Sunday, May 15, 2016

Lambda Architecture and alternatives @LinkedIn

Questioning the Lambda Architecture - O'Reilly Media
"Jay Kreps is a Principal Staff Engineer at LinkedIn where he is the lead architect for online data infrastructure. He is among the original authors of several open source projects including a distributed key-value store called Project Voldemort, a messaging system called Kafka, and a stream processing system called Samza."
"These days, my advice is to use a batch processing framework like MapReduce if you aren’t latency sensitive, and use a stream processing framework if you are, but not to try to do both at the same time unless you absolutely must.
why can’t the stream processing system just be improved to handle the full problem set in its target domain? Why do you need to glue on another system? Why can’t you do both real-time processing and also handle the reprocessing when code changes? Stream processing systems already have a notion of parallelism; why not just handle reprocessing by increasing the parallelism and replaying history very, very fast?"

Topics - O'Reilly Media
"The essential topics and big ideas we’re tracking."

SharePoint 2016 Framework

The SharePoint Framework—an open and connected platform - Office Blogs
SharePoint is evolving ... SharePoint Framework—a Page and Part model that enables fully supported client-side development, easy integration with the Microsoft Graph and support for open source tooling.

The SharePoint framework-an open and connected platform 2

The SharePoint framework-an open and connected platform 3

Open and Connected Platform: The SharePoint Framework - YouTube

still has "WebParts"... and not web components...

AI, Open Source: Amazon DSSTNE

amznlabs/amazon-dsstne: Deep Scalable Sparse Tensor Network Engine (DSSTNE) is an Amazon developed library for building Deep Learning (DL) machine learning (ML) models @ GitHub

"Amazon DSSTNE: Deep Scalable Sparse Tensor Network Engine
DSSTNE (pronounced "Destiny") is an open source software library for training and deploying deep neural networks using GPUs. Amazon engineers built DSSTNE to solve deep learning problems at Amazon's scale. DSSTNE is built for production deployment of real-world deep learning applications, emphasizing speed and scale over experimental flexibility."