1 Big Data - Solutions for RDBMS Problems - A Survey S. Vikram Phaneendra 1, E. Madhusudhana Reddy 2 Assistant Professor, Dept. of CSE, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh Professor, Dept. of CSE, Madanapalle Institute of Technology & Science, Madanapalle , Andhra Pradesh Abstract: Now a day s increases shared data very fast due to social networking and mobile phone. In olden days the data is less and able to handle most popular RDBMS concepts, but recently it is difficult to handle this much of huge data through old RDBMS tools. To overcome this situation we told to prefer using of Big Data. In this paper, we will outline the origin and history of this new system to handle Big Data. We look up to current popular big data systems, illustrated by Hadoop architecture and its current & future use-cases of this system, apache drill high level architecture, applications of Big Data and its challenges. Keywords: Big Data, Hadoop, Apache Drill, RDBMS Solutions, Big Data Solutions. I. INTRODUCTION The data management firm has grown over the few decades, first and foremost Relational Data Base Management Systems (RDBMS) technology. Still today, the majority of backend systems are RDBMS for online digital data, financial system, medical, conveyance, insurance coverage, and telecommunication business. As of the sum of data collected, and analyzed in endeavors have increased many-folds in volume, variety and velocity of generation and consumption. Due to this the organizations have struggled with architectural limitations of traditional RDBMS architectures. Effect of this problem, the researchers are invented a new class of system, that has to be designed and implemented a new phenomenon of Big Data . A. Sources of Big Data infrastructure As of the popularity of Internet was one of the primary reason for rapid growth of communication and connectivity in the world, we saw emerge of the Big Data platforms in the Internet environment. The online world around us is having becoming abnormal proliferation of data. In Facebook one million users in year 2004, it exceeds more than one billion users in year 2012, i.e. a thousand-fold increase in within 8 years span. Recently more than 60% of users access Facebook from their mobile phones. The data generated by social networks are proportional to number of contacts among users of the social network, rather than number of users. According to Metcalfe s Law , and its variants, N users of contacts are proportional to N*log N. thus, the growth of contacts, and conversations among users in a social network results data generation . In 1998, Google was founded with the goal of organizing entire information in the world, became the dominant content discovery platform in the World Wide Web (WWW), managing man-power and semi-automated ways, such as web portals and directories. The Google faced challenges are, crawling the web, indexed, ranking of several billions of web pages difficult to solve with the existing data management systems economically. The amounts of data available on the web in Google s search index explode from 26 million pages to more than one trillion pages within a decade . In addition to this data was multi structured, i.e. having natural language text, images, audio, video, sensor information, animations, and structured data . High Velocity: Due to the deluge of data from different data sources, enterprises are increasingly facing challenges. Because of increasing connectedness of people, applications, sensors, diversity and speed of data is very large. Analysis of this data with minimum delay is a challenging task . What is Big Data Big Data is which demands cost effective, innovative forms of information processing for enhanced insight and decision-making of high-volume, high-velocity, and highvariety . Big Data is naturally huge amount of data able to process and which cannot be effectively, processed, captured and analyzed by traditional database and search tools in minimal amount of time. McKinsey estimates that, the big in Big Data would be anywhere between few dozens of terabytes to petabytes of the enterprises data. The Copyright to IJARCCE
2 explosion of Big Data information is mainly due to the vast amount of data generated by social networking platforms, various mobile network devices, and sensors data and so on. Data analysts call it as Digital Universe . Historical Data Social Data Attributes of Big Data differs from other data in 5 dimensions such as Volume, Velocity, Variety, Value, and Complexity. Value: The unstructured data is also having some valuable information, so mine such data from huge volumes of information is more considerable. Big Data Convergence Complexity: Considering to social networks data, connections and associated data which describes more about relationship among the data. Sensor Data Big Data Convergence Figure 2: Convergence of data from different views B. Versatile Technologies used in Big Data From Big Data can derive main data by aggregating enormous amounts of data integrated from various sources. The following diagram shows versatile