Big Data Project

My Sincere thanks to Prof Sandeep Kelkar for his guidance, insights during key stages of the project and extending his support during the entire project lifecycle.

This may sound silly, but I would like to thank Google for the search product and also ebook like big data in IBM which is tremendously effective to give access to the  books and corners in the th e internet over a vast sea of o f information.

Place : Mumbai Date: September 30, 2013

Prasad Bhoja Amin

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INDEX

Sr. No.

Table of Contents

Page No.

1

Introduction

1-33

2

Executive Summary

3

Design of Survey

4

Data Collection Summary

5

Data Analysis

41-48

6

Interfaces / Key Findings

49-50

7

Conclusions

51

8

Suggestions

52-56

9

Bibliography

57

10

SPSS Tool, Analysis.Digram& PIE

34 35-39 40

58-96

Chart (OUTPUT)

2

INDEX

Sr. No.

Table of Contents

Page No.

1

Introduction

1-33

2

Executive Summary

3

Design of Survey

4

Data Collection Summary

5

Data Analysis

41-48

6

Interfaces / Key Findings

49-50

7

Conclusions

51

8

Suggestions

52-56

9

Bibliography

57

10

SPSS Tool, Analysis.Digram& PIE

34 35-39 40

58-96

Chart (OUTPUT)

2

Table of contents

Content

Page No.

1.0 What is big data?

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1.1 The Importance of Big Data and What You Can Accomplish 1.2 1.3 Big Data has three characteristic

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1.3 Why big data is important it is shown in diagram? 1.5 Big data steps, vendors and technology landscape

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1.6 Operational Definitions 1.6.1

Data Scientist

1.6.2

Massive Parallel Processing

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1.6.3

In Memory analytics

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1.6.4

Redundant Array of Independent Disk (RAID)

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1.6.5

What business problems are being targeted?

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1.6.6

Structured , Semi-Structured & Unstructured Data

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2.0 Big Data Infrastructure

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2.1.1

Why Raid fail at scale

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2.1.2

Scale up v/s scale out NAS

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2.1.3 EMC ISILON

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2.2 Apache Hadoop

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2.2.1

Data Appliances

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2.2.2

HP Vertica

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2.2.3

Terradata Aster

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3.0 Domain Wise Challenges in big data Era 3.1 Log Management

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3.2 Data Integrity & Reliability in the big data era

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3.3 Backup Management in bid data era

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3.4 Database Management in big data era

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4.0 Big Data Use Cases: 4.1 Potential use Cases

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4.2 Big Data Actual Use Cases

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4.3 In IBM Big Data used for

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The internet has grown tremendously in the last decade, from 304 million users in March 2000 to 2280 million users in March 2012 according to internet worlds stats. Worldwide information is more than doubling every two years , with 1.8 zettabytes or 1.8 trillion gigabytes projected to be created and replicated in 2011 according to the study conducted by research firm IDC.

A buzzword,or catch  –   phrase, used to describe a massive volume of both structured and unstructured data that is so large that is difficult to process with traditional database and software techniques is “Big Data”. An example of big data might be

 perabytes(1,024 terabytes) or exabytes (1,024 petabytes) and zettabytes of data consisting of billions to trillions of records of million of people

All from different sources (e.g blogs,social media,email,sensors,RFID readers ,  photographs,videos, microphones,mobile data and so on). The data is typically loosely structured data that is often incomplete and inaccessible. When dealing with larger datasets, organizations face difficulties in being able to create, manipulate , and manage Big Data. Scientists regularly encounter this problem in meteorology, Genomics, connectomics, complex physics simulations ,biological and environmental research, internet search , finance and business informatics. Big data is particularly a  problem in business analytics because standard tools and procedures are not designed to search and analyze massive datasets. While the term may seem to reference the volume of data , that isn‟t always the case.  The term Big data, especially when used

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 by vendors, refer to the technology(the tools and processes) that an organization requires to handle the large amounts of data and storage facilities. Over a distributed storage system

Hadoop used to process unstructured and semistructured big data uses the map  paradigm to locate all relevant data then select only the data directly answering the query.NoSQL,MongoDB and TerraStore process structured big data. Nosql data is characterized by being basically available,soft state (changeable), and eventually consistent .MongoDB and Terrastore are both no sql-related products used for document –   orient applications .The advent of the age of big data poses opportunities and challenges for businesses.Previously unavailable forms of data can now  besavved,retrieved and processed . However, change to hardware ,software , and data  processing techniques are necessary to employ this new paradigm.

1.1 What is big data ?

Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. And big data may be as important to business –  and society –   as the Internet has become. Why? More data may lead to more accurate analysis. More accurate analysis may lead to more confident decision making. And  better decisions can mean greater operational efficiencies, cost reductions and reduced risk. Data Growth Curve :- Terabytes  Petabytes Exabyteszettabytesyottabytes bronotobytes-geopbytes

. it getting more

interesting.

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Analytical Infrastucturecurve :- Databases datamartsoperational data stores(ods) enterprise

data warehouses  data appliances in-memory appliances  no sql

databases hadoop clusters

1.2 The Importance of Big Data and What You Can Accomplish

The real issue is not that you are acquiring large amounts of data. It's what you do with the data that counts. The hopeful vision is that organizations will be able to take data from any source, harness relevant data and analyze it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smarter business decision making. For instance, by combining big data and high-powered analytics, it is possible to:

1.3 Big Data has three characteristic 1. Velocity

2. Volume

3. Variety

Figure 1.1 Characteristics of Big Data

VARIETY {Structure --> Unstructure}

VELOCITY {Batch Processing --> Video Streaming}

VOLUME {ZettaByte to TeraByte}

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Source: IBM, Hadoops

1.4 Why big data is important it is shown in diagram?

For .e.g if you see in IBM BIG insight having a log analysis for performance optimizer. Log is a volume,it can be formed in semistructure or row format. When there is a up gradation in any organization for e.g we can say upgrading the operating system or database or migration that time log also change the format sometimes.

We having another example why big data is important for e.g we can say any public sector and private sector bank having big data when customer come to the bank ,or customer having daily transaction in debit card and credit card . when transaction goes on log file has been generated . In online transaction each day log file having more than 5 terabytes. It is day to day log generated .we cant do delete logfile because it is more useful in organization.

1.5 Big data steps, vendors and technology landscape

1. Data Acquisition Data is collected from the data sources and distributed across multiple nodes –  often a grid –  each of which processes a subset of data in parallel. Here we have technological provier like IBM ,HP etc and data providers like reuters, saleforce etc and social network websites like facebook,google +,LinkedIn etc.. 2. Marshallling

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In This domain , we have very large data warehousing and BI appliances,actors like action ,emc2(greenplum),hp(vertica),IBM (netezza) etc. 3. Analytics In this phase ,we have the predictive technologies (suach as data mining )and vendors which are adobe ,emc2,good data ,hadoop map reduce etc.. 4. Action Includes all the data acquisition providers plus the ERP ,CRM, and BPM actors including adobe,eloqua ,emc2 etc.both in analytical and action phases , BI tools vendors are good data ,google, hp (autonomy),IBM (cognos suite)etc. 5. Data Governance An efficient master data management solution. As defined ,data governance applies to each of the six preceding stages of big data dlivery.By establishing process and guiding principles it sanctions behaviors around data delivery.By establishing  processes and guiding principles it sanctions behaviours around data. In short data governance means that the application of big data is useful and relevant. Its an insurance policy that the right questions are being asked.so we won‟t be squandering

the immense powe of new big data technologies that make processing storage and delivery speed more cost effective and nimble than ever.

