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SAS Clinical Research Trainers required for elegant it services good salary based on training quality 1. History of SAS 2. SAS comes in ERP sector or not? Why? 3. Role of Statistical Analysis in Clinical Research Study and Banking analysis 4. SAS role in Clinical Research Study and Banking analysis 5. Data modeling and Data mining concepts 6. Project management in Clinical Research Study analysis and Banking analysis 7. What is Clinical research Study? 8. What is Protocol and role of Protocol in Clinical Research Study? 9. What is randomization and non randomization? Which is playing 10. Main role in Clinical research study? 11.What is SOP (Standard operating Procedure)? 11. What is DBMS? 12. Role of DBMS team in Clinical Research Study 13. What is CDM (Clinical data management)? 14. Importance of CDM systems for data loading 15. What is SAP (Statistical Analysis Plan)? 16. Role of SAP in Clinical Research Study 17. SAS Work Flow in Clinical Research Study 18. Relation between SAS and DBMS 19. Interaction between SAS with CDMs for data access 20. Various report generation in Clinical Research Study 21. Role of SAS in Credit Analysis and Different Banking Channels 22. When will go any Industry for Data modeling and Data mining 23. Role of SAS in Data modeling and Data mining GETTING STARTED WITH SAS SYSTEMS: 1. Basic operating system commands, operating system file structures 2. Managing windows in SAS window environment 3. How to run SAS application in different modes and in different environment like Windows, Unix and Mainframes. 4. Use of different kind of SAS products and how to use in SAS application. 5. Difference between the SAS products. 6. Why using the SAS in different sectors 7. How to use the Data Step to read and manipulate complex forms of data 8. Write Data and Proc Steps 9. Data step compile and execution 10. To run SAS application on different modes 11. Reading internal raw data into SAS 12. Read any type of external raw data into SAS 13. Reading raw data SAS environment into DATASET using Input statement & advance INFILE Statement options 14. Working with Data Storage in SAS libraries creation for user defined libraries and multi-engine architecture 15. Using a single libref to reference some or all SAS libraries reading and printing mixed records formats 16. Reading packed and zoned decimal data Working with EBCDIC and ASCII data 17. Reading data from dataset to another dataset 18. To manage the SAS window environment use with Global options 19. Reducing memory requirements with BUFFNO and BUFSIZE working with SAS dataset options 20. To manage existing data with controlling statements and expressions 21. Creating summary information, SAS functions, transforming data 22. Changing variable types using the PUT and INPUT functions Summarizing Data Files 23. Generation Data Sets to create historical information SAS 24. To export data from datasets to delimiter files using with dataset block 25. Understand error messages in the SAS log and debug your program 26. Use with error handling concepts Thanks&Regards Elegant IT Services, #nd Floor,Aswath Nagar Varthur main road,Near Railway Fly Over Maratha halli,Land mark: Chemmunar Jewellers,

SAS Clinical research organization oriented training in elegant it services in marathahalli. 1. History of SAS 2. SAS comes in ERP sector or not? Why? 3. Role of Statistical Analysis in Clinical Research Study and Banking analysis 4. SAS role in Clinical Research Study and Banking analysis 5. Data modeling and Data mining concepts 6. Project management in Clinical Research Study analysis and Banking analysis 7. What is Clinical research Study? 8. What is Protocol and role of Protocol in Clinical Research Study? 9. What is randomization and non randomization? Which is playing 10. Main role in Clinical research study? 11.What is SOP (Standard operating Procedure)? 11. What is DBMS? 12. Role of DBMS team in Clinical Research Study 13. What is CDM (Clinical data management)? 14. Importance of CDM systems for data loading 15. What is SAP (Statistical Analysis Plan)? 16. Role of SAP in Clinical Research Study 17. SAS Work Flow in Clinical Research Study 18. Relation between SAS and DBMS 19. Interaction between SAS with CDMs for data access 20. Various report generation in Clinical Research Study 21. Role of SAS in Credit Analysis and Different Banking Channels 22. When will go any Industry for Data modeling and Data mining 23. Role of SAS in Data modeling and Data mining GETTING STARTED WITH SAS SYSTEMS: 1. Basic operating system commands, operating system file structures 2. Managing windows in SAS window environment 3. How to run SAS application in different modes and in different environment like Windows, Unix and Mainframes. 