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image with aws fargate and lambda logos
Explainer

Fargate vs Lambda: a comparison of serverless technologies

The main difference between these two serverless compute platforms is that AWS Fargate takes care of the underlying VMs, networking, and other resources you need to run containers using ECS or EKS, whereas AWS Lambda lets you run standalone, stateless functions without having to consider any of the infrastructure whatsoever.
Mike Rosam
Words by
Banner image for the blog article "Analyze clickstream data in real time and trigger special offers based on user behavior"
Tutorial

Analyze clickstream data in real time and trigger special offers based on user behavior

Learn how to analyze clickstream data in real time using Python. Trigger frontend events and show aggregations in a real-time dashboard—using Quix, Streamlit and Redis Cloud.
Steve Rosam
Words by
Banner image for the blog article "Analyze clickstream data in real time and trigger special offers based on user behavior"
Tutorial

Analyze clickstream data in real time and trigger special offers based on user behavior

Learn how to analyze clickstream data in real time using Python. Trigger frontend events and show aggregations in a real-time dashboard—using Quix, Streamlit and Redis Cloud.
Steve Rosam
Words by
image with aws fargate and lambda logos
Explainer

Fargate vs Lambda: a comparison of serverless technologies

The main difference between these two serverless compute platforms is that AWS Fargate takes care of the underlying VMs, networking, and other resources you need to run containers using ECS or EKS, whereas AWS Lambda lets you run standalone, stateless functions without having to consider any of the infrastructure whatsoever.
Mike Rosam
Words by
Graphic featuring Amazon ECS, EKS and Fargate logos
Explainer

Amazon ECS vs. EKS. vs. Fargate: a comparison of container management services

The main difference between them? ECS and EKS are container orchestration services for Docker and Kubernetes that simplify the deployment, management, and scaling of containerized apps. Meanwhile, Fargate is a serverless compute engine that works with both ECS and EKS, removing the need to manage underlying server infrastructure.
Mike Rosam
Words by
Preview of the front end of a computer vision project template.
Explainer

Build and deploy your own traffic monitoring app using computer vision

Learn how to fork our new computer vision template and deploy an application that uses London's traffic cameras to gauge current congestion by leveraging object detection to count vehicles.
Tomáš Neubauer
Words by
Simplified diagram showing event-driven programming components (event listener, event queue, event loop, event handler)
Explainer

The what, why and how of event-driven programming

Read about the fundamentals of event-driven programming (EDP): key concepts, advantages, and challenges. Discover EDP use cases and technologies, and learn about the relation between EDP and event-driven architecture (EDA).
Tomáš Neubauer
Words by
Graphic featuring Apache Kafka and Redpanda logos
Explainer

Redpanda vs. Kafka: comparing architectures, capabilities, and performance

The main difference between them? Kafka is an established Java-based data streaming platform, with a large community and a robust ecosystem. Meanwhile, Redpanda is an emerging, Kafka-compatible tech written in C++, with an architecture designed for high performance and simplicity.
Mike Rosam
Words by
Graphic featuring Apache Kafka and ActiveMQ logos
Explainer

ActiveMQ vs. Kafka: A comparison of differences and use cases

The main difference between them is that Kafka is a distributed event streaming platform designed to ingest and process massive amounts of data, while ActiveMQ is a traditional message broker that supports multiple protocols and flexible messaging patterns.
Mike Rosam
Words by
2.0 text on radial gradient background.
Quix

Announcing Quix 2.0—now with Git integration and multiple environments

Quix 2.0 is here 🚀 Designed around the concept of Infrastructure-as-Code, Quix 2.0 makes it easier to build and run reliable, powerful event-streaming applications that scale, with a single source of truth powered by Git.
Mike Rosam
Words by
Graphic featuring Apache Kafka and RabbitMQ logos
Explainer

Apache Kafka vs. RabbitMQ: Comparing architectures, capabilities, and use cases

The main difference between them is that Kafka is an event streaming platform designed to ingest and process massive amounts of data, while RabbitMQ is a general-purpose message broker that supports flexible messaging patterns, multiple protocols, and complex routing.
Mike Rosam
Words by
Spark vs Beam image.
Explainer

Apache Beam vs. Apache Spark: Big data processing solutions compared

The main difference between Spark and Beam is that the former enables you to both write and run data processing pipelines, while the latter allows you to write data processing pipelines, and then run them on various external execution environments (runners). But what are the other differences between Spark and Beam, and how are they similar?
Alex Diaconu
Words by
Simplified diagram of a machine learning pipeline.
Explainer

The anatomy of a machine learning pipeline

Explore the characteristics, challenges, and benefits of machine learning pipelines, and read about the steps involved in training and deploying ML models to production.
Alex Diaconu
Words by
Graphic featuring Apache and Kafka logo.
Explainer

Kafka vs Pulsar: Streaming data platforms compared

An in-depth comparison of Kafka and Pulsar, covering criteria such as architectural differences, operational attributes, developer experience, ecosystems, deployment options, and security.
Alex Diaconu
Words by
Quix ML model icons on black background.
Quix

Accelerating AI-ready application development: Quix and Confluent partnership

Teams can now build AI applications on Confluent’s data in motion, with Quix, the AI-ready event streaming application framework.
Mike Rosam
Words by
Four icons connected to one box in the center.
Quix

Unlocking new use cases: Quix and Confluent partnership

Explore the AI applications that you can build when connecting Quix with Confluent.
Mike Rosam
Words by
Three data processing icons in blue background.
Explainer

The fundamentals of real-time machine learning

What is real-time machine learning? How is it different from batch ML? What are common real-time ML use cases? What are the challenges of building real-time ML capabilities? All these questions and more are answered in this article.
Mike Rosam
Words by
Man standing in front of a labyrinth illustration.
Explainer

Real-Time infrastructure tooling for data scientists

Explore the evolution of new tools for real-time pipelines that aim to solve the ongoing problem of data scientists' need for more infrastructure expertise.
Tun Shwe
Words by
Language friction image timeline.
Explainer

Feature engineering has a language problem

Should data scientists know Java? Java and Scala underpin many real-time, ML-based applications—yet data scientists usually work in Python. Someone has to port the Python into Java or adapt it to use a Python wrapper. Neither of these options is ideal, so what are some better solutions?
Tun Shwe
Words by
Orange and green chart on blue background.
Explainer

Time series analysis: a gentle introduction

Explore the fundamentals of time series analysis in this comprehensive article. Learn about key concepts, use cases, and types of time series analysis, and discover models, techniques, and methods to analyze time series data.
Javier Blanco
Words by
The stream

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