jm + lambda-architecture   8

Open Sourcing Twitter Heron
Twitter are open sourcing their Storm replacement, and moving it to an independent open source foundation
open-source  twitter  heron  storm  streaming  architecture  lambda-architecture 
may 2016 by jm
Scaling Analytics at Amplitude
Good blog post on Amplitude's lambda architecture setup, based on S3 and a custom "real-time set database" they wrote themselves.

antirez' comment from a Redis angle on the set database: http://antirez.com/news/92

HN thread: https://news.ycombinator.com/item?id=10118413
lambda-architecture  analytics  via:hn  redis  set-storage  storage  databases  architecture  s3  realtime 
august 2015 by jm
The world beyond batch: Streaming 101 - O'Reilly Media
To summarize, in this post I’ve:

Clarified terminology, specifically narrowing the definition of “streaming” to apply to execution engines only, while using more descriptive terms like unbounded data and approximate/speculative results for distinct concepts often categorized under the “streaming” umbrella.

Assessed the relative capabilities of well-designed batch and streaming systems, positing that streaming is in fact a strict superset of batch, and that notions like the Lambda Architecture, which are predicated on streaming being inferior to batch, are destined for retirement as streaming systems mature.

Proposed two high-level concepts necessary for streaming systems to both catch up to and ultimately surpass batch, those being correctness and tools for reasoning about time, respectively.

Established the important differences between event time and processing time, characterized the difficulties those differences impose when analyzing data in the context of when they occurred, and proposed a shift in approach away from notions of completeness and toward simply adapting to changes in data over time.

Looked at the major data processing approaches in common use today for bounded and unbounded data, via both batch and streaming engines, roughly categorizing the unbounded approaches into: time-agnostic, approximation, windowing by processing time, and windowing by event time.
streaming  batch  big-data  lambda-architecture  dataflow  event-processing  cep  millwheel  data  data-processing 
august 2015 by jm
Elements of Scale: Composing and Scaling Data Platforms
Great, encyclopedic blog post rounding up common architectural and algorithmic patterns using in scalable data platforms. Cut out and keep!
architecture  storage  databases  data  big-data  scaling  scalability  ben-stopford  cqrs  druid  parquet  columnar-stores  lambda-architecture 
may 2015 by jm
RADStack - an open source Lambda Architecture built on Druid, Kafka and Samza
'In this paper we presented the RADStack, a collection of complementary technologies that can be used together to power interactive analytic applications. The key pieces of the stack are Kafka, Samza, Hadoop, and Druid. Druid is designed for exploratory analytics and is optimized for low latency data exploration, aggregation, and ingestion, and is well suited for OLAP workflows. Samza and Hadoop complement Druid and add data processing functionality, and Kafka enables high throughput event delivery.'
druid  samza  kafka  streaming  cep  lambda-architecture  architecture  hadoop  big-data  olap 
april 2015 by jm
Twitter's Answers architecture
Twitter's mobile-device analytics service architecture, with Kafka and Storm in full Lambda-Architecture mode
twitter  lambda-architecture  storm  kafka  architecture 
february 2015 by jm
Questioning the Lambda Architecture
Jay Kreps (Kafka, Samza) with a thought-provoking post on the batch/stream-processing dichotomy
jay-kreps  toread  architecture  data  stream-processing  batch  hadoop  storm  lambda-architecture 
july 2014 by jm
Big Data Lambda Architecture
An article by Nathan "Storm" Marz describing the system architecture he's been talking about for a while; Hadoop-driven batch view, Storm-driven "speed view", and a merging API
storm  systems  architecture  lambda-architecture  design  Hadoop 
january 2013 by jm

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