4. Use of different kind of SAS products and how to use in SAS application. 5. Difference between the SAS products. 6. Why using the SAS in different sectors 7. How to use the Data Step to read and manipulate complex forms of data 8. Write Data and Proc Steps 9. Data step compile and execution 10. To run SAS application on different modes 11. Reading internal raw data into SAS 12. Read any type of external raw data into SAS 13. Reading raw data SAS environment into DATASET using Input statement & advance INFILE Statement options 14. Working with Data Storage in SAS libraries creation for user defined libraries and multi-engine architecture 15. Using a single libref to reference some or all SAS libraries reading and printing mixed records formats 16. Reading packed and zoned decimal data Working with EBCDIC and ASCII data 17. Reading data from dataset to another dataset 18. To manage the SAS window environment use with Global options 19. Reducing memory requirements with BUFFNO and BUFSIZE working with SAS dataset options 20. To manage existing data with controlling statements and expressions 21. Creating summary information, SAS functions, transforming data 22. Changing variable types using the PUT and INPUT functions Summarizing Data Files 23. Generation Data Sets to create historical information SAS 24. To export data from datasets to delimiter files using with dataset block 25. Understand error messages in the SAS log and debug your program 26. Use with error handling concepts Thanks&Regards Elegant IT Services, #nd Floor, Aswath Nagar,Varthur main road, Opp Safal Fresh,Near Railway Fly Over Maratha halli,Bangalore

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Spanning the 1870s to the present, Individuality Incorporated demonstrates how crucial a knowledge of Native American-White history is to rethinking key issues in American studies, cultural studies, and the history of subjectivity. Joel Pfister proposes an ingenious critical and historical reinterpretation of constructions of “Indians” and “individuals.” Native Americans have long contemplated the irony that the government used its schools to coerce children from diverse tribes to view themselves first as “Indians”—encoded as the evolutionary problem—and then as “individuals”—defined as the civilized industrial solution. As Luther Standing Bear, Charles Eastman, and Black Elk attest, tribal cultures had their own complex ways of imagining, enhancing, motivating, and performing the self that did not conform to federal blueprints labeled “individuality.” Enlarging the scope of this history of “individuality,” Pfister elaborates the implications of state, corporate, and aesthetic experiments that moved beyond the tactics of an older melting pot hegemony to impose a modern protomulticultural rule on Natives. The argument focuses on the famous Carlisle Indian School; assimilationist novels; Native literature and cultural critique from Zitkala-Sa to Leslie Marmon Silko; Taos and Santa Fe bohemians (Mabel Dodge Luhan, D. H. Lawrence, Mary Austin); multicultural modernisms (Fred Kabotie, Oliver La Farge, John Sloan, D’Arcy McNickle); the Southwestern tourism industry’s development of corporate multiculturalism; the diversity management schemes that John Collier implemented as head of the Indian New Deal; and early formulations of ethnic studies. Pfister’s unique analysis moves from Gilded Age incorporations of individuality to postmodern incorporations of multicultural reworkings of individuality to unpack what is at stake in producing subjectivity in World America.

QA Testing Course Content: MANUAL TESTING SDLC 1. What are the different phases of SDLC? 2. How does the process of Software Development Start? 3. Project Initiation 4. Requirement Gathering and Analysis 5. What is Requirement document and what it contains? 6. What is use case document and what it contains? 7. What is Basic path and Alternate Path? 8. Role of Business Analyst 9. Example for explaining each phase 10. Role of technical specification team 11. What is Technical specification document? 12. What is System Design? 13. Role of Design team 14. What is design document? 15. Role of architecture team 16. System development 17. Role of development team 18. Deliverable of Development phase 19. System testing 20. Role of testers and types of testing 21. User acceptance testing 22. System deployment 23. System maintenance 24. Events in the maintenance phase like bug fixes 25. STLC 26. How are the phases of STLC carried out? 27. What is testing? 28. Role of testers 29. Why do we need to test? 30. Activities involved in the testing phase 31. What is test plan and test case document? 32. Steps of test case execution 33. What does test case document contain? 