technologies used in Big Data. A. Hadoop II. ARCHITECTURE Predictive Analysis Natural Language Processing Big Data Unstructured Data Hadoop is a group of applications projected to support parallel processing using the Google s MapReduce programming model  and is a quickly evolving ecosystem of components for implementing the Google Map Reduce Algorithms in a scalable model on commodity hardware. Users to process and store large volumes of data and analyzed it in ways not previously possible with less scalable solutions or standard SQLbased approached enabled by Hadoop. Visualization Machine Learning Algorithms Data Mining Figure 1: Various Technologies used in Big Data Conventional sorting, searching and processing algorithms would not scale to handle the data in this rage, and that too most of it being unstructured. Most popular Big Data processing technologies include natural language processing algorithms, predictive modeling, and machine learning algorithms, unstructured programming and other artificial intelligence based techniques . Bid Data is premeditated important for many enterprises, because any new product or new service will be eventually copied by competitors, but an organization can differentiate it by what it can do with the data it has. The following fig. shows the convergence of data from different views . I). Hadoop node types Hadoop have different types of nodes within each Hadoop cluster; these include, NameNodes, DataNodes and EdgeNodes. Hadoop architecture is modular, allows individual components to be able to scale up and down as the needs of the industry requirement. The basic type nodes for a Hadoop cluster are as follows: NameNode: The NameNode is the central location for data about the file system spreading in Hadoop environment. An environment can having one or two NameNodes, configuration allowed to provide minimal redundance between the NameNodes. The NameNode is link between clients of the Hadoop Distributed File System (HDFS) to locate intormation with the file system and provides updates for data that they have modified, added, moved and deleted. DataNode: DataNodes build up the all most all of the servers contained in a Hadoop environment. General Hadoop environments would have more than one DataNodes and it may be increased up to thousands based on capacity and performance needed. The DataNode Copyright to IJARCCE
3 servers have two functions: it having a portion of the data in the HDFS and it acts as a computing platform for running jobs running in local data within the HDFS. EdgeNode: the EdgeNode is the access point for the users that need to utilize the Hadoop environment and external applications. The EdgeNode resides between the Hadoop cluster and the corporate network to provide access control, logging, policy enforcement and gateway services to the Hadoop environment. A distinctive Hadoop environment will have a minimum at least one EdgeNode and more are required on performance needs. Figure 3: Node Types within Hadoop Clusters II). Hadoop Uses Actually the Hadoop was developed to be an open implementation of Google MapReduce and Google File System (GFS) . As the ecosystem around Hadoop has fill-grown, a variety of tools have been developed III). to streamline data access, information access, security, specialized additions for industries, and data management. Inspite of this large ecosystem, there are several primary uses and workloads for Hadoop that can be outlined as: Compute: A general use of Hadoop is as a distributed computing platform for processing of analyzing large amounts of information. The compute advantage is characterized by the need for huge number of CPUs and large amount of memory to store in process data. The Hadoop ecosystem provides the API required distributing and tracking workload as they are run on huge number of individual machines. Storage: one basic component of the Hadoop ecosystem is HDFS. The HFS allowing users to have a distinct addressable namespace, stretch across many thousands of servers, creating in a single large file system. HDFS maintains replication of the data to avoid hardware failures do not lead to data loss. Most of the users it will use this scalable file system as a place to store large amounts of information that is accessed within processes run in Hadoop and or by external systems. will commonly use this approach for presenting data in SQL format for easy integration with existing systems and streamlined access by users. What is Hadoop good for? Hadoop is a collection of application projected to support parallel processing using the MapReduce programming model. When a actual MapReduce algorithm is released, Hadoop was subsequently developed around them, these tools are designed for specific uses. The actual use is for managing large data sets that needed to be easily searched. It having several other specific uses has emerged for Hadoop as an influential solution. Large Data Sets: MapReduce coupled with HDFS is a victorious solution for storing huge volume of unstructured data. Scalable Algorithm: For distributed processing capability of Hadoop, Any