1.6 Operational Definitions 1.6.1 Data Scientist

A data scientist represents an evolution from the business or data analyst role. Data scientists also known as data analysts  –   are professionals with core statistics or mathematics background coupled with good knowledge in analytics and data software tools. AMckinsey study on big data states indiawil need nearly ,100,000 data

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scientists in the next few years.”A data Scientists is a fairly new role defined by Hillary mason of big as someone who can obtain ,srub, explore,model and interpret data, blending hacking, statistics and machine learning who culls information from data. These data scientists take a blend of the hackers arts, statistics and machine learning and apply their expertise in mathematics and understanding the domain of the data  –   where the data originated  –   to process the data into useful information . This require the ability to make creative ecisions about the data and the information created and maintaining a perspective that goes beyond ordinary scientific boundaries.

1.6.2 Massive Parallel Processing (MPP)

Mpp is the coordinated processing of a program by multiple processors that work on different parts of the program, with each processor using its own operating system and memory. An MPP system is considered better than a symmetrically parallel system(SMP) for applications that allow a number of databases to be searched in  parallel. These include decision support system and data warehouse applications.

1.6.3 In memory analytics

The key difference between conventional BI tools and in memory products is that the former query data on disk while the latter query data in random access memory(RAM).When a user runs a query against a typical data warehouse, the query normally goes to a database that reads the information from multiple tables stored on a server‟s shared Itdisk. With a serverbased inmemory database,all information is

initially loaded into memory. Users than query and interact with the data load into the machine‟s memory.

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1.6.4 Redundant Array of Independent Disk (RAID)

RAID is short for redundant array of independent (or inexpensive) disks.It is a category of disk drives that employ two or more drives in combination for fault tolerance and performance. RAID disk drives are used frequently on servers but aren't generally necessary for  personal computers. RAID allows you to store the same data redundantly (in multiple paces) in a balanced way to improve overall storage  performance

1.6.5 What business problems are being targeted?

2. modeling true risk

8. Precision targeting,

3. customer churn analysis

9. Pos transaction analysis

4. Flexible supply chains,

10. Threat analsysis

5. Loyalty pricing,

11. Trade surveillance

6. Recommendation engines,

12. Search quality fine tuningand

7. Ad targeting

13. Mashups such as location +ad targeting.

Does an in-memory analytics platform replace or augment traditional indatabase approaches?

The answer is that it is quite complementary. In –  database approaches put a large focus on the data preparation and scoring portions of the analytic process. The value of in-database processing is the ability to handle terabytes or petabytes of data effectively. Much of the processing may not be highly sophisticated but it is a critical.The new in memory architiectures use a massively parallel platform to enable the mutilpe terabytes of system memory to be utilized (conceptually) as one big pool of memory. This means that samples can be much larger, or even eliminated. The number of variables tested can be expanded immensely.

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In  –  Memory approaches fit best in situations where there is a need for:

1. High Volume & speed: it is necessary to run many,many models quickly 2. High Width &Depth : It is desired to test hundred or thousands of metrics across tens of millions customers. 3. High complexity : It is critical to run processing-intensive algorithms on all this data and to allow for many iterations to occur.

Tics

1. In Memory OLAP :- Classic MOLAP (multidimensional online analytical  processing ) cube loaded entirely in memory. 2. In Memory ROLAP : Relational OLAP metadata loaded entirely in memory. 3. In Memory inverted index :- Index with data loaded inot memory. 4. In Memory associative index : An Array / index with every entity / attribute correlated to every entity/attribute. 5. In-memory spreadsheet :- Spreadsheet like array loaded entirely into memory.

1.6.6 Structured , Semi  – Structured and unstructured Data Structured Data is that type that would fit nearly into a standard relational database

management system,RDBMS, and lend itself to that type of processing. Semi-Structured Data  is the which has some level of commonality but does not fit

the structured data type Unstructured Data is the type that varies in it content and can change from entry to

entry. Structured data

Semi structure data

Unstructured data

Customer Records

Web Logs

Pictures

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Point of Sale data

Social Media

Video Editing Data

Inventory

E-Commercce

Productivity (office docs)

Financial Records

Geological Data

2.0 Big Data Infrastructure 2.1.1 Why RAID Fails at Scale

RAID schemes are based on parity and at its root, if more than two drives fail simultaneously , data is not recoverable . The Statistical likelihood of multiple drive failures has not been an issue in the past. However as drive capacities continue to grow beyond the terabyte range and storage systems continues to grow to hundreds of terabytes and petabytes, the likelihood of multiple drive failures is not reality. Further drives aren‟t perfect and typical SATA drives have a published bit rate

error(BRE) of 10 14, meaning100, 000,000,000,000 bits there will be a bit that is unrecoverable.Doesn‟t seem signigicant ? In Today bid data storage systems it is the

likelihood of having one drive fail, and encountering a bit rate error when rebuilding from the remaining RAID set is highly probable in real world scenarios. To put this inot perspective, when reading 10 terabytes , the probability of an unreadable bit is likely (565%) and when reading 100 terabytes it is nearly certain (99.97%). 2.1.2 Scale up VS Scale out NAS

Traditional scale up system would provide a small number of access points, or data servers that would sit in front of a set of disk protected with RAID . As these systems needed to provide more data to more users the storage administrator would add more disks to the back end but this only caused to create the data servers as a choke point. Larger and faster data servers could be created using faster processor and more memory but this architecture still had significant scalability issues.Scale out uses the

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approach of more of everything instead of adding drives behind aair of servers, it adds servers each with processor, memory,network interfaces and storageCapacity. As I need to add capacity to a grid –   to scale out version of an array –   I insert a new node with all the available resources. This architecture required a number of things to make it work from both a technology and financial aspect.

Some of these factors include. 1. Clusterd architecture

FOR this model to work the entire grid needed to work as a single entity and each node in the grid would need to be able to pick up a portion of the function of any other node that may fail. 2. Distributed / parallel file system

The file system must allow for a file to be accessed from any one or any number of nodes to be sent to the requesting system. This required different mechanism underlying the file system : distribution of data across multiple nodes for redundancy , as distributed metadata o locking mechanism and data scrubbinh / validation routines. 3. Commodity Hardware

For these systems to be affordable they must rely on commodity hardware that is inexpensive and easily accessible instead of purpose built systems.

Benefits of Scale Out

There are a number of significant benefits to these new scale out systems that meet the needs of big data challenges. 1. Manageability

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When data can grow in a single file system namespace the manageability of the system increases significantly and a single data administrator can now mange a  petabyte or more of storage versus 50 or 100 terabytes on a scale up system. 2. Elimination of stovepipes

Since these systems scale linearly and do not have the bottlenecks that scale up systems create, all data is kept in a single file system in a single grid eliminationg the stovepipes introduced by the multiple arrays and files systems required. 3. Just in time scalability

As my storage needs grow I can add an appropriate number of nodes to meet my needs at the time I need them. With scale up arrays I would have to guess at the final size my data may grow while using that array which often led to the purchase of large data servers with only a few disks behind them initially so I would not hit bottleneck in the data server as I added disk. 4. Increased utilization rates

Since the data servers in these scale out systems can address the entire pool of storage there is no stranded capacity. There are five core tenets of scale out NAS should be simple to scale offer predictable performance , be efficient to operate always available and be proven to work in a large enterprises

2.1.3 EMC ISILON

EMC Isilon is the scale  –   out platform that delivers ideal storage for big data. Powered by the oneFS operating system, Isilon nodes are clustered to created a high –   performing single pool of storage.EMC Corporation announced in May 2011, the world largest single file system with the introduction of emcisilon‟s new iq 108NL scale –   out NAS hardware product . Leveraging three terabyte (TB) enterprise –   class

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hitachiultrastar drives in a 4u node, the 108NL scales to more than 15 petabytes (PB) in a single file system and single volume, providing the storage foundation for maximizing the big data opportunity. EMC also announced isilon‟s new smartlock data retention software application

,delivering immutable protection for big data to ensure the integrity and continuity of  big data assets from initial creation to archival.