34. How to write test case document? 35. What is required to test any application? TEST CASES 1. What is test case? 2. What does test case document contain? 3. How to write test case document? 4. Different test case techniques TEST PLAN 1. What is Test Plan? 2. How to write test plan document? 3. What does the test plan document contain? 4. Who writes and approves the test plan document? 5. How manage the test case documents? 6. What is the pass/fail criterion? TYPES OF TESTING 1. Different Phases of testing 2. What is unit testing? 3. What is Minimum acceptance testing? 4. What is integration, system and system integration testing? 5. What is User acceptance testing? 6. What is Regression Testing? DEFECT ANALYSIS 1. What is a defect? 2. Various Defect tracking tools 3. How to use the defect tracking tools? 4. How to enter the details of defect in the defect tracking tool? 5. How to identify a defect? What is severity and priority? TRACEABILITY MATRIX 1. What is Traceability Matrix[TM]? 2. Who Prepares the TM document? 3. What is the reference for writing TM? 4. What is the use of TM? 5. What is present in the TM document? 6. Sample TM 7. Tools used for developing TM Various Teams and their Roles 1. Configuration Management Team 2. Role of configuration management team 3. What is Deployment? 4. Deployment Team 5. Role of Deployment team 6. Version Control 7. Various version tools and its usage 8. Difference between QC and QA 9. What is automation testing? 10. Why, what and when to automate? 11. Various tools used for automation testing MANAGEMENT TOOL QUALITY CENTER 1. Introduction to Quality Center 2. What is Quality Center ? 3. Why to use Quality Center 4. Version of Quality Center 5. Overview of quality Center user interface 6. Various tabs in Quality center 7. Requirement Module 8. Requirement Module Overview 9. Creating Requirement tree 10. Creating Parent & Child requirements 11. Understanding the Description tab 12. Understanding the History tab 13. Uploading attachments to requirement 14. A glance at various columns in requirement 15. Types of views in requirement module 16. Requirement grid view 17. Requirement coverage view 18. Test Plan Module 19. Creating folder structure 20. Create test cases 21. Requirement coverage 22. Test Lab Module 23. Overview of Test Lab 24. Detail of various tabs 25. Creating folders 26. Creating Test sets 27. Adding test cases 28. Different columns in Execution Grid 29. Execution Flow tab 30. Scheduling Test runs 31. Test Set Properties tab 32. Linked Defects tab 33. Running Tests manually 34. Running Tests automatically 35. Viewing Test results Quality Center Defects Overview of Defects 1. Creating defects after test execution 2. Adding defects in defect module 3. Creating defects during test execution 4. Linking Defects 5. How to close a defect 6. Closing defects after retest 7. Mailing the defects 8. Quality Center reports & graphs 9. Why Reports & Graphs? 10. How to create reports & graphs for Analysis? AUTOMATION TESTING: QUICK TEST PROFESSIONAL 1. Introduction to QTP 2. What is QTP 3. Prepare Before Automation 4. Application Under Test 5. First look at Add-Ins for QTP 6. Understanding the QTP User Interface Setting up preferences in QTP before recording. 1. Different versions of QTP(Text based) 2. Difference between QTP 9.0 and QTP 8.2. 3. Difference between QTP 9.2 and QTP 9.0 4. Difference between QTP 9.5 and QTP 9.2. • Record and run a script • Record a simple script • Save the script • Run the test script • Understand the result Introduction to Object Repository 1. What is Object Repository 2. Types of Object Repository 3. Object Repository Manager 4. Associating shared Object Repository with test 5. Shared Object Repository 6. Local Object Repository 7. Create a shared Object Repository 8. Add objects to shared Object Repository 9. Save an shared Object Repository 10. Modify the shared Object Repository 11. Comparing Local and Shared Object Repository Synchronization 1. What is Synchronization 2. Synchronization in QTP 3. Add Synchronization Steps for an Object Check Point 1. What is Check Point 2. Using Standard Check Point 3. Using Text Check Point 4. Using Access ability check point 5. Using XML Check Point Database checkpoint 1. What is database checkpoint 2. Writing SQL Query using Microsoft Query 3. Parameterize a database checkpoint 4. Output Values 5. What is Output value 6. Using Standard Output value 7. Using Text Output value 8. Using Database Output value 9. Using XML Output value Data Driven Test 1. What is Data Driven test 2. What is Parameterization http://trainingforu.blogspot.in/ Contact Person: Ashwini (M) 09620914455. Email: ashwini.hbst@gmail.com For More Details: Contact or Consult Our Office Once.

