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

Data Engineering Podcast

Links Pravega Amazon SQS (Simple Queue Service) Amazon Simple Workflow Service (SWF) Azure EMC Zookeeper Podcast Episode Bookkeeper Kafka Pulsar Podcast Episode RocksDB Flink Podcast Episode Spark Podcast Episode Heron Lambda Architecture Kappa Architecture Erasure Code Flink Forward Conference CAP Theorem The intro and outro music is from The Hug (..)

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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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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 challenges presented by streaming approaches to data transformations?

Data Lake 100
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Revolutionizing Real-Time Streaming Processing: 4 Trillion Events Daily at LinkedIn

LinkedIn Engineering

Authors: Bingfeng Xia and Xinyu Liu Background At LinkedIn, Apache Beam plays a pivotal role in stream processing infrastructures that process over 4 trillion events daily through more than 3,000 pipelines across multiple production data centers.

Process 119
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Unified Streaming And Batch Pipelines At LinkedIn: Reducing Processing time by 94% with Apache Beam

LinkedIn Engineering

Co-Authors: Yuhong Cheng , Shangjin Zhang , Xinyu Liu, and Yi Pan Efficient data processing is crucial in reducing learning curves, simplifying maintenance efforts, and decreasing operational complexity. A PTransform represents a data processing operation, or a step, in the pipeline.

Process 97
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8 Essential Data Pipeline Design Patterns You Should Know

Monte Carlo

In this guide, we’ll explore the patterns that can help you design data pipelines that actually work. Table of Contents Common Data Pipeline Design Patterns Explained 1. Batch Processing Pattern 2. Stream Processing Pattern 3. Lambda Architecture Pattern 4. Kappa Architecture Pattern 5.

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The Stream Processing Model Behind Google Cloud Dataflow

Towards Data Science

Balancing correctness, latency, and cost in unbounded data processing Image created by the author. Intro Google Dataflow is a fully managed data processing service that provides serverless unified stream and batch data processing. Table of contents Before we move on Introduction from the paper.