Object Based Storage

Object storage is based on a single . Flat addresss space that enables the automatic routing of data to the right storage systems and the right and protections levels within those systems according to it value and stage in the data life cycle.

Better Data Availability than RAID

In a properly configured object storage system content it replicated so that a minimum of to replicas assure continuous data availability. If a disk dies all the other disk in the cluster join in to replace the lost replicas while the system still runs at nearly full speed . Recovery takes only minutes with no interruption of data availability and no noticeable performancedegradation.

Provides unlimited capacity and scalability

In object storage systems there is no directory hierarchy and the object location does not have to be specified in the same way a directory path has to be known in order to retrieve it. This Nables object storage system to scaleout limits on the number of files (objects ), to petabytes and beyond without limits on the number of files ,file size or

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file system capacity, such as the 2-terabyte restriction that is common for window and linux file systems.

Backups are Eliminated

With a well designed object storage system ,backups are not required . ultiples replicas ensure that content is always available and an offsite disaster recovery replica can be automatically created if desired.

Automatic Load Balancing

A well designed object storage cluster is totally symmetrical which means that each node is independent provides an entry point into the cluster and runs the same code.

Companies that provide this are cleversafe,compiverde,amplidata ,caring,emc ,hitachi data systems (hitachi content platform),netapp (storage grid ) and scality.

2.2 Apache Hadoop  has been the drivig force behind the growth of the big data

industry . it is a frame work for running applications on large cluster buiyt of commodity hardware. The hadoop framework transparaently provides applications  both reliability and data motion.

MapReduce   is the core of hadoop created at google in response to the problem of

creating web search indexes , the map reduces frame work is the power house behind most of today big data processing . In addition to hadoop you will find map reduce inside MPP and no SQL databases such as vertica or mongoDB .The important

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innovation of map reduce is the ability to take a query over a dataset, divide it and run it in parallel over multiple nodes. Distributing the computation solves the issures of data too large to fit into a single machine.

Combine this technique with commodity linux servers and you have a cost effective alternative to massive computing arrays.

HDFS - we discussed the ability of map reduce to distribute computation over multiple servers. For that computation to take palce, each server must have acess to the data. This is the role of HDFS , the hadoop distributed file system. HDFS and mapreduce are robust .severs in a hadoop cluster can fail and not abort the computation process. HDFS ensures data is replicated with redundancy across the cluster. On completion of a calculation a node will write its results back intoHDFS . There are also restrictions on the data thatHDFS stores . Data may be structured and schemes be defined before storing the data .with HDFS making sense of the data is the responsibility of the developers code.

Why a company will be interested in hadoop?

The number one reason is that the company is interested in taking advantage of unstructured or semi structured data. This data will not fit well into a relational databases, but hadoop offers a scalable and relatively easy to program way to work with it. This category includes emails web serve logs instrumentation of online stores , images video and external data sets. All this data can contain information that is critical to this businesses orgranized by geographical area. All this data can contain information that is critical to this business and should reside in your data

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warehouse,but it needs a lot of pre-processing and this pre-processing will not happen in oracle RDBMS .

For Example

The other reason to look into hadoop is for information that exists in the databse, but can‟t be efficiently processed within the database.this is a wide usecase and it is

usually labeled ETL because the data is going out of an OLTP system and inot a data warehouse. You use hadoop when 99% of the work is in the t of etl processing the data into useful information.

2.3 DATA APPLIANCES

Purpose built solutions like teredata, IBM/NETEZZA, EMC/Greenplum, SAP HANA( High-Performance Analytic appliance), HP Vertica and oracle exadata are forming a new category . Data appliances are one of the fastest growing categories in  bid data. Data appliances integrate databases, processing and storage in a integrated system optimized for analytics. 1. Proeccessing close to the data source 2. Appliance simplicity 3. Massively parallel architecture 4. Platform for advanced analytics 5. Flexible configurations and extreme scalability.

2.3.1 HP Vertica

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The vertica Analytics platform is purpose built from the ground up to enable companies to extract value from their data at the speed and scale they need to thrive In today economy. Vertica was designed and built 000since it inceptions for today most demanding analytic workloads each vertica component is able to take full-advantage of the others by design. Key features of the Vertica Analytics Platform

1. Real Time Query &Loading>> Capture the time value of data by continuously loading information while simultaneously allowing immediate access for rich analytics. 2. Advanced In Database Analytics >> Ever growing library of features and functions to explore and process more data closer to the CPU cores without the need to extract. 3. Database Designer & Administration Tools >>Powerfullsetup , tuning and control with minimal administration effort. Can make continual improvements while the system remains online. 4. Columnar Storage & Execution >> Perform queries 50 x –  1000x faster by eliminating costly disk i/owithout the hassie and overhead of indexes and materialized views. 5. Aggressive Data Compression >> Accomplished more with less CAPX while delivering superior performance with our engine tha operated on compressed data. 6. Scale-Out MPP Architecture >>Vertica automatically scales linearly and limitlessly by just adding industry –  standard x86 servers to the grid. 7. Automatic high Availability >> Runs non stop with automatically redundancy failover and recovery optimized to deliver superior query performance as well.

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8. Optimizer,Execution Engine & workload Management >> Get Maximum Performance without worrying about the details of how it gets done. Users just think about questions we deliver answers ,fast. 9.  Native BI ETL & Hadoop /mapreduce integration >> Seamless integration with a robust and ever growing ecosystem of analytics solutions . 2.3.2 Terradata Aster

To Gain Business insight using mapreduce and apache hadoop with SQL Based analytics below is a summary using a unified big data architecture that blends the  best of hadoop and SQL allowing user to; 1. Capture and refine data from a wide variety of sources 2. Perform necessary multi-stuctured data preprocessing 3. Develop rapid analytics 4. Process embedded analytics, analyzing both relational and non relational data. 5. Produce sem-stuctured data as output often with metadata and heuristic analysis 6. Solve new analytical workloads with reduced time to insight. 7. Usemassively parallel storage in hadoop to efficientlystora and retain data.

Below figure offer frame work to help enterprise architects most effectively use each  part of a unified big data architecture. This framework allows a best of breed approach that you can apply to each schema type, helping you achieve maximum  performance , rapid enterprise adoption and the lowest TCO.

3.0 Domain Wise Challenges in Big Data Era 3.1 Log Management

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Log data does not fall into the convenient schemas required by relational databases. Log data is at its core, unstructured or in fact semi-structured which leads to a deafening cacophony of formats, the sheer variety in which logs are being generated is presenting a major problem in how they are analyzed . The emergence of big data has not only been driven by the increasing amount of unstructured data to be  processed in near real  –   time , but also by the availability of new toolst to deal with these challenges. There are 2 things that don,t receive enough attention in the log management space. The 1st is real scalability which means thinking beyond what data centers can do. That inevitably leads to ambient cloud models for log management .Splunk has doe an amaing job of pioneering an ambient cloud mdel with th way they created and eventual consistency model which allow you to make a query to get a good enough answer quickly or a perfect answer in more time.

The 2nd  thing is security. Log data is next to useless if it is not nonrepudiatable . Basically all the log data in the world is not useful as evidence unless you can prove that nobody changed it.Sumo DataLogglySpluunkare the primary companies that currently have products around log management.

