Real-time

Packt – Real-Time Data Stream Processing in Azure

Packt – Real-Time Data Stream Processing in Azure [Video]
English | Size: 1.11 GB
Category: Tutorial


Gain a thorough understanding of Azure Stream Analytics
Discover how to ingest data using Azure Event Hubs
Find out how to archive data from Event Hubs to Azure Data Lake
Provision Event Hubs, Data Lake, and SQL Server in Azure
Listen to an event hub and stream the data to a SQL Server table
Communicate with event hubs for effortless data transfer
Discover how event hub groups work

Packt – Real Time Data Stream Processing in Azure

Packt – Real Time Data Stream Processing in Azure-RiDWARE
English | Size: 1.11 GB
Category: Tutorial


Delve into big data streaming with Azure using Event Hubs, Data Lake, and Azure Stream Analytics
More Information
Learn
Gain a thorough understanding of Azure Stream Analytics
Discover how to ingest data using Azure Event Hubs
Find out how to archive data from Event Hubs to Azure Data Lake
Provision Event Hubs, Data Lake, and SQL Server in Azure
Listen to an event hub and stream the data to a SQL Server table
Communicate with event hubs for effortless data transfer
Discover how event hub groups work

Pluralsight – Building a Real-time App with React, Flux, Webpack, and Firebase

Pluralsight – Building a Real-time App with React, Flux, Webpack, and Firebase
English | Size: 427.23 MB
Category: Tutorial


Do you feel a little bit overwhelmed by all of the other technology you need to decide on and learn in order to get your React app in production? You need to build JSX, and you probably want to use ES6 and ES7 too. How about flux, how does that work, and what library should you use to implement it? This course will show you what issues need to be addressed when you build React apps, what technology to address it, and how the technologies fit together.

Pluralsight – Real Time Personalised Customer Interaction At Scale

Pluralsight – Real Time Personalised Customer Interaction At Scale-NOLEDGE
English | Size: 100.28 MB
Category: Tutorial


Big Data LDN 2019 | Real-time Personalised Customer Interaction at Scale | Rares Rusu and Alexandru Objelean How do you build a customer
interaction platform that is agile enough to respond to constantly changing requirements and still able to perform at scale? This is the challenge the Customer Interaction team set out to solve at Paddy Power Betfair. You will learn how they rebuilt their promotional platform moving from one based on a closed slow relational database to a high performance agile platform that integrates promotions from 3rd parties. You will see how this transformation has enabled marketing to continuously innovate and apply timely, relevant personalized offers to hundreds of thousands of customers across two brands, even during massive spikes in traffic

Pluralsight – Machine Learning In Real Time Predicting Taxi Fare In NYC

Pluralsight – Machine Learning In Real Time Predicting Taxi Fare In NYC-NOLEDGE
English | Size: 78.43 MB
Category: TUtorial


Big Data LDN 2019 | Machine Learning in Real-time: Predicting Taxi Fare in NYC | Adam Jelley
Today, the benefit of Machine Learning is conditioned to its deployment in real-time. In this talk, Adam Jelley, Data Scientist will explain how to deploy a real-time taxi fare prediction engine to power an Uber-like application. Along the cycle of developing such a project, he will highlight key lessons learned like understand the problem before building models, do not add features for the sake of features, try as many algorithms as possible, and simplify your pipeline before deployment