Preparing Your Data For Cloud

                       Narinder Kumar
                    Inphina Technologies




        ...
Agenda
   Relational DBMS's : Pros & Cons
   Non-Relational DBMS's : Pros & Cons
   Types of Non-Relational DBMS's
   ...
Relational DBMS - Pros
   Data Integrity
   ACID Capabilities
   High Level Query Model
   Data Normalization
   Data...
Relational DBMS - Cons
   Scaling Issues
       By Duplication (Master-Slave)
       By Sharding/Division (Not transpar...
Non-Relational DBMS - Pros
   Scalability

   Replication / Availability

   Performance

   Deployment Flexibility

...
Non-Relational DBMS - Cons
   Lack of Transactional Support
   Data Integrity is Application's responsibility
   Data D...
RDBMS's and Cloud




                    7
Cloud Capable RDBMS




       s




Almost every RDBMS can run in a IAAS Cloud Platform

                                ...
Cloud Native RDBMS




                     9
Types of Non-Relational DBMS

   Key Value Stores

   Document Stores

   Column Stores

   Graph Stores


           ...
Key Value Data-Bases
   Object is completely Opaque to DB
   Mostly GET, PUT & DELETE operations are
    supported
   T...
Key Value DataBases & Cloud

                Project Voldemort




MemCachedDB       Tokyo Tyrant




                    ...
Document Data-Bases
   Object is not completely opaque to DB
   Every Object has it's own schema
     FirstName="Bob", A...
Document Data-Bases & Cloud




                              14
Column-Store Data-Bases
   Richer than Document Stores
   Multi-Dimensional Map
       Tables
           Row
        ...
Column-Store Data-Bases & Cloud




                              16
Key Factors while Making a Choice
   Application Architecture Requirements
   Platform choices
   Non-Functional Requir...
Different Requirements = Different Solutions




                                          18
Scenario 1
   Feature First
       Corporate Data
       Consistency Requirements
       Business Intelligence
      ...
Scenario 2
   Consumer Facing Application
       Big Files (Images, BLOB's, Files)
       Geographically Distributed
  ...
Scenario 3
   Hundreds Of Government Documents with
    different schemas
       Need to serve on Web
       Data Minin...
Scenario 4
   Scale First
       Huge Data-Set
       Analytical Requirements
       Consumer Facing
       High Avai...
Mix & Match of Earlier Scenarios
                     Polyglot Persistence
                         RDBMS for low-volume...
CAP Theorem




              24
Conclusions
   One Size does not Fit all

   Many choices

   No-SQL DB's providing Alternatives

   RDBMS serve usefu...
nkumar@inphina.com

      www.inphina.com
http://thoughts.inphina.com


                              26
References
   http://nosql-database.org/

   http://www.drdobbs.com/database/218900502

   http://perspectives.mvdirona...
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Preparing your data for the cloud

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More and more Enterprises are moving their IT infrastructure to Cloud platforms. Out of the entire components, Data Storage still remains a tricky part of the puzzle. I would like to present an overview of the choices, their advantages and limitations, we as Software Developers have currently. Based upon the choices, we may need to think about the design and architecture of the data-manipulation components of the application, we plan to put on Cloud. Following is an overview of the proposed agenda:

Existing “Cloud Capable” and “Cloud Native” Relational DBMS
Existing “Cloud Capable” and “Cloud Native” Non-Relational DBMS
Main differences between Relational and Non-Relational DBMS’s
Advantages and Limitations of Relational DBMS on Cloud Platforms
Advantages and Limitations of Non-Relational DBMS on Cloud Platforms
Design Patterns while using Non-Relational DBMS in the application
Code Walk-through showing Integration of “Cloud Capable” and “Cloud Native” Non-Relational DBMS with a Web-Application

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Preparing your data for the cloud

