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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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The Stream Processing Model Behind Google Cloud Dataflow

Towards Data Science

This blog post is my note after reading the paper: The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing. The processing system must also be simple and flexible to adapt to the business’s complexity.

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

LinkedIn Engineering

This framework, along with Apache Spark for batch processing, formed the basis of LinkedIn’s lambda architecture for data processing jobs. The lambda architecture approach led to operational complexity and inefficiencies, because it required maintaining two different codebases and two different engines for batch and streaming data.

Process 119
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What is Data Ingestion? Types, Frameworks, Tools, Use Cases

Knowledge Hut

A Data ingestion pipeline could be grouped under several types: Batch architecture: In this system, the raw data from various sources is collected in batches and moved to a target location. The batch processing system could be triggered by a user query or scheduled automatically at specific intervals.

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Rockset Architecture Whiteboard Session With CTO Dhruba Borthakur

Rockset

Embedded content: [link] We'll be doing more videos like this in the future, so sign up for notices from our blog and join our community so you don't miss them. Earlier at Yahoo, he was one of the founding engineers of the Hadoop Distributed File System. He was also a contributor to the open source Apache HBase project.

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

LinkedIn Engineering

In the past, we often used lambda architecture for processing jobs, meaning that our developers used two different systems for batch and stream processing. In this blog post, we will share our progress, challenges, and lessons learned from implementing Apache Beam.

Process 97
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Large-scale User Sequences at Pinterest

Pinterest Engineering

So our user sequence real-time indexing pipeline is composed of a Flink job that reads the relevant events as they come into our Kafka streams, fetches the desired features for each event from our feature services, and stores the enriched events into our KV store system. The first module retrieves key-value data from the storage system.