algorithm can scale too many cores of minimal inter-process communications. Log Management: Hadoop is regularly used for analyzing and storing large sets of logs from different locations. In distributed environment log information is very necessary for tracing conversations among the nodes. It creates a very good area for manipulating, managing and analyzing different logs from diversity of sources within an organization, because the nature of distributed networks and it s scalability of Hadoop. Database: Hadoop components that allows to be presented in SQL-Like interfaces. It allows standard SQL Extract-Transform-load (ETL) Platform: now a day tools like SELECT, INSERT, DELETE, and UPDATE almost all companies using many of the data warehouses data within the Hadoop environment that too minimal and different relational database management system code changes to existing applications. Many of the users (RDBMS) platforms for their requirements within a Copyright to IJARCCE
4 organization. Maintaining data up to data and synchronized these all different data can be a difficult process. Hadoop is a single solution for synchronize these all types of data and able to process and provide required information. IV). Hadoop Adoption and use cases From some years, Hadoop and other Big Data technologies have become most popular in non-internet based organization, as well as also struggled to handle the data deluge. Many of the organizations of infrastructure in various industries, such as finance, healthcare, insurance, retail, manufacturing, banking, advertizing and others have been almost fully digitized. Up to now, these organizations, data was stored in archival systems for recurrently retrieval purposes. The data is growing it is difficult to retrieve required information. However there was a growing realization among these organizations that this information can be utilized for gaining competitive advantage, improving customer experiences, and increasing process efficiencies . The 3V s Volume, Velocity, and Variety of data, along with need to develop agile information driven applications, effects that the humans detecting patterns, analyzing and make indentify rich toolset data at hand. Conventional data exploration, visualization, e-commerce intelligence and reporting tolls are being adapted to coexist with this new Big Data technologies; then implement advances in machine learning algorithms and methods, as well as abundant speed processing power and have able to deep and predictive analysis to used in information technology (IT) enterprises . Industrial internet: the next leading edge Most of the use cases of Big Data are analyzing customer behavior, their buying models, their likes and dislikes as posted in social networking media and their conversations, their visiting websites, geographic location from their mobile devices, the system generated data could be the next frontier for Big Data Systems. In addition to low cost sensors technology and minimum-range wireless connectivity has created possibility of real-time monitoring and chronological patterns of traditionally analog data sources . The huge amount of data captured by the sensors; and prospect of storing and analyzing the data to make intelligent design, Operational decisions have created a new opportunity, now knowing by a new name as industrial internet . B. Apache drill high level architecture Earlier 2010, Google was published has seminal Dremel2 paper, it inaugurates two main innovations: generically handle nested data with column-striped representation and multilevel query execution trees, and those allowing for parallel processing of data broaden over thousands of computing nodes. These innovations are taken by the Apache Software Foundation in middle of the year 2012 and forming the core of a new incubator, Apache drill. At high level, 3 layers are in Apache Drill s Architecture. User: It provides REST, Command Line Interface (CLI), JDBC/ODBC, etc., for human or application driven Interface. Processing: Processing planar queries, execution, storage engines and allowable for pluggable query languages. Data Sources: pluggable data origins either in a cluster setup or local, providing in place data processing. Figure 4: Apache Drill s Architecture Copyright to IJARCCE
5 Main Features: Extensible Rules: This Apache Drill Architecture is designed for capability of extensibility with interfaces and well-defined Application Programming Interface (API). It includes, starting from user layer, pluggable Query Language through the query API and also it supports User Defined Functions (UDF). Complete Structured Query Language (SQL): Many settings are required in integrating Business Intelligence (BI) tools, Excel, SAP Crystal Reports, etc. Nested data as a 1st class citizen: Apache Drill is flexible concerning to support different types data. As nested data is becoming widespread like JSON /BSON in document stores, XML, etc., and pulling down of nested data is error-prone. This architecture is supports nested data directly, effectively establishing an extension to most popular nested