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SUM TWO is making 'serious investments' in big data, cloud, mobility !!! “Big data refers to the datasets whose size is beyond the ability of atypical database software tools to capture ,store, ...

SUM TWO is making 'serious investments' in big data, cloud, mobility !!! “Big data refers to the datasets whose size is beyond the ability of atypical database software tools to capture ,store, manage and analyze.defines big data the following way: “Big data is data that exceeds theprocessing capacity of conventional database systems. The data is too big, moves toofast, or doesnt fit the strictures of your database architectures. The 3 Vs of Big data.Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.Hadoop’s cost advantages over legacy systems redefine the economics of data. Legacy systems, while fine for certain workloads, simply were not engineered with the needs of Big Data in mind and are far too expensive to be used for general purpose with today's largest data sets.One of the cost advantages of Hadoop is that because it relies in an internally redundant data structure and is deployed on industry standard servers rather than expensive specialized data storage systems, you can afford to store data not previously viable . And we all know that once data is on tape, it’s essentially the same as if it had been deleted - accessible only in extreme circumstances.Make Big Data the Lifeblood of Your Enterprise
With data growing so rapidly and the rise of unstructured data accounting for 90% of the data today, the time has come for enterprises to re-evaluate their approach to data storage, management and analytics. Legacy systems will remain necessary for specific high-value, low-volume workloads, and compliment the use of Hadoop-optimizing the data management structure in your organization by putting the right Big Data workloads in the right systems. The cost-effectiveness, scalability and streamlined architectures of Hadoop will make the technology more and more attractive. In fact, the need for Hadoop is no longer a question.

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Bigdata Presentation Transcript

  • 1. BIGDATA Decisions! Delivered !! SARAVANAN . M SALES MANAGER 21ST NOV 2013 Index: 1. What is BIGDATA? 2. BIGDATA Analytics 3. Characteristics of BIGDATA 4. Attributes of BIGDATA 5. Examples of BIGDATA 6. Size of BIGDATA 7. BIGDATA landscape 8. Industries using BIGDATA 9. Technologies Used 10. HADOOP 11. When should we go for HADOOP 12. Advantages of BIGDATA 13. Risks of BIGDATA
  • 2. WHAT IS BIGDATA ?  Bigdata is a term that describes large volumes of high velocity, complex and variable data that require advance techniques and technologies to enable the capture, storage, distribution, management and the analysis of information.  Bigdata is a data that exceeds the processing capacity of conventional database systems.  The data is too big , moves too fast, or doesn’t fit the structures of your database architecture.  To gain value from this data, you must choose an alternative way to process it.
  • 3. BIGDATA ANALYTICS :  Bigdata analytic is the process of examining and interrogating big data assets to derive insights of value for decision making.
  • 4. CHARACTERISTICS OF BIGDATA  The word ―big‖ in bigdata is not just about the volume. Its also about the 3v`s.  They are;  Volume  Velocity  Variety.
  • 5. ATTRIBUTES OF BIGDATA :  Volume – is that huge amount of digital data created by all sources – companies, individuals and devices. (What constitutes ―big‖ varies by perspective and will certainly change over time.)  Velocity – is the speed of creation, which in turn drives interest in real-time analytics and automated decision-making.  Variety - comes from increasing types of data – some structured, as in databases, much of it unstructured text or video and some semi-structured data like social media data, location-based data, and log-file data.
  • 6. EXAMPLES OF BIGDATA  Sensor networks  Social networks  Internet search index  Astronomy  Internet text and documents  Large scale e-commerce  Weblogs and video archives  Medical records and call detail records ,etc.
  • 7. SIZE OF BIGDATA?  Google : 24PB data processed daily.  Facebook: 750 million users 12TB daily content 2.7 billion ―likes‖ and ―comments‖.  Twitter: 340 million daily tweets 1.6 billion search queries 7TB added daily.
  • 8. BIGDATA LANDSCAPE:
  • 9. INDUSTRIES THAT ARE USING BIGDATA:  Banking  Risk & Fraudulent management  Customer Analytics  Telecommunications  Call detail record processing  Customer profile  Health care  Medical Record text analytics  Genomic Analysis  Digital Media  Real-time ad targeting  Website analysis  Government  Abuse & Fraudulent management  Customer Analytics
  • 10. TECHNOLOGY:  Bigdata is Driven mainly by Open Source Initiatives such as :  Apache TM HADOOP Project  Apache TM CASSANDRA Project  Apache TM HBASE Project  Apache TM HIVE Project  Apache TM SOLR Project
  • 11. HADOOP :  What is Hadoop?  Flexible infrastructure for large scale computation and data processing on a network of commodity hardware.  Hadoop is completely written using JAVA.  Hadoop is an open source and it is distributed under Apache license,  Hadoop is not :  a file system nor a database.  Not a replacement for exciting data warehouse systems nor for all programing logics.  Not an On Line Transaction Processing (OLTP) system.
  • 12. WHEN SHOULD WE GO FOR HADOOP?  When the data is too huge  When the processes are independent  For online analytical processing (OLAP)  For a better scalability  For Unstructured data  Also for Parallelism
  • 13. --BACK TO BIGDATA— ADVANTAGES:  Largest and fast growing market  Leaveraging bigdata for insights can enhance productivity and competitiveness for companies  Harnessing bigdata will enable business to improve market intelligence  Latest trend for IT Professionals in the area of data analytics
  • 14. RISKS OF BIGDATA:  Will b so overwhelmed  Need the right people and solve the right problem  Technological considerations  Open source  Scalability and performance issue  Many source of bigdata is privacy  Self regulation  Legal regulation
  • 15. THANKYOU ! Decisions! Delivered !!