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Database Management Myths & Reality for the future

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Database Management Myths & Reality for the future - By A B M Moniruzzaman

Database Management Myths & Reality for the future - By A B M Moniruzzaman

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  • 1. DATABASE MANAGEMENT MYTHS & REALITY For The Future ByName: A B M Moniruzzaman, ID:121-25-238 Name: ID: Name: ID:
  • 2. High rate of data input
  • 3. Big Data ChallengesThis “Big Data” usually has oneor more of the followingcharacteristics:Very Large Data Volumes– Measured in terabytes orpetabytesVariety – Structured andUnstructured DataHigh Velocity – RapidlyChanging Data
  • 4. Abstract.The challenge of building consistent, available, andscalable data management systems capable of servingpetabytes of data for millions of users as large internetenterprises.Though scalable data management has been a vision formore than three decades and much research has focussedon large scale data management in traditional enterprisesetting, cloud computing brings its own set of novelchallenges that must be addressed to ensure the successof data management solutions in the cloud environmentin future.
  • 5. Scalable Data Management• Scalable database management systems (DBMS) —both• For update intensive application workloads as well as decision support systems• For descriptive and deep analytics—are a critical part of the cloud infrastructure• and play an important role in ensuring the smooth transition of applications from the traditional enterprise infrastructures to next generation cloud infrastructures.
  • 6. Different class of scalable data managementGoogle’sBigtable [5], PNUTS [6] fromYahoo!, Amazon’s Dynamo [7]and other similar butundocumented systems. All ofthese systems deal withpetabytes of data, serve onlinerequests with stringent latencyand availability requirements,accommodate erraticworkloads, and run on clustercomputing architectures; stakingclaims to the territoriesused to be occupied by databasesystems.
  • 7. Cloud Computing
  • 8. Cloud Computing• Cloud computing is an extremely successful paradigm of service oriented computing, and has revolutionized the way computing infrastructure is abstracted and used. Three most popular cloud paradigms include:• Infrastructure as a Service (IaaS),• Platform as a Service (PaaS), and• Software as a Service (SaaS).• The concept however can also be extended to Database as a Service or Storage as a Service.
  • 9. Cloud Computing Services
  • 10. Cloud Database
  • 11. Cloud Database System• a system in the cloud must posses some features to be able to effectively utilize the cloud economies. These cloud features include:• scalability• elasticity• fault-tolerance• self-manageability• ability to run on commodity hardware. Most traditional relational database systems were designed for enterprise infrastructures and hence were not designed to meet all these goals. This calls for novel data management systems for cloud infrastructures.
  • 12. Scalable database system to supports large application withlots of data and supporting hundreds of thousands of clients
  • 13. Large data analysis tools• Hadoop• MapReduce Open source tools like Hadoop and MapReduce provide tremendous data processing power to big data.
  • 14. How Hadoop Map/Reduce works
  • 15. Large multitenant system• Another important domain for data management in the cloud is the need to support large number of applications. This is referred to as a large multitenant system.
  • 16. Multi-tenant Cloud Storage
  • 17. Data Management for Large Applications
  • 18. REFERENCES•• [1] An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads• http://www.cs.uwaterloo.ca/~kmsalem/courses/CS848W10/presentations/Chalamalla-HadoopDB.pdf•• [2] Divyakant Agrawal Sudipto Das Amr El Abbadi• Department of Computer Science• University of California, Santa Barbara• Santa Barbara, CA 93106-5110, USA• http://www.edbt.org/Proceedings/2011-Uppsala/papers/edbt/a50-agrawal.pdf•• [3] Big Data Challenge: Future is Right Here!• http://www.e-zest.net/blog/big-data-challenge-future-is-right-here/•• [4] Hadoop and Big Data challenge• http://www.apexcloud.com/blog/category/big-data•• [5] Big data and cloud computing: New wine or just new bottles?• http://www.vldb2010.org/proceedings/files/papers/T02.pdf•• [6] C. Curino, E. Jones, Y. Zhang, E. Wu, and S. Madden. Relational• Cloud: The Case for a Database Service. Technical Report 2010-14,• CSAIL, MIT, 2010. http://hdl.handle.net/1721.1/52606.•• [7] D. Agrawal, A. El Abbadi, S. Antony, and S. Das. Data Management• Challenges in Cloud Computing Infrastructures.• http://www.just.edu.jo/~amerb/teaching/2-11-12/cs728/paper1.pdf•• [8] Bigtable: A Distributed Storage System for Structured Data.• http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en//archive/bigtable-osdi06.pdf•• [9] J. Cohen, B. Dolan, M. Dunlap, J. M. Hellerstein, and C. Welton.• Mad skills: New analysis practices for big data. PVLDB,• http://db.cs.berkeley.edu/papers/vldb09-madskills.pdf•• [10] Divyakant Agrawal Sudipto Das Amr El Abbadi• Department of Computer Science• University of California, Santa Barbara• Santa Barbara, CA 93106-5110, USA• {agrawal, sudipto, amr}@cs.ucsb.edu
  • 19. REFERENCES• [11] Scalable Data Management in the Cloud: Research Challenges• http://cse.vnit.ac.in/comad2010/pdf/Keynotes/Key%20Note%202.pdf••• [12] C. Curino, E. Jones, Y. Zhang, E. Wu, and S. Madden. Relational• Cloud: The Case for a Database Service. Technical Report 2010-14,• CSAIL, MIT, 2010. http://hdl.handle.net/1721.1/52606.•• [13] S. Das, D. Agrawal, and A. El Abbadi. ElasTraS: An Elastic• Transactional Data Store in the Cloud. In USENIX HotCloud, 2009.• http://www.cs.ucsb.edu/~sudipto/diss/Dissertation_Single.pdf•• [14] S. Das, D. Agrawal, and A. El Abbadi. G-Store: A Scalable Data• Store for Transactional Multi key Access in the Cloud. In ACM• SOCC, 2010.•• [15] D. Jacobs and S. Aulbach. Ruminations on multi-tenant databases. In• BTW, pages 514–521, 2007.• http://static.usenix.org/event/osdi04/tech/full_papers/dean/dean.pdf•• [16] T. Kraska, M. Hentschel, G. Alonso, and D. Kossmann. Consistency• Rationing in the Cloud: Pay only when it matters. PVLDB,• http://www.vldb.org/pvldb/2/vldb09-759.pdf•• [17] C. D. Weissman and S. Bobrowski. The design of the force.com• multitenant internet application development platform. In SIGMOD,• http://cloud.pubs.dbs.uni-leipzig.de/sites/cloud.pubs.dbs.uni-leipzig.de/files/p889-weissman-1.pdf•• [18] F. Yang, J. Shanmugasundaram, and R. Yerneni. A scalable data• platform for a large number of small applications. In CIDR, 2009.• https://database.cs.wisc.edu/cidr/cidr2009/Paper_17.pdf•• [19] S. Das, S. Agarwal, D. Agrawal, and A. El Abbadi. ElasTraS: An• Elastic, Scalable, and Self Managing Transactional Database for the• Cloud. Technical Report 2010-04, CS, UCSB, 2010.•• [20] S. Das, S. Nishimura, D. Agrawal, and A. El Abbadi. Live Database• Migration for Elasticity in a Multitenant Database for Cloud• Platforms. Technical Report 2010-09, CS, UCSB, 2010.•

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