Current Technology - Big Data Training Chennai

Big Data Visualization

We've entered a data-driven era, in which data are continuously acquired for a variety of purposes. The ability to make timely decisions based on available data is crucial to business success, clinical treatments, cyber and national security, and disaster management. Additionally, the data generated from large-scale simulations, astronomical observatories, high-throughput experiments, or high-resolution sensors will help lead to new discoveries if scientists have adequate tools to extract knowledge from them.

However, most data have become simply too large and often have too short a lifespan. Almost all fields of study and practice sooner or later will confront this big data problem. Government agencies and large corporations are launching research programs to address the challenges presented by big data. Visualization has been shown to be an effective tool not only for presenting essential information in vast amounts of data but also for driving complex analyses. Big data analytics and discovery present new research opportunities to the computer graphics and visualization community. This 2013 theme issue of IEEE Computer Graphics and Applications aims to highlight the latest advancements in solving the big data problems via visual means. Computer magazine will also be publishing a special issue on big data in June 2013.

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Big Data, cloud to create new platform for enterprises

IT-ITeS industry body Nasscom said it expects emerging technologies like Big Data, cloud and mobility to create a new platform for enterprises to develop new business capabilities across verticals. These capabilities will enable a more integrated view of Indian IT architecture, Nasscom President Som Mittal said.

The industry body aims to achieve revenue of $300 billion for the IT industry by 2020 and it is confident that Internet and mobile technology, big data and cloud computing will form a major driving force for it to achieve this target, he added.

Big Data Training Chennai

Apache Hadoop’s market will expand to $2.2 billion by 2018, according to a new report by Market Research Media. A new report by Market Research Media suggests that the current spike of interest in Apache Hadoop will continue for the next several years.

Apache Hadoop is an open-source framework for reliably running distributed applications on large hardware clusters, and has become a go-to technology for many companies (including IBM and Facebook) with epic amounts of data to crunch. The MapReduce framework processes problems across huge datasets and clusters of machines, deriving its reliability from the master node’s facility for distributing work to nodes within the cluster as appropriate.

Suggesting “it would not be an exaggeration to say that today Hadoop-MapReduce is the only cost-sensible and scalable Big Data management alternative” to commercial platforms on the market, the market-research firm predicts that the market for the framework will expand at a compound annual growth rate (CAGR) of 58 percent over the next few years, becoming a $2.2 billion market by 2018.

Global Hadoop Market worth $13.95 Billion by 2017

According to a new market research report, “Hadoop Market [By Hardware (Servers, Storage & Network Equipment); By Software (Packaged, Management, Application and Performance Monitoring); By Services (Professional, Technical & Cloud)] - Global Advancements, Business Models, Technology Roadmap, Forecasts & Analysis (2012 - 2017)”, published by MarketsandMarkets (www.marketsandmarkets.com), the total Hadoop market is expected to reach $13.95 Billion by 2017 with a CAGR of 54.9%. 

BigData Training- @ Chennai - BigCHALLENGE

INTRODUCTION: THE CHALLENGE OF BIG DATA

The amount of data in the world is being collected and stored at unprecedented rates. A study by IDC Gantz & Reinsel, (2011) indicates that the world’s information is doubling every two years. 

Also the IDC study by Gantz & Reinsel (2011), mentions that the world created a staggering 1.8 zettabytes of information (a zettabyte is 1000 exabytes), and projections suggest that by 2020, we’ll generate will generate 50 times that amount.

Big Data has been defined as, when data sets get so large, that traditional technologies, techniques, and tools for extracting insights are no longer useful in a reasonable timeframe and cost-effective manner. This has spawned a new generation of technologies and corresponding considerations. Desai, Kommu & Rapp (2011) examine the cause of this explosion of Big Data, the following factors dominate:

·         Mobility trends: Mobile devices and sensor proliferation;

·         New data access: Internet, interconnected systems, and social networking;

·         Open source model: Major changes in the information processing model and the availability of an open source framework.

What distinguishes Big Data from data in the past, however, is not just its vast volume. The defining features of Big Data are also its variety—the sources and types of data being collected—and its velocity, the speed at which the data is flowing through the networked systems. Studies like Cisco Virtual Networking Index by Barnett, (2011) estimate that in 2016, global IP traffic will reach 1.3 zettabytes per year or 110.3 exabytes per month. Moreover, it is anticipated that there will be 19 billion networked devices by 2016.

One of the most interesting aspects about Big Data is that that unstructured data is the fastest growing type of data. Unstructured data refers to information that either does not have a predefined data model or does not fit well into relational database tables. 

Examples of unstructured data include imagery, sensor data, telemetry data, video, documents, log files, and email files. The challenge is not only to store and manage this vast mix, but to analyze and extract meaningful value from it—and to do so in a reasonable timeframe and at a reasonable cost.

Step by Step learning guide for Hadoop, Big Data - BigDataTraining.IN

Apache Hadoop mainly consists of two major components:

• The Hadoop Distributed File System (HDFS)
• The MapReduce software framework

HDFS is responsible for storing the data in a distributed manner across multiple Hadoop cluster nodes. The MapReduce framework provides rich computational APIs for developers to code, which eventually run as map and reduce tasks on the Hadoop cluster.

The ecosystem of Apache Hadoop

Understanding Apache Hadoop ecosystem enables us to effectively apply the concepts of the MapReduce paradigm at different requirements. It also provides end-to-end solutions to various problems that are faced by us every day.

Apache Hadoop ecosystem is vast in nature. It has grown drastically over the time due to different organizations contributing to this open source initiative. Due to the huge ecosystem, it meets the needs of different organizations for high performance analytics.

Apache HBase

HDFS is append-only file system; it does not allow data modification. Apache HBase is a distributed, random-access, and column-oriented database. HBase directly runs on top of HDFS, and it allows application developers to read/write the HDFS data directly. HBase does not support SQL; hence, it is also called as NOSQL database. However, it provides command-line-based interface, as well as a rich set of APIs to update the data. The data in HBase gets stored as key-value pairs in HDFS.

 

Apache Pig

Apache Pig provides another abstraction layer on top of MapReduce. It provides something called Pig Latin, which is a programming language that creates MapReduce programs using Pig. Pig Latin is a high-level language for developers to write high-level software for analyzing data. Pig code generates parallel execution tasks, therefore effectively uses the distributed Hadoop cluster. Pig was initially developed at Yahoo! Research to enable developers create ad-hoc MapReduce jobs for Hadoop. Since then, many big organizations such as eBay, LinkedIn, and Twitter have started using Apache Pig.

 

Apache Hive

Apache Hive provides data warehouse capabilities using Big Data. Hive runs on top of Apache Hadoop, and uses HDFS for storing its data. The Apache Hadoop framework is difficult to understand, and it requires a different approach from traditional programming to write MapReduce-based programs. With Hive, developers do not write MapReduce at all. Hive provides a SQL like query language called HiveQL to application developers, enabling them to quickly write ad-hoc queries similar to RDBMS SQL queries.

Apache Sqoop

Apache Sqoop is a tool designed to do load large datasets in Hadoop efficiently. Apache Sqoop allows application developers to import/export easily from specific data sources such as relational databases, enterprise data warehouses, and custom applications. Apache Sqoop internally uses a map task to perform data import/export effectively on Hadoop cluster. Each mapper loads/unloads slice of data across HDFS and data source. Apache Sqoop establishes connectivity between non-Hadoop data sources and HDFS.