data formats. Use but don t abuse schema: Different data sources increasingly, and this architecture do not have inflexible schemas; the schema may change quickly or differ in a per-record level. Apache Drill supports queries against unknown schemas and also the user able to define a schemas their requirement of let the Apache Drill discover it. III. APPLICATIONS OF BIG DATA We will analyze the impact and applications of Big Data of related technologies all over in various industries and technology domains. Unstructured data analysis from social media, multi-media to understand the tastes, preferences, and customer patterns and do sentiment analysis Targeted marketing based on user segmentation Competitor analysis Mobility: Mining of customer location data, call patterns. Integrate with social media to provide location based services like sale off ers, friend alerts, points-of interest suggestions etc. Geo-location analysis: Health care: Effective drug prescription by analyzing all structured and unstructured medical history and records of the patient Avoid un-necessary prescriptions Insurance: Risk analysis of customer Analyzing cross-sell and up-sell opportunities based on customer spending patterns Insurance portfolio optimization and pricing optimization B. Application across technology domains  Earthquake Analysis Search Engine improvements: New algorithms to analyze large unstructured data will be used. The algorithms will be artificial intelligence based working in parallel in multiple grids to process huge amount of data Business intelligence tools: Analytics tools will be able to provide new and creative visualizations to intuitively depict the meaning of the data Storage management tools: Private/cloud storage systems will undergo change to store huge amount of data Cloud computing: Cloud and social media play a vital role in handling Big Data. Cloud would be the platform of choice to store massive amount of data and to run the software as service to process the data ERP systems like CRM undergo great improvements. CRM system can help the on-call analysts to provide real-time customer offers, customer churn probability etc. A. Applications in industry domains  Financial Industry: Better financial data management Investment banking using aggregated information from various sources likes financial forecasting, asset pricing and portfolio management. More accurate pricing adjustments based on vast amount of real-time data Stock advises based on huge amount of stock Predictive analytics will be more effective by data analysis, unstructured data like social media content analyzing data from multiple dimensions etc. Credit worthiness analysis by analyzing huge C. An interesting Case of Big Data is US 2012 amount of customer transaction data from various sources Elections Pro-active fraudulent transaction analysis Usually Big Data had a bid impact and re-define the way Regulation conformance elections in US in the year This elections process Risk analytics elections team is used effectively Big Data for achieve Trading analytics victory. The democratic team was done data analysis and Retail industry: aggregated data from different data sources like voter list, Better analysis of supply chain data and touch social networking posts, fund raisers, etc., different tests points across Omnichannel operations were conducted to identify the voter s decision. Analyzing Customer segmentation based on previous the Big Data was the main differentiator in alternation a transactions and profi le information good percentage of voters participating in elections. Analysis of purchase patterns and tailor made product off erings IV. CHALLENGES Copyright to IJARCCE
6 One of the computing areas that are attracting a lot of attention is 'Big Data'. What exactly is Big Data? As per Wikipedia, 'Big Data is a collection of data sets so large and complex that it becomes difficult to process using onhand database management tools or traditional data processing apps . Large enterprises across industries, from retail to financial services to manufacturing, are today actively exploring this new and exciting arena. At the same time, the veracity of inputs received from social media remains a matter of concern. There are also challenges in measuring the return on investment (i.e., ROI) from socio-business intelligence exercises: Statistically sound techniques for measuring ROI, even for simple matter such as advertising campaigns, are as yet not widely popular. Both these questions, i.e., efficiently establishing the veracity of social media inputs, as well as properly measuring ROI from socio-business intelligence, pose challenges for future research . Challenges Storing and Maintaining the Big Data is a challenging task. The following challenges need to be faced by the enterprises or media when handling Big Data : Data Privacy Capture Duration Storages Search Sharing Analysis Visualizations REFERENCES  The Google File System, google.com/archive/gfs.html, October  MapReduce: Simplifi ed Data Processing on Large Clusters, com/archive/ mapreduce.html, December 2004  