3.2 Data Integrity and reliability in the big data era

Consider standard business practices and how nearly all physical forms of documentation and transactions have evolved to become digitized versions and with them come the inherenllenges of validating not just the authenticity of their contents  but also the impact of acting upon an inavalid data set something which is highly  possible in today high velocity big data business environment . with view we can then  begin to identify the scale of the challenge. With cybercrime and insider threats

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clearly emerging as a much mre profitable business the the criminal element , the need to validate and verify is going to become critical to all business documentation and related transactions even within the existing supply chains.

Keyless signature technology is a relatively new concept in the marke and will require a different set of perspectives when put under consideration . A keyless signature  provides an alternative method to key based technologies by providing proof and non repudiation of electronic data using only hash functions for verification. The implementation of keyless signature is done via a globally distributed machine, taking hash values of data as inputs and returning keyless signatures that prove the time , integrity and origin of the input data.

A primary goal of the keyless signature technology is to provide mass-scale ,nonexpiring data validation while elimanting the need for secrets or other forms of trust thereby reducing or even eliminating the need for more complex certificate based solutions as these are ripe with certificate management issues , including expiration and revocation.

As more orgainisations become affected by big data phenomenon , the clear implication is that many business will potentially be making business based on massice amounts of internal a third party data .

Consequently the demand for novel and trusted approaches to validating data will grow. Extend this concept to the ability to validate a virtual machine, switch logs or indeed the security logs and then multiply by the clear advantages that cloud

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computing (public or private) has over the traditional datacenter design  –   we will  begin to understand why keyless data integrity technology that can ensure self –  validating data is a technology that is likely to experience adoption.

The ability to move away from reliance on a third party certification authority will be welcomed by many although this move from the traditionally accepted approach to verify data integrity needs to be more fully broadcasted and understood for more mass market adoption and acceptance.

Another solution for monitoring the stability , performance and security of your big data environment is from a company called Gazzang. Enterprises and SaaS solution  providers have new needs that re driven by the new infrastructures and opportunities of cloud computing . For Example , business intelligence analysis use big data stores such as mongo db , hadoop and Cassandra . The data is spread across hundreds of server in order to optimize processing time and return business insight to the user. Leraging its extensive experience with cloud architectures and big data platform ,gazzang is delivering a Sass solution for the capture , management and analysis of massive volume of IT DATA. Gazzangzops is purpose built for monitoring big data  platforms and multiple cloud environments . The powerful engine collects and correlates vast amounts of data from numerous sources in a variety of forms.

3.3 Backup Management in Big Data Era

For protection against user or application error ,asharbaig a senior analyst and consultant with the taneja group , said snapshots can help with big data backups. Big also recommends a local disk based system for quick and simple first  –   level data

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recovery problem. “ look for a solution that provide you an option for local copies of

data so that you can do local restores which are much faster he said , having a local copy and having an image based technology to do fast image based snaps and replications does speed it up and takes care of the performance concern. Faster Scanning Needed

One of the issures big data backup systems face is scanning each time the backup and archiving solutions start their jobs. Legacy data protection systems scan the file system each time a backup job is run and each time an archiving job is run. For file systems is big data Environment this can be time consuming. Commvault solution for the scanning issue in its impana data protection software is it one pass feature. According to commvault , one pass is an object level converged process for collecting  backup archiving and reporting data. The data is collected and moved off the primary system to a content store virtual repository for completing the data protection operations. Once a complete scan has been accomplished , the commvault software  places an agent on the file system to report on incremental backups making the  process even more efficient.

Casino doesn’t want to gamble on backups

Pechanga resort and casino in Temecula calif went live with a cluster of 50 EMC isilon X200 nodes in February to back up data from its surveillance cameras. The casino has 1.4 PB of usable isilon storage to keep the data, which is critical to operations because the casino must shutdown all gaming operations if its surveillance system is interrupted.

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“In gaming we‟re mandated to have surveillance coverage ,” said Michael grimsley

director of systems for Pechanga technology solutions group. If surveillance is down all gaming has to stop. If a security incident occurs their team pulls footage from the x200 nodes and moves it to worm compliant storage and back it up with networker software to emc data domain dd860 the duplication target appliances. The casino doesn‟t need tape for worm capability because worm is part of isilons smart lock software. Another possibility is adding replication to a DR site so the casino can recover quickly if the surveillance system goes down.

Scale out Systems

Another option to solving the performance and capacity issues is using a scale out  backup system one similar to scale out NAS,but built for data protection .you add nodes with additional performance and capacity resources as. the amount of protected data growschnogy. “Any backup architecture especially for the big data world has to officer balance the performance and the capacity properly said jefftofanosepatoninc chief technology officer .otherwise at the end of the day , it not a good solution for the customer and is a more expensive solution than it should be .

Sepaton s2100-es2 modular virtual tape library (VTL) was built for data intensive large enterprises. According to the company its is 64 bits processor nodes backup data at up to 43.2tb per hour , regardless of the data type and can store up to 1.6pb yoy can add up to eight performance nodes per cluster as your needs require and add disk shelves to add capacity.

3.4 Database management in Big Era

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There are currently three tree trends in the industry: 1. The NoSQL databases designed to meet the scalability requirements of distribution architectures and or schemaless data management requirements. 2. The NewSQL databases designed to meet the reqirements of distributed architectures or to improves performance such that horizontal scalability is no longer needed. 3. The data grid /cache products designed to store data in memory to increase application and database performance . Computer World Tam Harbert explored the skills and needs organizations are searching for in the quest to manage the big data challenge and also identified five  job titles emerging in the big data world .

Along with habert findings here are 7 new types job being created by big data : 1. Data Scientists : This emerging role is taking the lead in processing raw data and determining what types of analysis would deliver the best results. 2. Data Architects : Organistaitons managing bid data need professional who will be able to build a data model and plan out roadmap of how and when various data sources and analytical will come online had how they will all fit together. 3. Data Visualizer : These days a lot of decision –   maker rely on information that is  presented to them in highly visual format –  either on dashboards with colorful alerts and dials or in quick understand charts and graphs organizations need  professionals who can harness the data and put it in context , in layman language exploring what the data means and how t will impact the company. 4. Data Change agents : - Every forward thinking organistation needs change agents usually an informal role who can evangelize and marshal the necessary resources

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for new innovation and ways of doing business. Harbert predicts that data change in internal operations and processes based on data analytics. They need to be good communicators and six sigma background –  meaning they know how to apply statistics to improve quality on a continuous basis also help. 5. Data engineer/operators : these are the people that make the big data infrastructure hum on day to day basis. They develop the architecture that hels analyze and supply data in the way the business needs and make sure systems are performing smoothly says harbert. 6. Data stewards : not mentioned in harbert list but essential to any analytics-driven organization is the emerging the role of data steward.Every bit and byte of data across the enterprise should be owned by someone –   ideally a linne of business . Data Stewards ensure that data sourcesare properly accounted for and may also maintained a centralized repository as part of master data management approach in which there is one gold copy of enterprise data to be referenced . 7. Data Virtualization/cloud specialists :- Databases themselves are no longer as unique as they use to be . what matter now is the ability to build and maintain a virtualized data service layer in a consistent easy to access manner. Sometimes this is called databases a service . No matter what it called organization need  professional that can also build support these virtualized layer or clouds.

4.0 BigData use cases : 4.1 Potential use cases

The key to exploiting big data analytics is focusing on a compelling business opportunity as defined by use case what (what exactly are we trying to do ? ) what value is ther proving a hypothesis ?

