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8 Essential Data Pipeline Design Patterns You Should Know

Monte Carlo

They’re basically architectural blueprints for moving and processing your data. Lambda Architecture Pattern 4. Kappa Architecture Pattern 5. Lambda Architecture Pattern Here’s where things get interesting. That’s where data pipeline design patterns come in. Batch Processing Pattern 2.

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

Rockset

Aggregator Leaf Tailer (ALT) is the data architecture favored by web-scale companies, like Facebook, LinkedIn, and Google, for its efficiency and scalability. In this blog post, I will describe the Aggregator Leaf Tailer architecture and its advantages for low-latency data processing and analytics.

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

Data Engineering Weekly

Architecture Difference The first difference is the Data Model. The fourth difference is the Lakehouse Architecture. Fluss embraces the Lakehouse Architecture. On the other hand, Fluss is a Kappa Architecture ; it stores one copy of data and presents it as a stream or a table, depending on the use case.

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Exploring Processing Patterns For Streaming Data Integration In Your Data Lake

Data Engineering Podcast

What are the prevailing architectural and technological patterns that are being used to manage these systems? The Lambda architecture has largely been abandoned, so what is the answer for today’s data lakes? What are the most interesting, innovative, or unexpected ways that you have seen streaming architectures used?

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

Data Engineering Podcast

For someone who wants to build an application on top of Pravega, what interfaces does it provide and what architectural patterns does it lend itself toward? For someone who wants to build an application on top of Pravega, what interfaces does it provide and what architectural patterns does it lend itself toward?

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Rockset Architecture Whiteboard Session With CTO Dhruba Borthakur

Rockset

In this 30 minute video overview, CTO and Rockset Co-founder Dhruba Borthakur discusses Rockset's ALT architecture , how data is ingested, stored and queried in Rockset, and why Rockset is simple to use, incredibly fast, and capable of the highly efficient execution of complex distributed queries across diverse data sets.

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

Data Engineering Podcast

Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java.