Dr. Milind Bhandarkar, Big Data Systems: Past, Present & (possibly) Future, Cover Story, CSI Communication, ISSN X Volume No. 37 Issue No. 1 April  Industrial Internet: Pushing the Boundaries of Minds and Machines, Industrial_Internet.pdf, November 2012  Shailesh Kumar Shivakumar, Big Data A Big game changer, Cover Story, CSI Communication, ISSN X Volume No. 37 Issue No. 1 April  Gautam Shroff,* Lipika Dey,** & Puneet Agarwal***, Socio- Business Intelligence Using Big Data, Technical Trends, CSI Communication, ISSN X Volume No. 37 Issue No. 1 April  A Kavitha*, S Suseela**, and G Kapilya***, Big Data, Article, CSI Communication, ISSN X Volume No. 37 Issue No. 1 April  Michael Hausenblas and Jacques Nadeau, APACHE DRILL: Interactive Ad-Hoc Analysis at Scale, DOI: /big _ MARY ANN LIEBERT, INC. _ VOL. 1 NO. 2 _ JUNE 2013 BIG DATA.  Jyotiranjan Hota, Adoption of In-Memory Analytics, Article, CSI Communication, ISSN X Volume No. 37 Issue No. 1 April  Avinash Kadam, Five Key Knowledge Areas for Risk Managers, Article, CSI Communication, ISSN X Volume No. 37 Issue No. 1 April  Bipin Patwardhan* and Sanghamitra Mitra**, Deriving Operational Insights from High Velocity Data, CIO Perspective, CSI Communication, ISSN X Volume No. 37 Issue No. 1 April  Joey Jablonski, Introduction to Hadoop, A Dell Technical White Paper, whitepapers/en/documents/hadoop-introduction.pdf.  Metcalfe s Law Recurses Down the Long Tail of Social Networks, wordpress.com/ 2006/08/18/metcalfesocialnetworks/, April 2006  We knew the web was big, July 2008  Pramod Taneja* and Prashant Wate**, Big Data Enabled Digital Oil Field, Research Front, CSI Communication, ISSN X Volume No. 37 Issue No. 1 April BIOGRAPHY S. Vikram Phaneendra received B.Tech (Computer Science and Engineering) from JNTU, Hyderabad, M.Tech (Computer Science) from JNTUA, Anantapur. Currently he is working as Assistant Professor, Computer Science and Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India. His research lie in the areas of Big Data, Cryptography & Network Security. Dr. E. Madhusudhana Reddy received M.C.A from S.V. University, Tirupathi, M.Tech (IT) from Punjabi University, Patiala, M.Phil (Computer Science) from Madurai Kamaraj University, Madhurai, and Ph.D from S.V. University, Tirupathi. Currently he is working as Professor, Computer Science and Engineering, Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh, India. His research interest lie in the areas of Data Mining and Warehousing, Cryptography & Network Security and E-Commerce. He has published 39 research papers in National/ International Journals and Conferences. He is a Research Supervisor in JNTUH, JNTUA, JNTUAK, Andhra Pradesh, India. Also he is life member of Indian Society of Technical Education of India (ISTE), life member of Cryptography Research Society of India (CRSI) and fellow in ISET, India. Copyright to IJARCCE
W H I T E P A P E R Deriving Intelligence from Large Data Using Hadoop and Applying Analytics Abstract This white paper is focused on discussing the challenges facing large scale data processing and the
ISSN: 2454-2377, October 2015 Big Data and Hadoop Simmi Bagga 1 Satinder Kaur 2 1 Assistant Professor, Sant Hira Dass Kanya MahaVidyalaya, Kala Sanghian, Distt Kpt. INDIA E-mail: email@example.com
Volume 1, Issue 7, December 2013 International Journal of Advance Research in Computer Science and Management Studies Research Paper Available online at: www.ijarcsms.com ISSN: 2321-7782 (Online) Big Data
The Future of Data Management with Hadoop and the Enterprise Data Hub Amr Awadallah (@awadallah) Cofounder and CTO Cloudera Snapshot Founded 2008, by former employees of Employees Today ~ 800 World Class
Manifest for Big Data Pig, Hive & Jaql Ajay Chotrani, Priyanka Punjabi, Prachi Ratnani, Rupali Hande Final Year Student, Dept. of Computer Engineering, V.E.S.I.T, Mumbai, India Faculty, Computer Engineering,
International Journal of Information and Computation Technology. ISSN 0974-2239 Volume 3, Number 7 (2013), pp. 655-662 International Research Publications House http://www. irphouse.com /ijict.htm Data
Managing Big Data with Hadoop & Vertica A look at integration between the Cloudera distribution for Hadoop and the Vertica Analytic Database Copyright Vertica Systems, Inc. October 2009 Cloudera and Vertica
Are You Ready for Big Data? Jim Gallo National Director, Business Analytics February 11, 2013 Agenda What is Big Data? How do you leverage Big Data in your company? How do you prepare for a Big Data initiative?
AGENDA What is BIG DATA? What is Hadoop? Why Microsoft? The Microsoft BIG DATA story Hadoop PDW Our BIG DATA Roadmap BIG DATA? Volume 59% growth in annual WW information 1.2M Zetabytes (10 21 bytes) this
Improving Data Processing Speed in Big Data Analytics Using HDFS Method M.R.Sundarakumar Assistant Professor, Department Of Computer Science and Engineering, R.V College of Engineering, Bangalore, India
Are You Ready for Big Data? Jim Gallo National Director, Business Analytics April 10, 2013 Agenda What is Big Data? How do you leverage Big Data in your company? How do you prepare for a Big Data initiative?
