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

Rockset

Traditional Data Processing: Batch and Streaming MapReduce, most commonly associated with Apache Hadoop, is a pure batch system that often introduces significant time lag in massaging new data into processed results. This architecture has become popular in the last decade because it addresses the stale-output problem of MapReduce systems.

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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? Batch and streaming systems have been used in various combinations since the early days of Hadoop. The Lambda architecture has largely been abandoned, so what is the answer for today’s data lakes?

Data Lake 100
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Rockset Architecture Whiteboard Session With CTO Dhruba Borthakur

Rockset

Earlier at Yahoo, he was one of the founding engineers of the Hadoop Distributed File System. He was an engineer on the database team at Facebook, where he was the founding engineer of the RocksDB data store. He was also a contributor to the open source Apache HBase project.

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StreamNative Brings Streaming Data To The Cloud Native Landscape With Pulsar

Data Engineering Podcast

Lambda Architecture Event Sourcing WebAssembly Apache Flink Podcast Episode Pulsar Summit The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Cloud 100
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The Stream Processing Model Behind Google Cloud Dataflow

Towards Data Science

Paper’s Introduction At the time of the paper writing, data processing frameworks like MapReduce and its “cousins “ like Hadoop , Pig , Hive , or Spark allow the data consumer to process batch data at scale. On the stream processing side, tools like MillWheel , Spark Streaming , or Storm came to support the user.

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Handling Bursty Traffic in Real-Time Analytics Applications

Rockset

Lambda Architecture: Too Many Compromises A decade ago, a multitiered database architecture called Lambda began to emerge. Lambda systems try to accommodate the needs of both big data-focused data scientists as well as streaming-focused developers by separating data ingestion into two layers.

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Maintaining Your Data Lake At Scale With Spark

Data Engineering Podcast

The Lambda architecture was popular in the early days of Hadoop but seems to have fallen out of favor. The Lambda architecture was popular in the early days of Hadoop but seems to have fallen out of favor. How does this unified interface resolve the shortcomings and complexities of that approach?

Data Lake 100