  1. 1. Preparing Your Data For Cloud Narinder Kumar Inphina Technologies 1
  2. 2. Agenda  Relational DBMS's : Pros & Cons  Non-Relational DBMS's : Pros & Cons  Types of Non-Relational DBMS's  Current Market State  Applicability of Different Data-Bases in different environments 2
  3. 3. Relational DBMS - Pros  Data Integrity  ACID Capabilities  High Level Query Model  Data Normalization  Data Independence 3
  4. 4. Relational DBMS - Cons  Scaling Issues  By Duplication (Master-Slave)  By Sharding/Division (Not transparent)  Fixed Schema  Mostly disk-oriented (Performance)  May fair poorly with large data 4
  5. 5. Non-Relational DBMS - Pros  Scalability  Replication / Availability  Performance  Deployment Flexibility  Modelling Flexibility  Faster Development (?) 5
  6. 6. Non-Relational DBMS - Cons  Lack of Transactional Support  Data Integrity is Application's responsibility  Data Duplication / Application Dependent  Eventually Consistent (mostly)  No Standardization  New Technology 6
  7. 7. RDBMS's and Cloud 7
  8. 8. Cloud Capable RDBMS s Almost every RDBMS can run in a IAAS Cloud Platform 8
  9. 9. Cloud Native RDBMS 9
  10. 10. Types of Non-Relational DBMS  Key Value Stores  Document Stores  Column Stores  Graph Stores 10
  11. 11. Key Value Data-Bases  Object is completely Opaque to DB  Mostly GET, PUT & DELETE operations are supported  There may be limits on size of Objects Inspired by Amazon Dynamo Paper 11
  12. 12. Key Value DataBases & Cloud Project Voldemort MemCachedDB Tokyo Tyrant 12
  13. 13. Document Data-Bases  Object is not completely opaque to DB  Every Object has it's own schema FirstName="Bob", Address="5 Oak St.", Hobby="sailing". FirstName="Jonathan", Children=("Michael,10", "Jennifer,8")  Can perform queries based on Object's attributes  Possible to describe relationships between Objects  Joins and Transactions are not supported  Good for XML or JSON objects 13
  14. 14. Document Data-Bases & Cloud 14
  15. 15. Column-Store Data-Bases  Richer than Document Stores  Multi-Dimensional Map  Tables  Row  Column  Time-Stamp  Supports Multiple Data Types  Usually use an Underlying DFS Inspired by Google Big Table Paper 15
  16. 16. Column-Store Data-Bases & Cloud 16
  17. 17. Key Factors while Making a Choice  Application Architecture Requirements  Platform choices  Non-Functional Requirements  Consistency  Availability  Partition  Security  Data Redemption 17
  18. 18. Different Requirements = Different Solutions 18
  19. 19. Scenario 1  Feature First  Corporate Data  Consistency Requirements  Business Intelligence  Legacy Application RDBMS on Amazon Cloud, RackSpace (IaaS) or Microsoft Azure/Amazon RDS (PaaS) 19
  20. 20. Scenario 2  Consumer Facing Application  Big Files (Images, BLOB's, Files)  Geographically Distributed  Mostly writes  Not heavy requirement on Rich Queries Key-Value Data Stores (Amazon S3, Project Voldemort, Redis) 20
  21. 21. Scenario 3  Hundreds Of Government Documents with different schemas  Need to serve on Web  Data Mining Document Data-Stores (Amazon SimpleDB, Apache Couche DB, MongoDB) 21
  22. 22. Scenario 4  Scale First  Huge Data-Set  Analytical Requirements  Consumer Facing  High Availability over Consistency Column Data-Stores (Google App Engine, Hbase, Cassandra) 22
  23. 23. Mix & Match of Earlier Scenarios  Polyglot Persistence  RDBMS for low-volume and high value  Key-Value DB for large files with little queries  Memcached DB for short- lived Data  Column DB for Analytics 23
  24. 24. CAP Theorem 24
  25. 25. Conclusions  One Size does not Fit all  Many choices  No-SQL DB's providing Alternatives  RDBMS serve useful purpose 25
  26. 26. nkumar@inphina.com www.inphina.com http://thoughts.inphina.com 26
  27. 27. References  http://nosql-database.org/  http://www.drdobbs.com/database/218900502  http://perspectives.mvdirona.com/2009/11/03/OneSizeDoesNotFitAll.aspx  http://blog.nahurst.com/visual-guide-to-nosql-systems  http://blog.heroku.com/archives/2010/7/20/nosql/  http://www.vineetgupta.com/2010/01/nosql-databases-part-1-landscape.html  http://project-voldemort.com/  http://code.google.com/p/redis/  http://memcachedb.org/  http://aws.amazon.com/simpledb/  http://couchdb.apache.org/  http://www.mongodb.org  http://riak.basho.com/ 27
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