He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics.
Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students. March 15, Also, you will delve into Spark and its related tools to perform real-time data analytics, streaming, and batch processing on your application.
Finally, you'll learn how to extend your analytics solutions to the cloud. Download Big Data Analytics Beyond Hadoop books , Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning.
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 help you take the next steps beyond Hadoop.
He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parallel real-time Big Data analytics technology from Twitter GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington with comparisons to alternatives such as Pregel and Piccolo Halo also 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. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics. Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students.
Handling big data, be it of good or bad quality, is not an easy task. The prime job for any big data architect is to build an end-to-end big data solution that integrates data from different sources and analyzes it to find useful, hidden insights.
For example, the company might see that the Vietnamese dish pho is trending in online searches and add that to the menu. Tas Bindi talked with the CEO of online bookseller Booktopia about how the company used analytics tools to improve the book buying experience and increase sales. And in a case study of online home goods retailer Wayfair, Alison DeNisco details how the company increased sales by using big data to create a feature which allowed shoppers to search the site with a photo and find like items.
Who This Book Is For Though this book is primarily aimed at data analysts and data scientists, it will also help architects, programmers, and practitioners. Knowledge of either Spark or Hadoop would be beneficial. Working experience within big data environments is not mandatory. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds.
Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.
Style and approach This step-by-step pragmatic guide will make life easy no matter what your level of experience. You will deep dive into Apache Spark on Hadoop clusters through ample exciting real-life examples.
Practical tutorial explains data science in simple terms to help programmers and data analysts get started with Data Science. This book reveals how IBM is leveraging open source Big Data technology, infused with IBM technologies, to deliver a robust, secure, highly available, enterprise-class Big Data platform. The Berkeley researchers have proposed Berkeley Data Analytics BDA stack as a collection of technologies that help in running data analytics tasks across a cluster of nodes.
The lowest level component of the BDA is Mesos, the cluster manager which helps in task allocation and resource management tasks of the cluster. The second component is the Tachyon file system built on top of Mesos. Tachyon provides a distributed file system abstraction and provides interfaces for file operations across the clus- ter. Spark, the computation paradigm is realized over Tachyon and Mesos in a specific embod- iment though it could be realized without Tachyon and even without Mesos for clustering.
Shark which is realized over Spark provides an SQL abstraction over a cluster — similar to the abstraction Hive provides over Hadoop. The other important paradigm that has looked beyond Hadoop Map-Reduce is graph pro- cessing, exemplified by the Pregel effort from Google.
Pregel is a Bulk Synchronous Processing BSP paradigm where user defined compute functions can be spawned on the nodes of the graph, with edges used for communication. This provides a deterministic computation frame- work. Apache Giraph is an open source implementation of Pregel.
GraphX is the other system with specific focus on graph construction and transformations. While Pregel is good at graph parallel ab- straction, easy to reason with and ensures deterministic computation, it leaves it to the user to architect the movement of data.
Further, like all BSP systems, it also suffers from the curse of the slow jobs — meaning that even a single slow job which could be due to load fluctuations or other reasons can slow down the whole computation.
GraphLab as well as its subsequent version known as Powergraph constitute the state of the art in graph processing and are especially well suited to power law graphs.
Another interesting effort in this space is the Graph Search work from Facebook, who are building search over what they term as an entity graph. Another interesting effort in the third generation paradigms comes from Twitter, who built the Storm framework for real-time complex event processing.
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