Big Data Analytics Beyond Hadoop: Real-Time Applications with Storm, Spark, and More Hadoop Alternatives
©2014 |Pearson FT Press | Out of print
Vijay Srinivas Agneeswaran
©2014 |Pearson FT Press | Out of print
When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to introduce these technologies and demonstrate their use in detail. An indispensable resource for data scientists and others who must scale traditional analytics tools and applications to Big Data, it illuminates these new alternatives at every level, from architecture all the way down to code. Dr. Vijay Srinivas Agneeswaran shows how to evaluate and choose the right tools, and then reengineer your solutions and products to work far more effectively in Big Data environments. Agneeswaran explains the Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, and the analysis of both performance and accuracy. He presents realistic use cases and up-to-date example code for:
Agneeswaran offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. To position you for tomorrow's advances, he identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics.
This product is part of the following series. Click on a series title to see the full list of products in the series.
Master Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning
1. Introduction to Big-data Analytics
2. Berkeley Big-data Analytics (BDA) Stack: Motivation, Design and Architecture
3. Implementing Machine Learning Algorithms with BDA
4. Real-time Analytics with Storm
5. Performance, Throughput and Accuracy Analysis
6. GraphLab: Processing Large Graphs
Master cutting-edge alternative technologies for Big Data analysis applications Hadoop can't handle well -- including real-time analysis and iterative machine learning
Pearson offers affordable and accessible purchase options to meet the needs of your students. Connect with us to learn more.
K12 Educators: Contact your Savvas Learning Company Account General Manager for purchase options. Instant Access ISBNs are for individuals purchasing with credit cards or PayPal.
Savvas Learning Company is a trademark of Savvas Learning Company LLC.
|Online purchase price||$69.99|
DR. VIJAY SRINIVAS AGNEESWARAN(Bangalore, India) is currently Director Technology/Principal Architect as head of Big Data R&D at Impetus. His R&D focuses on Big Data governance, batch and real-time analytics, and paradigms for implementing machine learning algorithms for Big Data. A professional member of ACM and the IEEE for more than 8 years, he was recently elevated to IEEE Senior Member. He has filed patents with US, European and Indian patent offices, holds two issued US patents, and has published in IEEE Transactions and other leading journals, and has been an invited speaker at multiple national and International conferences, including O’Reilly’s Strata Big Data Series.
We're sorry! We don't recognize your username or password. Please try again.
The work is protected by local and international copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning.
You have successfully signed out and will be required to sign back in should you need to download more resources.