Скачать или смотреть Google Dataflow - Unified Model for Batch & Streaming Data - Vadim Solovey @ DoIT (Heb)

Slides: http://bit.ly/26c6kqi
Abstract:
Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves. On top of that — consumers want answers now. At Google, we´ve evolved our earlier work on batch and streaming systems (including MapReduce, FlumeJava, and Millwheel) into Dataflow, a new programming model that allows users to clearly trade off correctness, latency, and cost.

Speaker's Bio:
Vadim is a solutions architect who designs complex and distributed systems serving millions of users for many Israeli startups.

As CTO of DoIT International, he led the implementation of Google Cloud Platforms for companies like myThings, Taboola, Datonics, Budgetao, Tabtale, BH and many more. At DoIT, Vadim works with startups to help them carefully plan, design and deploy their business on Google's Cloud Platform. For the past four years, he’s worked with more than 120 companies to establish their cloud operation with Google. He's also a DevOps expert, helping companies establish rapid software engineering processes and automations. Previously, Vadim was a CTO of WE! Consulting Group