Abstract
In this emerging era of analytics 3.0, where big data is the heart of talk in all sectors, achieving and extracting the full potential from this vast data is accomplished by many vendors through their new generation analytical processing systems. This chapter deals with a brief introduction of the categories of analytical processing system, followed by some prominent analytical platforms, appliances, frameworks, engines, fabrics, solutions, tools, and products of the big data vendors. Finally, it deals with big data analytics in the network, its security, WAN optimization tools, and techniques for cloud-based big data analytics.
TopCategories Of Analytical Processing Systems
In his blog, Eckerson, (2013) explained, at a high-level, there are four categories of Analytical Processing Systems available in this era of Big data:
Other than these categories, there are analytical engines, frameworks, fabrics, etc., which also play a prominent role in big data analytics.
TopHadoop Distributions
Hadoop is an open source software project run within the Apache Foundation for processing data-intensive applications in a distributed environment with built-in parallelism and failover. The most important parts of Hadoop are the Hadoop Distributed File System, which stores data in files on a cluster of servers, and MapReduce, a programming framework for building parallel applications that run on HDFS (Hadoop Distributed File System)
Key Terms in this Chapter
Columnar Systems: Because of the way these systems store and compress data by columns instead of rows, columnar databases, and offer fast performance for many types of queries.
Analytical Platforms: are purpose-built and designed as SQL-based system used as data warehousing replacements or stand-alone analytical systems.
Big Data Platform: which cannot just be a platform for processing data; it has to be a platform for analyzing that data to extract insight from an immense volume, variety, velocity, value and veracity of that data.
Analytical Appliance: They arrive as an integrated hardware-software blend, tuned for analytical workloads and come in many shapes, sizes, and configurations.
In-Memory Systems: The in-memory system is ideal so that all data can be put into memory where raw performance is needed.