27

Use cases are emerging in a variety of industries that illustrate different core competencies around analytics. Figure below illustrates some use cases along two dimensions data velocity and variety.

RAW DATA -> AGGREGATED DATA DECISIONS

 

 INTELLIGENCE

OPERATIONAL IMPACT

 

-- > INSIGHTS



FINANCIAL OUTCOMES -- >

VALUE CREATION.

Insurance:-   -- Individualize auto  –   insurance policies based on newly captured

vehicle telemetry data . Insurer gains insight inot customer driving habits delivering 1) more accurate assessments of risk 2) individualized pricing based on actual individual customer driving habits 3) influence and motivate individual customer to improve their drivinghabits .

Travel :-- optimize buying experience through web log and social media data nalysis

1) travel site gain insight in not customer preferences and desires 2) up  – selling  products by correlating current sales with subsequent browsing behavior increase  browse to buy conversions via customized offers and packages 3) deliver personalized travel recommendations based on social media data .

Gaming  –   Collect gaming data to optimize spend within and across games 1) games

company gains insight into likes , dislikes and relationships of it user 2)Enchance games to drive customer spend within games 3) recommend othecontenet based on

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analysis of player connections and similar like . Create special offers or packages  based on browsing and buying behaiour. 4.2 Big data Actual Use Cases

Below graphic mentions the survey result undertaken by information week which indicated the % of respondents who would be opting for a open source solutions for Bd Data.

1. Use Case

Amazon will pay shoppers $5 to walk out of stores emptyhandedInteresting use of consumer data entry to power next generation retail price competition amazon is offering consumers up to $ 5 off on purchase if they compare prices using their mobile phone application in a store. The promotion will serve as a way for amazon to increase usage of it bar code scanning application while also collectiong intelligence on prices in the stores.

Amazon‟s price check app which is available for iphone and android allows

shoppers to scan a bar code take a picture of an item or conduct a text search to find the lowest prices . Amazon is also asking consumer its still ofto submit the  prices of items with the app „ so amazon know offering the best prices . A great way to feed data inot it learningengine from brick and mortor retailers.This is an interesting trend that should terrify bricj and mortar retailer .while the realtimeevery day low price information empower consumers it terrifies retailer who increasingly are feeling like showroom shoppers come to be check out the merchandise but uttimately decide to walk out and buy online instead.

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2. Smart meters

1) Because of smart meters , electricity providers can read the meter once every 15 minutes reather than once a month . This not only eliminated the need to send some one for meter reading, but as the e is read once every fifteen minutes , electricity can be priced differently for peak and off peak hours .  pricing can be used to shape the demand curve during peak hours eliminationg the need for creating additional generating capacity just to meet peak demand , saving electicity providers millions of dollars worth of investment in generating capacity and plant maintenance costs. 2) Well there is a smart electric meter in a residence in texas and one of the electricity providers in the area is using the smart meter technology to shape the demand curve by offering free night time energy charges –   all night every night . All year long. 3) In Fact they promote their service as do your laundry or run the dishwasher at night and pay nothing for your energy charges. What txu energy is trying to do here is to reshape energy demand using pricing so as to manage peak time demand resulting in savings for both txu and customer . This wount have been  possible without smart electric meters. 4) T-mobile USA ---- has integrated big data across multiple it systems to combine customer transaction and interactions data in order to better predict customer defections. By leveraging social media data along with transactions data from CRM and billing systems , t mobile USA has been able to cut customer defections in half in a single quarter.

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5) Us express provider of a wide r=variety of transportation solutions collects about a thousand data elements ranging from fuel usage to tire conditions to truck engine operations to gps information and uses this data for optimal fleet management and to drive productivity saving millions o dollars in operating costs. 6) Mclaren formula one racing team ------- uses real –  time car sensor data during car races, identifies issues with its racing cars using predicatives analytics and take corrective actions proactively before it too late | 7) How morgan Stanley uses hadoop ---- Gary bhattarcharjee executive director of enterprises information management at the firm has worked with hadoop as early as 2008 and thought that it might provide a solutions .so the it department hooked up some old servers.

At the fountained head conference on hadoop In finance in new York  bhattacharjee said the investment bank has started by stringether 15 end of life  boxes . it allowed us to bring really cheap infrastructure into a framework and install hadoop and let it run.One area that bhattacharjee would talk about was in it and log analyisis.A typical approach would be a look at web logs and database logs to see problems but one log wouldn‟t shpow if a web delay was

caused by a databse logs pu them inot hadoop and ran tim based correlations .now they can see market events and how they correlate with web issues and databases read write problems.

8) Big data at ford

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With analytics now embedded ito the culture of ford , the rise of big data analytics has created a whole host of new possibilities for the automaker

generate

internally from our business operations and also from our vehicle. We recognize that the volumes of data we generate internally –  from our business operations and also from our vehicle research activities as well as the universeof data that our customers live inn and our vehicle research activities as well as the universe of daa that our customers live in and that exists on the internet –   all of those things are hug opportunities for us that will likely require some new specialized techniques or platforms to manage, said ginder . our research organization is experimenting with haddo and we are trying to combine all of these various data sorce that we have accesto . we thing to sky is the limit . we recognize that we are  just kind of scraping the tip of the iceberg here.The other major asset that fordhs going for it when it comes to bigdata is that hthe company is tracking enormpus amounts of useful data in both the product development process and the products themselves.

Ginder noted our manufacturing sites are all very well instrumental. Our vehicles are very well instrtmental. They closed loop control systems . There are many many sensors in each vehivle until now most of that information was in the vehicle , but we think ththat data andere opportunity to grab that data and understand better how the car operated and how consumers use the vehicles and feed that informations back inot our design process and help optimize the user experience in the future as well.

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Of course big data is about a lot more than just hearnessing all of the runaway data sources that mos companies are trying to grapple with it about structured data plus unstructured data . structure data is all the traditional stuff most companies have in their databases as the stuf like ford is talking about with sensors in its vehicles and assembly ) unstructured data is the stuff that now freely available across he internet , from public data now being exposed by government on sites such as data.gov in the us to treasure troves of consumer intelligence such as twiteer .mxing the two and coming up with new analysis is that big data is all about. The amount of that data is only goingtassumption of big data is only own goin grow and there aopportunity for us to combine that external data with our internal data in new ways said ginder . for better forecasting or for better insight into  product design there are many many opportunities.

Ford is alos digging into the consumer intelligence aspect of unstructured data .Ginder said we recognize that the data on the internet is potentially insightful for understaning what our customer or our potential customer are looking for what their attitudes are so we do some sentiment analysis around blog post, comments and other types of content on the internet.

That kind of thing is pretty common and a lot of fortune 500 companies are doing similar kinds of things .however there another way that ford is uusing unstructured data from the web that is a little more unique and it tohas impacted the way the company predicits future sales of tit vehicles.

33

We use google trends which measures the popularity of search terms to help form ourwon internal sales forecasts ,ginder explained . Along with other internal data we have , we use that to build a better forecast , ginder explained . Along with other internal data we have we use that to buid a better forecast .it one of the inputs for oursals forecast . In the past it would justbe what we sold last week . ow it what we sold last weel plus the popularity of the search terms again, I think we are just scratching the surface . There a lot more I think we will be doing in the future.

Computer and electronics products and information sectors traded globally stand out as sctors that have already been experiencing very strong productively growth and that are poised to gain substaintially from the use of big data.

Two services sectors and insurance and government are positioned to benefit very strongly from big data as long as barriers ot it use can overcome.