BIG DATA STRATEGY Rama Kattunga Chair at American institute of Big Data Professionals Building Big Data Strategy For Your Organization In this session What is Big Data? Prepare your organization Building
Indian Journal of Science The International Journal for Science ISSN 2319 7730 EISSN 2319 7749 2016 Discovery Publication. All Rights Reserved Perspective Big Data Framework for Healthcare using Hadoop
Big Data Are You Ready? Jorge Plascencia Solution Architect Manager Big Data: The Datafication Of Everything Thoughts Devices Processes Thoughts Things Processes Run the Business Organize data to do something
BIG DATA What it is and how to use? Lauri Ilison, PhD Data Scientist 21.11.2014 Big Data definition? There is no clear definition for BIG DATA BIG DATA is more of a concept than precise term 1 21.11.14
BIG DATA CHALLENGES AND PERSPECTIVES Meenakshi Sharma 1, Keshav Kishore 2 1 Student of Master of Technology, 2 Head of Department, Department of Computer Science and Engineering, A P Goyal Shimla University,
BIG DATA TECHNOLOGY Hadoop Ecosystem Agenda Background What is Big Data Solution Objective Introduction to Hadoop Hadoop Ecosystem Hybrid EDW Model Predictive Analysis using Hadoop Conclusion What is Big
Beyond Web Application Log Analysis using Apache TM Hadoop A Whitepaper by Orzota, Inc. 1 Web Applications As more and more software moves to a Software as a Service (SaaS) model, the web application has
III Big Data Technologies Today, new technologies make it possible to realize value from Big Data. Big data technologies can replace highly customized, expensive legacy systems with a standard solution
Chapter 7 Using Hadoop Cluster and MapReduce Modeling and Prototyping of RMS for QoS Oriented Grid Page 152 7. Using Hadoop Cluster and MapReduce for Big Data Problems The size of the databases used in
Investor Presentation Second Quarter 2015 Note to Investors Certain non-gaap financial information regarding operating results may be discussed during this presentation. Reconciliations of the differences
Technology Insight Paper Converged, Real-time Analytics Enabling Faster Decision Making and New Business Opportunities By John Webster February 2015 Enabling you to make the best technology decisions Enabling
How to use Big Data in Industry 4.0 implementations LAURI ILISON, PhD Head of Big Data and Machine Learning Big Data definition? Big Data is about structured vs unstructured data Big Data is about Volume
Volume 5, Issue 7, July 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Configuration Tuning
5 Keys to Unlocking the Big Data Analytics Puzzle Anurag Tandon Director, Product Marketing March 26, 2014 1 A Little About Us A global footprint. A proven innovator. A leader in enterprise analytics for
Big Data on Microsoft Platform Prepared by GJ Srinivas Corporate TEG - Microsoft Page 1 Contents 1. What is Big Data?...3 2. Characteristics of Big Data...3 3. Enter Hadoop...3 4. Microsoft Big Data Solutions...4
beyond possible Big Use End-user applications Big Analytics Visualisation tools Big Analytical tools Big management systems The 4 Pillars of Technosoft s Big Practice Overview Businesses have long managed
An ENTERPRISE MANAGEMENT ASSOCIATES (EMA ) White Paper Prepared for SAP April 2013 IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Table of Contents Introduction... 1 Drivers of Change...
Apache Hadoop in the Enterprise Dr. Amr Awadallah, CTO/Founder @awadallah, firstname.lastname@example.org Cloudera The Leader in Big Data Management Powered by Apache Hadoop The Leading Open Source Distribution of Apache
MySQL and Hadoop: Big Data Integration Shubhangi Garg & Neha Kumari MySQL Engineering 1Copyright 2013, Oracle and/or its affiliates. All rights reserved. Agenda Design rationale Implementation Installation
Hadoop Beyond Hype: Complex Adaptive Systems Conference Nov 16, 2012 Viswa Sharma Solutions Architect Tata Consultancy Services 1 Agenda What is Hadoop Why Hadoop? The Net Generation is here Sizing the
INFO 1500 Introduction to IT Fundamentals 5. Database Systems and Managing Data Resources Learning Objectives 1. Describe how the problems of managing data resources in a traditional file environment are
B I G D ATA How to Enhance Traditional BI Architecture to Leverage Big Data Contents Executive Summary... 1 Traditional BI - DataStack 2.0 Architecture... 2 Benefits of Traditional BI - DataStack 2.0...