HADOOP - Modules that we undertake: Introduction to Bigdata •Architecture of BD •3V Concept •Framework and Applications/Tools •Samples •What is Hadoop? •History of Hadoop •Building Blocks - Hadoop Eco - System •Who is behind Hadoop? •What Hadoop is good for and what it is not •HDFS Overview and Architecture •HDFS Installation •HDFS Use Cases •Hadoop FileSystem Shell •FileSystem Java API •Hadoop Configuration •Map/Reduce 2.0 •Pig •Hive •Map Reduce Workflows •Sqoop •HBase - The Hadoop DataBase •Big Data Analysis with R •Data Science •Application and Certification Introduction to Big Data and Hadoop •What is Big Data? •What are the challenges for processing big data? •What technologies support big data? •Distributed systems •What is Hadoop? •Why Hadoop? •History of Hadoop •Use Cases of Hadoop •Hadoop eco System •HDFS •Map Reduce •Statistics Understanding the Cluster •Typical workflow •Writing files to HDFS •Reading files from HDFS •Rack Awareness •5 daemons Best Practices for Cluster Setup •Best Practices •How to choose the right hadoop distribution •How to choose right hardware Cluster Setup •Install Pseudo cluster •Install Multi node cluster •Configuration •Setup cluster on Cloud - EC2 •Tools •Security •Benchmarking the cluster Routine Admin procedures •Metadata & Data Backups •Filesystem check (fsck) •File system Balancer •Commissioning and decommissioning nodes •Upgrading •Using DFSAdmin Monitoring the Cluster •Using the Web user interfaces •Hadoop Log files •Setting the log levels •Monitoring with Nagios Install,Configure and use •PIG •HIVE •HBASE •Flume and Sqoop •Zookeeper Contact Details Dotexe Technologies, #23, South Sivan Koil Street, Vadapalani Chennai–,Phone:+,

Listen to Alberti's voice. This is what Mark Jarzombek has done in studying virtually all of Alberti's writings on philosophy, ethics, aesthetics architecture, and literature. Jarzombek's thorough grasp of Alberti's thought and painstaking analysis of his elusive identity transform our image of this remarkable man carving out a new place for Alberti in literary theory, art history, and Renaissance scholarship. Instead of warming over the stereotypes of Alberti as a "universal man" or as a proponent of "civic Humanism," Jarzombek explores Alberti's views on the relationship between the writer and society. He asserts that, while Alberti was indeed an architect, an art theorist and a man of letters, he was above all a theoretician of writing: "Everywhere one turns, the problems of writing, authorship and textuality seem to appear, from his first writings... to his last." Jarzombek, opening the possibilities for a different type of discussion of Alberti and of such major works as De pictura and De re aedificatora, places Alberti more accurately within the context of his times and clarifies the intertextual relationship among his works. Jarzombek's investigation brings to light themes that have remained hidden in the complex world of Alberti's speculations The Alberti of Jarzombeks book is an outsider struggling to resolve conflicting impulses of pessimism and hope. He is also a profound and willful thinker who, while amalgamating contemporary trends, did not endorse them but countered with a cosmological philosophy of his own. Mark Jarzombek is Assistant Professor of Architectural History at Cornell University.

"Danielle Goldman's contribution to the theory and history of improvisation in dance is rich, beautiful and extraordinary. In her careful, rigorously imaginative analysis of the discipline of choreography in real time, Goldman both compels and allows us to become initiates in the mysteries of flight and preparation. She studies the massive volitional resources that one unleashes in giving oneself over to being unleashed. It is customary to say of such a text that it is 'long-awaited' or 'much anticipated'; because of Goldman's work we now know something about the potenza, the kinetic explosion, those terms carry. Reader, get ready to move and be moved." ---Fred Moten, Duke University "In this careful, intelligent, and theoretically rigorous book, Danielle Goldman attends to the 'tight spaces' within which improvised dance explores both its limitations and its capacity to press back against them. While doing this, Goldman also allows herself---and us---to be moved by dance itself. The poignant conclusion, evoking specific moments of embodied elegance, vulnerability, and courage, asks the reader: 'Does it make you feel like dancing?' Whether taken literally or figuratively, I can't imagine any other response to this beautiful book." ---Barbara Browning, New York University "This book will become the single most important reflection on the question of improvisation, a question which has become foundational to dance itself. The achievement of I Want to Be Ready lies not simply in its mastery of the relevant literature within dance, but in its capacity to engage dance in a deep and abiding dialogue with other expressive forms, to think improvisation through myriad sites and a rich vein of cultural diversity, and to join improvisation in dance with its manifestations in life so as to consider what constitutes dance's own politics." ---Randy Martin, Tisch School of Arts at New York University I Want To Be Ready draws on original archival research, careful readings of individual performances, and a thorough knowledge of dance scholarship to offer an understanding of the "freedom" of improvisational dance. While scholars often celebrate the freedom of improvised performances, they are generally focusing on freedom from formal constraints. Drawing on the work of Michel Foucault and Houston Baker, among others, Danielle Goldman argues that this negative idea of freedom elides improvisation's greatest power. Far from representing an escape from the necessities of genre, gender, class, and race, the most skillful improvisations negotiate an ever shifting landscape of constraints. This work will appeal to those interested in dance history and criticism and also interdisciplinary audiences in the fields of American and cultural studies. Danielle Goldman is Assistant Professor of Dance at The New School and a professional dancer in New York City, where she recently has danced for DD Dorvillier and Beth Gill. Cover art: Still from Ghostcatching, 1999, by Bill T. Jones, Paul Kaiser, and Shelley Eshkar. Image courtesy of Kaiser/Eshkar.