Several sectos have experienced negative productivity growth probably indicationg that these sectos face strong systematic barriers to increasing  productivity. Among the remaing sectors we see that globally traded sectos ten to have experienced higher productivity growth while local services (mainly cluster E) have experienced lower growth.

While all sectors will have to overcome barriers to capture value from the use of  big data,barriers are structureally higher for some than for others . For Example , the public sector , including educations , faces higher hurdles because of a lack of

34

datadriven mindset and available data .capturing value in health care face challenges given the relatively low IT investment performed so far. Sectors such as retail , manufacturing and professiona services may have relatively lowe degrees of barriers to overcome for precisely the opposite reasons.

4.3 In IBM Big Data used for 1971 :- SPEECH RECOGNITION speech recognition  (SR) is the translation of spoken words into text. It is also known

as "automatic speech recognition", "ASR. Some SR systems use "speaker independent speech recognition" while others use "training" where an individual speaker reads sections of text into the SR system. These systems analyze the person's specific voice and use it to fine tune the recognition of that person's speech, resulting in more accurate transcription. Systems that do not use training are called "speaker independent" systems. Systems that use training are called "speaker dependent" systems.

Speech recognition applications include voice user interfaces such as voice dialling (e.g. "Call home"), call routing (e.g. "I would like to make a collect call"), domotic appliance control, search (e.g. find a podcast where particular words were spoken), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g. a radiology report), speech-to-text processing (e.g., word  processors or emails), and aircraft (usually termed Direct Voice Input). 1980 :- RISC Architecture  (Reduced Instruction Set Computer). In old ibmserver ,

 performance level speed has been improved. 35

1988 :-NSFNET :- Having connecting to network between many university in

US.NSINET speed 92 countries for isp in 1995. 1993: Scalable Parallel System. A multiprocessor is a tightly coupled computer

system having two or more processing units (Multiple Processors) each sharing main memory and peripherals, in order to simultaneously process programs. Sometimes the term Multiprocessor  is confused with the term Multiprocessing . 1996 : DEEP THUNDER

It show a daily wheather report. It showcalclulation and manipulation of project. 1997 :- DEEP BLUE

IBM 6000 super computer having parallel process. Breaking up the task into smaller subtask and execute them in parallel.2000 :- Linux operating system.BLUE GENE –  2004 . Fastest wide range of application , medical and climate 2009 :- The First Nationalwide  smart energy and water grid having water shortage

,skyrocketing energy cost,monitor waste, incentive efficient resource usedetecttheft,reduce dependency and also other utilities. 2009 :- STREAM COMPUTING :- (Video Streaming) for w.g we can say is there

events in orgainisation all the audiofile and video file record are stored in server. 2009 :- CLOUDThe future of the cloud is going to be a hybrid combination of public

and private cloud, not one or the other. There will be times when you want to run a workload in a private cloud, then move it up to a public cloud, and later move it back again to your private cloud. We see a Microsoft private cloud as the first step towards  building a cloud that allows you to go into the public cloud, which is what we call

36

Windows Azure. With Microsoft, our cloud offerings are designed so that your  private cloud and public cloud work together. 2010 GPFS SNC –  General Parellel file system shared disk clusterd file system

stored in SAN, GPFS provide high availability , disaster recovery, security , herarichal system.

The internet has made new sources of vast amount of data to business executives . Big data is comprised of data sets too large be handled by traditional systems. To remain competitive, business executives need to adopt new technologies and techniques emerging e to big data. Big data includes structured data , semi structured and unstructured data. structured data are those data formatted for use in a database management system. Semi structured and unstructured data include all type of unformatted data including multimedia and social media content. Big data are also  provided by myriad hardware objects, including sensors and actuators embedded in  physical objects,

37

Page 1 of 1

Form Title

Name of the person filling the form:

ORGANISATION

Department Name:

38

Are you aware of Big Data ? Yes No Other:

What type(s) of data do you use in your organisation ? Microsoft Office Open Office Tally Libra Office Lotus Office None Other:

What kind of Module(s) do you use in your organisation ? * ERP SAP SUN TALLY

39

WEB PORTAL NONE Other:

What type(s) of Data Format do you use? Document Format (.doc, docx) Excel Format(.xls) PDF Format() JPEG Format Video Format() None Other:

If your data is lost, is there any process for data restoration? Yes No Other:

Do you have any procedure for Data Backup? Daily backup Weekly backup

40

Monthly backup None Other:

What media do you use to take backup of data stored on your system? CD/DVD USB EXTERNAL Storage Drive USB Pendrive Local drive(s) on your system None Other:

Which Email Service do you use? Zimbra Microsoft Exchange Lotus None Other:

Do you have an Archieved Backup of your data and email service? Yes No

41

Other:

Which service do you use for offline Backup of email service? Microsoft Outlook Outlook Express Mozilla ThunderBird Zimbra Client Lotus notes None Other:

Please provide your View(s) on Big Data ? Explain in 1 line

Do you have any Suggestions on the concept of Big Data ?

Add item Confirmation Page

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Send form

Data Collection we have only 73 person has response Web Based Questionnaires : A new and inevitably growing methodology is the use of Internet based research. This would mean receiving an e-mail on which you would be click of an address that would take you to secure web-site to fill in a questionnaire. This type of research is often quicker and less detailed. Some disadvantages of this method include the exclusion of people who do have a computer or are unable to access a computer. Also the validity of such surveys are in question as people might  be in a hurry to complete it and so might not give accurate responses. (https://https://docs.google.com/a/welingkarmail.org/forms/d/1OYNpCCQyEzCqn42i h_LX7NksW5GR_frU5wnhA_OPVAY/viewform)

Questionnaires often make use of checklist , checkbox and rating scale. These devices help simplify and quantify people‟s behaviors and attitudes. A Checklist is a list of 43

 behaviors , characteristics , or other entities that the researcher is looking for Either the researcher or survey participant simply checks whether each item on the list is observed, present or true or vice versa. A Rating Scale is more useful when behavior needs to be evaluated on a continuum. They are also known as LikertScaels.

In this data analysis we have 70 % of people know about the is big data ; In Analysis of Big Data this diagram it show the rate of scale and rating of analysis of  big data . 73 responses Summary Name of the person filling the form:

Chitin Salian VikramMadhavShinde Priti Nikita ShoaibMomim Gouri rohitkhana Neha gane shdevarushi sheetal siddhant |Bitla NazirKanoor TruptiMengle BhojaAminBhaveshdodia ArchanaRathod Deepakl  praveen surakhakamble Vinitha  Nair suhilaamin mehmoodKanoor Lynette PriyankaSalunke Subodh durgesh Deepak SupriyaMoreAnkurThakkar ParveenShaikh Vajreshwari Amin Siddhi Deshpande prashantdesai Tanuja Latesh 44

Poojary Vidya satish PriyankaAjgaonkar JitinSalian PramodMulik HeenaShailkh San deepKelkar piyush ManeeshaMhatre Rao DilipVishwasrao ajay Rupal Choudhari Vanmala Bhagwati Mrunal Shivan Naina naina Girish santoshkadam ajayd esai Mehek somappasalian parinita Maitreyee Anagheswar RutujaDeshmukh Jeetendr  aVelhal dipeshnagalkar Neeta Papal Ryan Rodricks AkshayadeviSawant SantoshRajeesh Nair Surjit Singh deepak husainkanor

