BIG DATA AND HADOOP

BIG DATA

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

WHY IS BIG DATA IMPORTANT?

The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyse it to find answers that enable

1) Cost reductions

2) Time reductions

3) New product development and optimized offerings

4) Smart decision making.

When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:

•  Determining root causes of failures, issues and defects in near-real time.
• Generating coupons at the point of sale based on the customer’s buying habits.
• Recalculating entire risk portfolios in minutes.
• Detecting fraudulent behavior before it affects your organization.

 THE 3V’S OF BIG DATA

Many of the technical industry follows Gartner’s ‘3Vs’ model to define Big Data. Data that is high in:

• ·         Volume
• ·         Velocity
• ·         Variety

The volume of data organisations handle can progress from megabytes through to terabytes and even petabytes. In terms of velocity, data has gone from being handled in batches and periodically to having to be processed in real time. The variety of data has also diversified from simple tables and databases through to photo, web, mobile and social data, and the most challenging: unstructured data.

• ·        Volume: Organizations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies (such as Hadoop) have eased the burden.
•       Velocity: Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.
•        Variety: Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.

At SAS, we consider two additional dimensions when it comes to big data:

• ·         Variability. In addition to the increasing velocities and varieties of data, data flows can be highly inconsistent with periodic peaks. Is something trending in social media? Daily, seasonal and event-triggered peak data loads can be challenging to manage. Even more so with unstructured data.
•       Complexity. Today's data comes from multiple sources, which makes it difficult to link, match, cleanse and transform data across systems. However, it’s necessary to connect and correlate relationships, hierarchies and multiple data linkages or your data can quickly spiral out of control.

HOW BIG IS ‘BIG DATA’?

Every day, we create 2.5 quintillion bytes of data – so much, that 90% of data in the world today has been created in the last two years alone.

When data sets get so big that they cannot be analysed by traditional data processing application tools, it becomes known as ‘Big Data’.

As different companies have varied ceilings on how much data they can handle, depending on their database management tools, there is no set level where data becomes ‘big’.

This means that Big Data and analytics tend to go hand-in-hand, as without being able to analyse the data it becomes meaningless.

WHAT IS BIG DATA?

Big data means really a big data; it is a collection of large datasets that cannot be processed using traditional computing techniques. Big data is not merely a data; rather it has become a complete subject, which involves various tools, techniques and frameworks.

WHAT COMES UNDER BIG DATA?

Big data involves the data produced by different devices and applications. Given below are some of the fields that come under the umbrella of Big Data.

• ·      Black Box Data: It is a component of helicopter, airplanes, and jets, etc. It captures voices of the flight crew, recordings of microphones and earphones, and the performance information of the aircraft.
• ·       Social Media Data: Social media such as Facebook and Twitter hold information and the views posted by millions of people across the globe.
• ·       Stock Exchange Data: The stock exchange data holds information about the ‘buy’ and ‘sell’ decisions made on a share of different companies made by the customers.
• ·        Power Grid Data: The power grid data holds information consumed by a particular node with respect to a base station.
• ·        Transport Data: Transport data includes model, capacity, distance and availability of a vehicle.
• ·        Search Engine Data: Search engines retrieve lots of data from different databases.

Thus Big Data includes huge volume, high velocity, and extensible variety of data. The data in it will be of three types.

• ·         Structured data: Relational data.
• ·         Semi Structured data: XML data.
• ·         Unstructured data: Word, PDF, Text, Media Logs.

BENEFITS OF BIG DATA

Big data is really critical to our life and its emerging as one of the most important technologies in modern world. Follow are just few benefits which are very much known to all of us:

• ·    Using the information kept in the social network like Facebook, the marketing agencies are learning about the response for their campaigns, promotions, and other advertising mediums.
• ·    Using the information in the social media like preferences and product perception of their consumers, product companies and retail organizations are planning their production.
• ·    Using the data regarding the previous medical history of patients, hospitals are providing better and quick service.

BIG DATA TECHNOLOGIES

Big data technologies are important in providing more accurate analysis, which may lead to more concrete decision-making resulting in greater operational efficiencies, cost reductions, and reduced risks for the business.

To harness the power of big data, you would require an infrastructure that can manage and process huge volumes of structured and unstructured data in real-time and can protect data privacy and security.

There are various technologies in the market from different vendors including Amazon, IBM, Microsoft, etc., to handle big data. While looking into the technologies that handle big data, we examine the following two classes of technology:

Operational Big Data

This includes systems like Mongo DB that provide operational capabilities for real-time, interactive workloads where data is primarily captured and stored.

