The performance of a machine learning model is first assessed based on its success rate. Then about the compatibility of this rate with business objectives.
This article talks about monitoring Apache Kafka on Azure using Telegraf and Grafana. I will guide you on installation, setup, and running monitoring solutions.
Lambda Architecture is one of the most useful frameworks to design big data applications and distributed data processing systems like social network platforms.
This blog post explores why a single real-time pipeline, called Kappa architecture, is the better fit and the role of batch processing in it without requiring Lambda.
In this post, you will learn to use Spring Cloud Sleuth for distributed tracing between Spring Boot microservices and Kafka with results displayed on the Zipkin server.
Data fabric is a viable strategy to help overcome the barriers that previously made it hard to access data and process it in a distributed data environment.
In this article, we will focus on which is the most appropriate big data store for high-scale, real-time, operational use cases – data fabric vs data lake.
Some scenarios in microservice-based architecture involve multiple microservices. Read how completion of the scenario depends on the task completion of each microservice.
After researching the databases and storage systems on the market, we found a more efficient and accurate real-time data warehouse solution: Pravega + TiDB.
In data science, categorical data can be considered the most usable data type. In this article, we’ll explore categorical data, types, and how to identify them.
Learn eight categories of engineering craft classifications that will help you grow as a software architect to develop depth in selected areas and awareness of others.