Remove Accessibility Remove Events Remove Kafka
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They Handle 500B Events Daily. Here’s Their Data Engineering Architecture.

Monte Carlo

While not every company needs to process millions of events per second, understanding these advanced architectures helps us make better decisions about our own data infrastructure, whether we’re handling user recommendations, ride-sharing logistics, or simply figuring out which meeting rooms are actually being used.

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Troubleshooting Kafka In Production

Data Engineering Podcast

Summary Kafka has become a ubiquitous technology, offering a simple method for coordinating events and data across different systems. Can you describe your experiences with Kafka? What are the operational challenges that you have had to overcome while working with Kafka?

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Beyond Kafka: Conversation with Jark Wu on Fluss - Streaming Storage for Real-Time Analytics

Data Engineering Weekly

It addresses many of Kafka's challenges in analytical infrastructure. The combination of Kafka and Flink is not a perfect fit for real-time analytics; the integration of Kafka and Lakehouse is very shallow. How do you compare Fluss with Apache Kafka? Fluss and Kafka differ fundamentally in design principles.

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Realtime Data Applications Made Easier With Meroxa

Data Engineering Podcast

Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. What are the shifts that have made them more accessible to a wider variety of teams? Email hosts@dataengineeringpodcast.com ) with your story.

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Introducing Derivative Event Sourcing

Confluent

First, what is event sourcing? We can answer all those questions because the individual events that make up our balance are stored. In fact, it’s the summation of these events that result in our current account balance. This, in a nutshell, is event sourcing. Event sourcing: primary vs. derivative.

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Internet of Things (IoT) and Event Streaming at Scale with Apache Kafka and MQTT

Confluent

Apache Kafka ® and its surrounding ecosystem, which includes Kafka Connect, Kafka Streams, and KSQL, have become the technology of choice for integrating and processing these kinds of datasets. Use cases for IoT technologies and an event streaming platform. Example: Severstal.

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Netflix’s Distributed Counter Abstraction

Netflix Tech

By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.

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