ORGANISATION

lokandwala SBI Salian Daulat Exim Pvt Ltd Accentures Lupin Ltd. web print SBI Life welingkarinstitiutes Ugam Solutions MT Educare Pvt. Ltd We School JLL Redington India Ltd poojary Kotian jsw steel Welingkar DATA Welingkar Institute WeSchool cargo AB Enterprises Desperado Inc accenture Cybertech Ferrero autonomous Welingkar Institute Of Management Accenture Services Pvt. Ltd. coca cola Godrej Infotech 3dplm wipro bajaj auto ltd TCS orange business service SIBER Maxwell Industries Ltd deloittepunjab national bank Tetra Pack College of ABM,Oros Rai University none Dhruvinfotech Sai Service Agency Pvt Ltd sorte Alitalia Airlines Atos origin central bank of india welingkar institute of management hdfc Welingkar Institute of Management Jacquor net magic ICFAI University Nokia eClerx Service Ltd xolo Amin Annet Tech. Sixsigma Pvt. Soft SolutionsYogaVidyaNiketan Ideate digital Welingkar capgemini HCL Infosystems Ltd. poona finance bank

Department Name

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Quality Operation BMLP IT Infrastructure Support production Sales & Marketing Technology Distribution Marketing supply chain mangaement support Customer Care GOC back office Event Management none Yoga Kendra quality Management CVN Testing Marketing Reparing printing dept Reservations and Holiday Packages mis acounting researchstoredept RAID HLDC admin finance Digital Marketing Designing manufacturing Coordination Admin Admin MBA Media Distan ce education Department FinanceAdministrator IT Design import dept UBI Data Analyst IT ADC Technology governance Accounts MIS Administration dispatch Acc ounts accounts logistic hardware HR & Admin it PGDM –  FMB

Are you aware of Big Data ?

Yes

42 59%

 No

27 38%

Other

2 3%

What type(s) of data do you use in your organisation ?

Microsoft Office

66 51%

Open Office

18 14%

46

Tally

17 13%

Libra Office

4 3%

Lotus Office

9 7%

 None

4 3%

Other

12 9%

What kind of Module(s) do you use in your organisation ?

ERP

28 21%

SAP

12 9%

SUN

4 3%

TALLY

20 15%

WEB PORTAL

49 37%

 NONE

7 5%

Other

13 10%

What type(s) of Data Format do you use?

Document Format (.doc, docx)

64 23%

Excel Format(.xls)

67 24%

PDF Format()

60 22%

JPEG Format

47 17%

Video Format()

28 10%

 None

3 1%

47

Other

6 2%

If your data is lost, is there any process for data restoration?

Yes

51 72%

 No

19 27%

Other

1 1%

Do you have any procedure for Data Backup?

Daily backup

32 40%

Weekly backup

22 28%

Monthly backup

11 14%

 None

12 15%

Other

3 4%

What media do you use to take backup of data stored on your system?

CD/DVD

25 18%

USB EXTERNAL Storage Drive

39 27%

USB Pendrive

29 20%

Local drive(s) on your system

31 22%

48

 None

9 6%

Other

9 6%

Which Email Service do you use?

Zimbra

24 34%

Microsoft Exchange

25 35%

Lotus

7 10%

 None

6 8%

Other

9 13%

Do you have an Archieved Backup of your data and email service?

Yes

40 58%

 No

28 41%

49

Other

1 1%

Which service do you use for offline Backup of email service?

Microsoft Outlook

21 29%

Outlook Express

8 11%

Mozilla ThunderBird

5 7%

Zimbra Client Lotus notes

10 14% 9 13%

 None

18 25%

Other

1 1%

Please provide your View(s) on Big Data ? Explain in 1 line

The biggest challenge for any huge organisation is to figure out who should own the  big data initiatives that straddle the entire organisation. Big Data is a must in every organisation as there is always a chance of losing a big chunk of the important data. no idea abt big data Great cloud backup Big data is collocation of all the relevant data. big data should be have backup Student DATA Big data is useful for viewing structured data. I don't deal with big data on day to day basis. Hence not able to  justify. other no idea about Big data Harddisk Most of the organizations maintain a mix of data sets in various forms. Irrespective of your profession apps are require to make your data more accessible, usable and valuable. More reliable Should have more capacity for Data use . Big data refers to groups of data that are so large and unwieldy that regular database management tools have difficulty capturing, storing, sharing and

50

managing the information. I don't know any thing about Big data big data Big Data Required More Efficient Big data Should be stored in centralised format The file which is more than 10 MB. Big Data is not limited to just email but its more about the  business data running on servers (Oracle database, data backup of servers, etc) none no idea we are use many types of data formats,for security reason we take daily backup. Use cloud Service for video and jpeg format require advance technology cloud computing not yet used, but aware of what big data is. Big data is future. no idea abt big data It's a huge hype with only top guns moving into the technology. The investment is high and seems risk prone. all the database should be stored in centralised data should be in cloud Big data require more security Provide custopmization

in

big

dataBig

data

should

be

disaster

reovery Accounts

Statement data should be in Icloud. It should be easily accessable for the person who is storing it .. should provide in cloud Do you have any Suggestions on the concept of Big Data?

no big

data cloud

backup NA big

data

should

be

have

backup Information

management is the most crucial in the organizations, hence more research on data management and analytics is required. other i suggestion data should be in cloud  backup,it is very much secure & better, Thanks Purchase New USB Hard disk Drive And Copy All Data For economic feasibility in any successful organisation ,it is essential to device ways and means to handle "big data" without driving up the hardware costs. More reliable No 1) Backup concept needs to be highlighted in this survey. 2) Software used should be asked 3) Kind of data to be backed up should be asked 4) Retrieval procedure should be asked 5) Incase of backup is corrupted there should be a concept of fail safe module which should be discussed Store more Hard disk Big data Should be stored in centralised format The file which is more than 10

51

MB. none Same as above no idea If daily back up is taken then it would be of great help and if possible back up is taken dept. wise then the time consumed for retriving the data would be reduced. require advance technology no idea abt big data data should be in cloud cloud computing Use cloud Serice for video and jpeg format Big data require more security Provide custopmization in big data Big data should be disaster reovery nope, not yet data should be in Icloud. It should be easily accessable for the person who is storing it .. should provide in cloud Number of daily responses

1.  Nearly half the data (49%) is unstructured (text), while 51% is structured. Also, about 70% of the data is from internal sources. 2. Logistics and finance expect the greatest ROI, although sales and marketing have a bigger share (30%) of the Big Data budget 3. Monitoring how customers use their products to detect product and design flaws is seen as a critical application for Big Data 4. About half of the firms surveyed are using Big Data, and many of them projected  big returns for 2014 5. Big split in spending on Big Data, with a minority of companies spending massive amounts and a larger number spending very little 6. Investments are geared toward generating and maintaining revenue. 52

7. The biggest challenges to getting business value from Big Data are as much cultural as they are technological. 8. The biggest projected 2012 Big Data returns for leaders came from places that laggards did not value as much: improving customers offline experience and location-based marketing. 9. Companies that do more business on the Internet spend more on Big Data and  project greater ROI. 10. Organizing a core unit of Big Data analysts in a separate function appears to be important to success. 11. Big Data has become big news almost overnight, and there are no signs that interest is waning. In fact, several indicators suggest executive attention will climb even higher. 12. Over the last three years, few business topics have been mentioned in the media and researched as extensively as Big Data. Hundreds of articles have appeared in the general business press (for example, Forbes, Fortune, Bloomberg  BusinessWeek, The Wall Street Journal, The Economist ), technology publications

and industry journals, and more seem to be written by the day. A March 2013 search on Amazon.com surfaces more than 250 books, articles and e-books on the topic, most of them published in the last three years. 13. Dozens of studies have been conducted on Big Data as well, and every week another one appears. Most of the big consulting firms and IT services companies have weighed in, as well as (of course) the technology research community: Gartner, Forrester, IDC and many of the rest.

