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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.

Kafka 73
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8 Essential Data Pipeline Design Patterns You Should Know

Monte Carlo

Lambda Architecture Pattern 4. Kappa Architecture Pattern 5. Lambda Architecture Pattern Here’s where things get interesting. Lambda architecture is like having both a regular washing machine for your weekly loads AND that magical instant-wash machine. Batch Processing Pattern 2.

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Aggregator Leaf Tailer: An Alternative to Lambda Architecture for Real-Time Analytics

Rockset

To mitigate the delays inherent in MapReduce, the Lambda architecture was conceived to supplement batch results from a MapReduce system with a real-time stream of updates. This architecture has become popular in the last decade because it addresses the stale-output problem of MapReduce systems.

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StreamNative Brings Streaming Data To The Cloud Native Landscape With Pulsar

Data Engineering Podcast

How have projects such as Kafka and Pulsar impacted the broader software and data landscape? How have projects such as Kafka and Pulsar impacted the broader software and data landscape? What motivates you to dedicate so much of your time and enery to Pulsar in particular, and the streaming data ecosystem in general?

Cloud 100
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Simplifying Continuous Data Processing Using Stream Native Storage In Pravega with Tom Kaitchuck - Episode 63

Data Engineering Podcast

How does it compare with systems such as Kafka and Pulsar for ingesting and persisting unbounded data? How does it compare with systems such as Kafka and Pulsar for ingesting and persisting unbounded data? Can you start by explaining what Pravega is and the story behind it? How do you represent a stream on-disk?

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Revolutionizing Real-Time Streaming Processing: 4 Trillion Events Daily at LinkedIn

LinkedIn Engineering

In 2010, they introduced Apache Kafka , a pivotal Big Data ingestion backbone for LinkedIn’s real-time infrastructure. To transition from batch-oriented processing and respond to Kafka events within minutes or seconds, they built an in-house distributed event streaming framework, Apache Samza.

Process 119
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An Exploration Of The Expectations, Ecosystem, and Realities Of Real-Time Data Applications

Data Engineering Podcast

Links Rockset Podcast Episode Embedded Analytics Confluent Kafka AWS Kinesis Lambda Architecture Data Observability Data Mesh DynamoDB Streams MongoDB Change Streams Bigeye Monte Carlo Data The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast