Common examples include a data lake, a data catalog, or streaming data (like Kafka, Kinesis).