February 2017
Beginner to intermediate
160 pages
3h 43m
English
Content preview from Big Data Now: 2016 Edition
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
Chapter 2. Tools and Architecture for Big Data
In this chapter, Evan Chan performs a storage and query cost-analysis on various analytics applications, and describes how Apache Cassandra stacks up in terms of ad hoc, batch, and time-series analysis. Next, Federico Castanedo discusses how using distributed frameworks to scale R can help solve the problem of storing large and ever-growing data sets in RAM. Daniel Whitenack then explains how a new programming language from Google—Go—could help data science teams overcome common obstacles such as integrating data science in an engineering organization. Whitenack also details the many tools, packages, and resources that allow users to perform data cleansing, visualization, and even machine learning in Go. Finally, Nicolas Seyvet and Ignacio Mulas Viela describe how the telecom industry is navigating the current data analytics environment. In their use case, they apply both Kappa architecture and a Bayesian anomaly detection model to a high-volume data stream originating from a cloud monitoring system.
Apache Cassandra for Analytics: A Performance and Storage Analysis
You can read this post on oreilly.com here.
This post is about using Apache Cassandra for analytics. Think time series, IoT, data warehousing, writing, and querying large swaths of data—not so much transactions or shopping carts. Users thinking of Cassandra as an event store and source/sink for machine learning/modeling/classification would also benefit greatly ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access
More than 5,000 organizations count on O’Reilly
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.