Big Data Big Data Defined Introducing DataStack 3.0 Inside: Executive Summary... 1 Introduction... 2 Emergence of DataStack 3.0... 3 DataStack 1.0 to 2.0... 4 DataStack 2.0 Refined for Large Data & Analytics...
Executive Summary... 2 Introduction... 3 Defining Big Data... 3 The Importance of Big Data... 4 Building a Big Data Platform... 5 Infrastructure Requirements... 5 Solution Spectrum... 6 Oracle s Big Data
In-Memory Analytics for Big Data Game-changing technology for faster, better insights WHITE PAPER SAS White Paper Table of Contents Introduction: A New Breed of Analytics... 1 SAS In-Memory Overview...
Volume 3, Issue 6, June 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: www.ijarcsms.com Image
Exploiting Data at Rest and Data in Motion with a Big Data Platform Sarah Brader, email@example.com What is Big Data? Where does it come from? 12+ TBs of tweet data every day 30 billion RFID tags
Big Data Challenges and Success Factors Deloitte Analytics Your data, inside out Big Data refers to the set of problems and subsequent technologies developed to solve them that are hard or expensive to
Using Tableau Software with Hortonworks Data Platform September 2013 2013 Hortonworks Inc. http:// Modern businesses need to manage vast amounts of data, and in many cases they have accumulated this data
Big Data Explained An introduction to Big Data Science. 1 Presentation Agenda What is Big Data Why learn Big Data Who is it for How to start learning Big Data When to learn it Objective and Benefits of
5 Big Data Use Cases to Understand Your Customer Journey CUSTOMER ANALYTICS EBOOK CUSTOMER JOURNEY Technology is radically transforming the customer journey. Today s customers are more empowered and connected
Chapter 11 Map-Reduce, Hadoop, HDFS, Hbase, MongoDB, Apache HIVE, and Related Summary Xiangzhe Li Nowadays, there are more and more data everyday about everything. For instance, here are some of the astonishing
BIG Data An Introductory Overview IT & Business Management Solutions What is Big Data? Having been a dominating industry buzzword for the past few years, there is no contesting that Big Data is attracting
PAGE 1 l Teradata Magazine l Q1/2011 l 2011 Teradata Corporation l AR-6309 It s going mainstream, and it s your next opportunity. by Merv Adrian Enterprises have never had more data, and it s no surprise
Sensor Network Messaging Service Hive/Hadoop Mr. Apichon Witayangkurn firstname.lastname@example.org Department of Civil Engineering The University of Tokyo Contents 1 Introduction 2 What & Why Sensor Network
mwd a d v i s o r s Turning Big Data into Big Insights Helena Schwenk A special report prepared for Actuate May 2013 This report is the fourth in a series and focuses principally on explaining what s needed
Datenverwaltung im Wandel - Building an Enterprise Data Hub with Cloudera Bernard Doering Regional Director, Central EMEA, Cloudera Cloudera Your Hadoop Experts Founded 2008, by former employees of Employees
Introduction to Hadoop HDFS and Ecosystems ANSHUL MITTAL Slides credits: Cloudera Academic Partners Program & Prof. De Liu, MSBA 6330 Harvesting Big Data Topics The goal of this presentation is to give
Volume 4, Issue 1, January 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Transitioning
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 5, Issue. 1, January 2016,
Eric Ledu, The Createch Group, a BELL company Intelligence Analytics maturity Past Present Future Predictive Modeling Optimization What is the best that could happen? Raw Data Cleaned Data Standard Reports
ANALYTICS BUILT FOR INTERNET OF THINGS Big Data Reporting is Out, Actionable Insights are In In recent years, it has become clear that data in itself has little relevance, it is the analysis of it that
[ Consumer goods, Data Services ] TEKSYSTEMS GLOBAL SERVICES CUSTOMER SUCCESS STORIES QUICK FACTS Objectives Develop a unified data architecture for capturing Sony Computer Entertainment America s (SCEA)
A Study on Big-Data Approach to Data Analytics Ishwinder Kaur Sandhu #1, Richa Chabbra 2 1 M.Tech Student, Department of Computer Science and Technology, NCU University, Gurgaon, Haryana, India 2 Assistant