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Type Tutoring The Best MSBI Online Training Institute From Hyderabad, India Course Content: MicroSoft BI Introduction Introduction to Microsoft Business Intelligence Installing and using BI tools Configuring BI tools SSIS INTRODUCTION TO SQL SERVER INTEGRATION SERVICES Over view of integration services solutions Integration Services Tools DEVELOPING INTEGRATION SERVICES SOLUTIONS Creating an Integration Services Solution Using Variables Building And Running a Solution IMPLEMENTING CONTROL FLOW Control Flow Tasks Control Flow Precedence Constraints Control Flow Containers IMPLEMENTING DATA FLOW Data Flow Sources and Destinations Data Flow Transformations Data Flow Pipeline IMPLEMENTING LOGGING Overview of Integration Services Logging Implementing Logging DEBUGGING AND ERROR HANDLING Debugging a Package Implementing handling IMPLEMENTING CHECKPOINTS AND TRANSACTIONS Implementing Checkpoints Implementing Transactions SSIS ADMINISTRATION Deploying Packages Managing Packages Securing Packages Scheduling a Package SSRS INTRODUCTION TO MICROSOFT SQL SERVER REPORTING SERVICES Over View of SQL Server Reporting Services Installing Reporting Services Reporting Services Tools AUTHORING REPORTS Creating a Basic Table Report Formatting Report Pages Calculating Values Interactive Navigation Displaying Data MANIPULATING DATA SETS Defining Report Data Using parameters and Filters Using Parameter Lists PUBLISHING AND EXECUTING REPORTS Publishing reports Executing reports Creating Cached instances Creating Snapshots and Report History USING SUBSCRIPTIONS TO DISTRIBUTE REPORTS Introduction to Report Subscriptions Creating Report Subscriptions Managing Report Subscriptions SSRS ADMINISTRATION Server Administration Performance and Reliability monitoring Administering Report Server Databases Security Administration SSAS INTRODUCTION TO ANALYSIS SERVICES OLTP,DWH OLAP TYPES SSAS Architecture Dimension Tables Fact tables Cubes Analysis Services Tools CUBE DESIGN PROCESS AND CONCEPTS Dimensions Measure groups Creating Simple Calculations Aggregations and Hierarchies Dimensions Relationships Transactions Perspectives ADVANCED CUBE DESIGN PROCESS MDX introduction and Queries Implementing KPI Implementing Actions Using Write back SSAS ADMINISTRATION Apply Security Roles Levels of Security VLDB with partitioning OLAP Processing Monitoring Cube activity Deployment and Scheduling Planning for Disaster Recovery Performance Tuning SSAS

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Even they can be innovative about their new products so that it can pertain to the customer�s viewpoint. �Big data analytics is becoming an integral part of organisations who want to grow in this age of innovation and is being done by most of the big companies. There is huge scope of big data analytics professionals as this is going to be an essential part of companies in the future. �Big data has become vital to our existence, in consideration one may take into account the recommendations provided by the e-commerce sites based on our search queries of for that matter analysis of our interests based on an acute analysis of our browser history and other frequent activities. �Big data is characterized by three vital aspects, volume, velocity and variety of its generation. As we insinuated earlier, big data can be generated at large volumes, the data is generated from various sources, which may bestow it with a variety formats and depending on type of data and nature of its origin, it may be generated at variable speeds. �Hadoop is the data platform chosen by many because it provides high performance � especially if you replace MapReduce in the Hadoop stack with the Apache Spark data processing engine. �Hadoop has evolved into a user-friendly data management system. Different implementations have done their part to optimize Hadoop�s manageability through different administrative tools. Look for a distribution that has intuitive administrative tools that assist in management, troubleshooting, job placement and monitoring. Advanced Hadoop EcoSystems Training in Chennai Most wanted � Classroom Training Now @ Chennai Weekend / Online /Fasttrack Training Modes Learn anything under Big Data & Cloud � Talk to our Big Data Expert Team. 