53

It seems that the general Blogging Idol conclusion is that big data is here to stay, that there are good arguments for moving forward with big data systems, and that the best way is to start small and prove the benefits. While this isn‟t much different from any

other new technology, it might be an especially good strategy to apply to big data applications. Cloud computing may also prove valuable for big data.But it is not necessary that vvery data should be in cloud

for example we can say ,

xyz

orgainisation having the big data , ver video ,audio ,jpeg and social media file were we can store in cloud computing . and Log data , confidential document ,events ,email  backup ,online transaction file and log data that we can store in internal organization .

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At the end of the day, big data provides an opportunity for “big analysis” lea ding to “big opportunities” to gain a competitive edge, to advance the quality of life, or to

solve the mysteries of the world.

1. Expanding customer intelligence 2. Improving operational efficiencies 3. Adding mobility to big data 4. “Big Data” and “Analytics” –  As a service 5. Define big data problems 6. Technology infrastructure recommendation, setup, ongoing operations & support. 7. Ingestion of big data 8. Analytics –   algorithms, map/reduce, statistical functions

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9. Integration with enterprise systems 10. Recommendation Engine for e-Commerce portals 11. Big data should be in cloud computing (for e.g video and jpeg file) 12. Replication and Disaster Recovery

It must not compromise the basic functionality of the cluster It should scale in the same manner as the cluster. It should not compromise the essential characteristics of big data It should address –  or at least mitigate –  a security threat to big data environments or data stored within the cluster. So how can we secure big data repositories today? The following is a list of common challenges, with security measures to address them: 1. User access: We use identity and access management systems to control users, including both regular and administrator access. 2. Separation of duties:  We use a combination of authentication, authorization, and encryption to provide separation of duties between administrative personnel. We use application space, namespace, or schemata to logically segregate user access to a subset of the data under management. 3. Indirect access: To close “back doors” –  access to data outside permitted interfaces –  we use a combination of encryption, access control, and configuration management. 4. User activity: We use logging and user activity monitoring (where available) to alert on suspicious activity and enable forensic analysis. 5. Data protection:   Removal of sensitive information prior to insertion and data masking (via tools) are common strategies for reducing risk. But the majority of  big data clusters we are aware of already store redundant copies of sensitive data. This means the data stored on disk must be protected against unauthorized access, 56

and data encryption is the de facto method of protecting sensitive data at rest. In keeping with the requirements above, any encryption solution must scale with the cluster, must not interfere with MapReduce capabilities, and must not store keys on hard drives along with the encrypted data –   keys must be handled by a secure key manager. 6. Eavesdropping: We use SSL and TLS encryption to protect network communications. Hadoop offers SSL, but its implementation is limited to client connections. Cloudera offers good integration of TLS; otherwise look for third  party products to close this gap. 7. Name and data node protection: By default Hadoop HTTP web consoles (JobTracker, NameNode, TaskTrackers, and DataNodes) allow access without any form of authentication. The good news is that Hadoop RPC and HTTP web consoles can be configured to require Kerberos authentication. Bi-directional authentication of nodes is built into Hadoop, and available in some other big data environments as well. Hadoop‟s model is built on Kerberos to authenticate

applications to nodes, nodes to applications, and client requests for MapReduce and similar functions. Care must be taken to secure granting and storage of Kerberos tickets, but this is a very effective method for controlling what nodes and applications can participate on the cluster. Application protection: Big data clusters are built on web-enabled platforms –   which means that remote injection, cross-site scripting, buffer overflows, and logic attacks against and through client applications are all possible avenues of attack for access to the cluster. Countermeasures typically include a mixture of secure code development  practices (such as input validation, and address space randomization), network segmentation, and third-party tools (including Web Application Firewalls, IDS,

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authentication, and authorization). Some platforms offer built-in features to  bolster application protection, such as YARN‟s web application proxy service.

Archive protection: As backups are largely an intractable problem for big data, we don‟t need to worry much about traditional backup/archive security. But just

 because legitimate users cannot perform conventional backups does not mean an attacker would not create at least a partial backup. We need to secure the management plane to keep unwanted copies of data or data nodes from being  propagated. Access controls, and possibly network segregation, are effective countermeasures against attackers trying to gain administrative access, and encryption can help protect data in case other protections are defeated. In the end, our big data security recommendations boil down to a handful of standard tools which can be effective in setting a secure baseline for big data environments: Use Kerberos: This is effective method for keeping rogue nodes and applications off your cluster. And it can help protect web console access, making administrative functions harder to compromise. We know Kerberos is a pain to set up, and (re-)validation of new nodes and applications takes work. But without bidirectional trust establishment it is too easy to fool Hadoop into letting malicious applications into the cluster, or into accepting introduce malicious nodes –   which can then add, alter, or extract data. Kerberos is one of the most effective security controls at your disposal, and it‟s built  into the Hadoop infrastructure, so use it.

File layer encryption: File encryption addresses two attacker methods for circumventing normal application security controls. Encryption protects in case malicious users or administrators gain access to data nodes and directly inspect

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files, and it also renders stolen files or disk images unreadable. Encryption  protects against two of the most serious threats. Just as importantly, it meets our requirements for big data security tools  –   it is transparent to both Hadoop and calling applications, and scales out as the cluster grows. Open source products are available for most Linux systems; commercial products additionally offer external key management, trusted binaries, and full support. This is a cost-effective way to address several data security threats. 8. Management:   Deployment consistency is difficult to ensure in a multi-node environment. Patching, application configuration, updating the Hadoop stack, collecting trusted machine images, certificates, and platform discrepancies, all contribute to what can easily become a management nightmare. The good news is that most of you will be deploying in cloud and virtual environments. You can leverage tools from your cloud provider, hypervisor vendor, and third parties (such as Chef and Puppet) to automate pre-deployment tasks. Machine images,  patches, and configuration should be fully automated and updated prior to deployment. You can even run validation tests, collect encryption keys, and request access tokens before nodes are accessible to the cluster. Building the scripts takes some time up front but pays for itself in reduced management time later, and additionally ensures that each node comes up with baseline security in  place. Log it!: Big data is a natural fit for collecting and managing log data. Many web companies started with big data specifically to manage log files. Why not add logging onto your existing cluster? It gives you a place to look when something fails, or if someone thinks perhaps you have been hacked. Without an event trace you are blind. Logging MR requests and other cluster activity is easy to do, and increases storage and processing demands by a small fraction, but the data is

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indispensable when you need it. Secure communication: Implement secure communication between nodes, and between nodes and applications. This requires an SSL/TLS implementation that actually protects all network communications rather than just a subset. Cloudera appears to get this right, and some cloud  providers offer secure communication options as well; otherwise you will likely need to integrate these services into your application stack. TRANSACTION LOG DATA

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1. Ibm analysis of big data 2. John

Webster

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“

Understanding

Big

Data

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Searchstorage.techtarget.com 3. Bill Franks – “What‟s up With In-Memory?”, May 7 ,2012 iilanalytics.com. 4. PankajMaru  –  “Datat Scientist: The new kid on the IT block”, Sep 3 ,2012,CIOL.com. 5. Yellow White Paper- “ In- Memory Analytics ,ww.yellowfin.bi.i 6. “Morgan Stanley takes on Big Data With Hadoop”, March 30,2012 ,Forbes.com 7. Ravi Kalakota, “New Tools For New Times - Primer on big Data, hadoop and “In-memory”

8. Data Clouds”, May 15,2011,practical analytics.wordpress.com 60