End to End Solution to Accelerate Data Warehouse Optimization Franco Flore Alliance Sales Director - APJ Big Data Is Driving Key Business Initiatives Increase profitability, innovation, customer satisfaction,
Hadoop & its Usage at Facebook Dhruba Borthakur Project Lead, Hadoop Distributed File System email@example.com Presented at the The Israeli Association of Grid Technologies July 15, 2009 Outline Architecture
GE Intelligent Platforms The Rise of Industrial Big Data Leveraging large time-series data sets to drive innovation, competitiveness and growth capitalizing on the big data opportunity The Rise of Industrial
Forecast of Big Data Trends Assoc. Prof. Dr. Thanachart Numnonda Executive Director IMC Institute 3 September 2014 Big Data transforms Business 2 Data created every minute Source http://mashable.com/2012/06/22/data-created-every-minute/
BIG DATA TRENDS AND TECHNOLOGIES THE WORLD OF DATA IS CHANGING Cloud WHAT IS BIG DATA? Big data are datasets that grow so large that they become awkward to work with using onhand database management tools.
White Paper IT Workload Automation: Control Big Data Management Costs with Cisco Tidal Enterprise Scheduler What You Will Learn Big data environments are pushing the performance limits of business processing
INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK A REVIEW ON HIGH PERFORMANCE DATA STORAGE ARCHITECTURE OF BIGDATA USING HDFS MS.
Evolution to Revolution: Big Data 2.0 An ENTERPRISE MANAGEMENT ASSOCIATES (EMA ) White Paper Prepared for Actian March 2014 IT & DATA MANAGEMENT RESEARCH, INDUSTRY ANALYSIS & CONSULTING Table of Contents
Special section Business intelligence Big data analytics, simplified By Joanna Schloss Intuitive content mining and analytics can transform a wealth of big data resources into timely strategic insights.
Hortonworks & SAS Analytics everywhere. Page 1 A change in focus. A shift in Advertising From mass branding A shift in Financial Services From Educated Investing A shift in Healthcare From mass treatment
CitusDB Architecture for Real-Time Big Data CitusDB Highlights Empowers real-time Big Data using PostgreSQL Scales out PostgreSQL to support up to hundreds of terabytes of data Fast parallel processing
marlabs driving digital agility WHITEPAPER Big Data and Hadoop Abstract This paper explains the significance of Hadoop, an emerging yet rapidly growing technology. The prime goal of this paper is to unveil
Microsoft SQL Server 2012 with Hadoop Debarchan Sarkar Chapter No. 1 "Introduction to Big Data and Hadoop" In this package, you will find: A Biography of the author of the book A preview chapter from the
Business Analytics Gaining the Advantage Clients Collective Intelligence is one of 100 partners Nationally Surfacing Previously Siloed Data Allow Users Direct Access to Data Streamline Business Practices
White Paper BIG DATA-AS-A-SERVICE What Big Data is about What service providers can do with Big Data What EMC can do to help EMC Solutions Group Abstract This white paper looks at what service providers
Oracle Database 12c Plug In. Switch On. Get SMART. Duncan Harvey Head of Core Technology, Oracle EMEA March 2015 Safe Harbor Statement The following is intended to outline our general product direction.
REVOLUTION ANALYTICS WHITE PAPER Advanced Big Data Analytics with R and Hadoop 'Big Data' Analytics as a Competitive Advantage Big Analytics delivers competitive advantage in two ways compared to the traditional
Abstract- Today data is increasing in volume, variety and velocity. To manage this data, we have to use databases with massively parallel software running on tens, hundreds, or more than thousands of servers.
EXECUTIVE SUMMARY Big Data is not an uncommon term in the technology industry anymore. It s of big interest to many leading IT providers and archiving companies. But what is Big Data? While many have formed
ESS event: Big Data in Official Statistics Antonino Virgillito, Istat v erbi v is 1 About me Head of Unit Web and BI Technologies, IT Directorate of Istat Project manager and technical coordinator of Web