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Best Bigdata Hadoop coaching from Experts Hadoop Training in Chennai | Best Hadoop Training Chennai HadoopTrainingChennai.IN �is a Chennai based Leading big data training and Hadoop Training institute in Chennai & Big Data Training Chennai Company. Our main focus areas are Hadoop & BigData Training, Technology Consulting and Software Development. �Hadoop Training in Chennai provided by Certified Professionals. We are the Best Hadoop Training Institute in Chennai. Best Bigdata Hadoop coaching from Experts Hadoop Training in Chennai | Best Hadoop Training Chennai Advanced Hadoop EcoSystems Training in Chennai Most wanted � Classroom Training Now @ Chennai Weekend / Online /Fasttrack Training Modes Learn anything under Big Data & Cloud � Talk to our Big Data Expert Team. BigData Developer / Consultant/ Architect / Admin Training Programs with placements! Hadoop Training in Chennai provided by Certified Professionals. We are the Best Hadoop Training Institute in Chennai. Best Bigdata Hadoop coaching from Experts � Hadoop Training in Chennai | Best Hadoop Training Chennai Hadoop Training in Chennai provided by Certified Professionals. We are the Best Hadoop Training Institute in Chennai. Best Bigdata Hadoop coaching from Experts / HadoopTrainingChennai.IN Hadoop & BigData Training Chennai INDIA: Best Hands on Training warehouse Job Oriented Hadoop & BigData Training � Classroom & Online Training BigdataTraining.IN Chennai, Leaders in Hadoop Training � � Big data is providing supplier networks with greater data accuracy, clarity, and insights, leading to more contextual intelligence shared across supply chains. �Big data is revolutionizing how supplier networks form, grow, proliferate into new markets and mature over time. Transactions aren�t the only goal, creating knowledge-sharing networks is, based on the insights gained from big data analytics. �Big data is having an impact on organizations� reaction time to supply chain issues (41%), increased supply chain efficiency of 10% or greater (36%), and greater integration across the supply chain (36%). �Embedding big data analytics in operations leads to a 4.25x improvement in order-to-cycle delivery times, and a 2.6x improvement in supply chain efficiency of 10% or greater. �Big data has the potential to provide improved traceability performance and reduce the thousands of hours lost just trying to access, integrate and manage product databases that provide data on where products are in the field needing to be recalled or retrofitted. �Increasing supplier quality from supplier audit to inbound inspection and final assembly with big data. �The advertised salary for technical professionals with Big Data expertise is $ �net of bonuses and additional compensation. The most in-demand skills are VMWare expertise, application development, open source technology, data warehousing and Python programming skills. The Hiring Scale is 72 for jobs that require big data skills with 11 candidates per job opening as of June . � The enormity of big data is not confined to only volume and velocity; it is also referred by the variety, variability and complexity of the data. So it requires more than just some tweaks and upgrades for fully realising the potential of big data. � The big deal about big data analytics is the way organisations use it to leverage their business performance, innovate and provide better customer service �Analysing the big data, organisations can understand better what customers want and with the help of this, they can provide better after-sales service. Even they can be innovative about their new products so that it can pertain to the customer�s viewpoint. �Big data analytics is becoming an integral part of organisations who want to grow in this age of innovation and is being done by most of the big companies. There is huge scope of big data analytics professionals as this is going to be an essential part of companies in the future. �Big data has become vital to our existence, in consideration one may take into account the recommendations provided by the e-commerce sites based on our search queries of for that matter analysis of our interests based on an acute analysis of our browser history and other frequent activities. �Big data is characterized by three vital aspects, volume, velocity and variety of its generation. As we insinuated earlier, big data can be generated at large volumes, the data is generated from various sources, which may bestow it with a variety formats and depending on type of data and nature of its origin, it may be generated at variable speeds. �Hadoop is the data platform chosen by many because it provides high performance � especially if you replace MapReduce in the Hadoop stack with the Apache Spark data processing engine. �Hadoop has evolved into a user-friendly data management system. Different implementations have done their part to optimize Hadoop�s manageability through different administrative tools. Look for a distribution that has intuitive administrative tools that assist in management, troubleshooting, job placement and monitoring. 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