No SQL Big Data systems are designed to take advantage of new cloud computing architectures that have emerged over the past decade to allow massive computations to be run inexpensively and efficiently. This makes operational big data workloads much easier to manage, cheaper, and faster to implement.

Some No SQL systems can provide insights into patterns and trends based on real-time data with minimal coding and without the need for data scientists and additional infrastructure.

Analytical Big Data

This includes systems like Massively Parallel Processing (MPP) database systems and MapReduce that provide analytical capabilities for retrospective and complex analysis that may touch most or all of the data.

MapReduce provides a new method of analysing data that is complementary to the capabilities provided by SQL, and a system based on MapReduce that can be scaled up from single servers to thousands of high and low end machines.

These two classes of technology are complementary and frequently deployed together.

HADOOP:

Hadoop is an open source, Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. It is part of the Apache project sponsored by the Apache Software Foundation.

Hadoop makes it possible to run applications on systems with thousands of commodity hardware nodes, and to handle thousands of terabytes of data. Its distributed file system facilitates rapid data transfer rates among nodes and allows the system to continue operating in case of a node failure. This approach lowers the risk of catastrophic system failure and unexpected data loss, even if a significant number of nodes become inoperative. Consequently, Hadoop quickly emerged as a foundation for big data processing tasks, such as scientific analytics, business and sales planning, and processing enormous volumes of sensor data, including from internet of things sensors.

Hadoop was created by computer scientists Doug Cutting and Mike Cafarella in 2006 to support distribution for the Nutch search engine. It was inspired by Google's MapReduce, a software framework in which an application is broken down into numerous small parts. Any of these parts, which are also called fragments or blocks, can be run on any node in the cluster. After years of development within the open source community, Hadoop 1.0 became publically available in November 2012 as part of the Apache project sponsored by the Apache Software Foundation.

Since its initial release, Hadoop has been continuously developed and updated. The second iteration of Hadoop (Hadoop 2) improved resource management and scheduling. It features a high-availability file-system option and support for Microsoft Windows and other components to expand the framework's versatility for data processing and analytics. 

WHAT IS HADOOP?

Organizations can deploy Hadoop components and supporting software packages in their local data centre. However, most big data projects depend on short-term use of substantial computing resources. This type of usage is best-suited to highly scalable public cloud services, such as Amazon Web Services (AWS), Google Cloud Platform and Microsoft Azure. Public cloud providers often support Hadoop components through basic services, such as AWS Elastic Compute Cloud and Simple Storage Service instances. However, there are also services tailored specifically for Hadoop-type tasks, such as AWS Elastic MapReduce, Google Cloud Dataproc and Microsoft Azure HDInsight.

Hadoop modules and projects

As a software framework, Hadoop is composed of numerous functional modules. At a minimum, Hadoop uses Hadoop Common as a kernel to provide the framework's essential libraries. Other components include Hadoop Distributed File System (HDFS), which is capable of storing data across thousands of commodity servers to achieve high bandwidth between nodes; Hadoop Yet Another Resource Negotiator (YARN), which provides resource management and scheduling for user applications; and Hadoop MapReduce, which provides the programming model used to tackle large distributed data processing -- mapping data and reducing it to a result.

Hadoop also supports a range of related projects that can complement and extend Hadoop's basic capabilities. Complementary software packages include:

• ·      Apache Flume. A tool used to collect, aggregate and move huge amounts of streaming data into HDFS.
• ·      Apache HBase. An open source, nonrelational, distributed database;
• ·      Apache Hive. A data warehouse that provides data summarization, query and analysis;
• ·     Cloudera Impala. A massively parallel processing database for Hadoop, originally created by the software company Cloudera, but now released as open source software;
• ·      Apache Oozie. A server-based workflow scheduling system to manage Hadoop jobs;
• ·      Apache Phoenix. An open source, massively parallel processing, relational database engine for Hadoop that is based on Apache HBase;
• ·      Apache Pig. A high-level platform for creating programs that run on Hadoop;
• ·      Apache Sqoop. A tool to transfer bulk data between Hadoop and structured data stores, such as relational databases;
• ·      Apache Spark. A fast engine for big data processing capable of streaming and supporting SQL, machine learning and graph processing;
• ·      Apache Storm. An open source data processing system; and
• ·      Apache Zookeeper. An open source configuration, synchronization and naming registry service for large distributed systems.