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Prepare Your Data For The Cloud
 

Prepare Your Data For The Cloud

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Session Presented @IndicThreads Cloud Computing Conference, Pune, India ( http://u10.indicthreads.com ) ...

Session Presented @IndicThreads Cloud Computing Conference, Pune, India ( http://u10.indicthreads.com )
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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

Takeaways from the session

* Overview of current Market Situation w.rt. Data Storage on Cloud
* Helpful Pointers towards making the right choice of Data Storage platform
* How Non-Relational DBMS’s can be integrated into our applications

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    Prepare Your Data For The Cloud Prepare Your Data For The Cloud Presentation Transcript

    • Preparing Your Data For Cloud Narinder Kumar Inphina Technologies 1
    • 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
    • Relational DBMS - Pros  Data Integrity  ACID Capabilities  High Level Query Model  Data Normalization  Data Independence 3
    • 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
    • Non-Relational DBMS - Pros  Scalability  Replication / Availability  Performance  Deployment Flexibility  Modelling Flexibility  Faster Development (?) 5
    • 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
    • RDBMS's and Cloud 7
    • Cloud Capable RDBMS s Almost every RDBMS can run in a IAAS Cloud Platform 8
    • Cloud Native RDBMS 9
    • Types of Non-Relational DBMS  Key Value Stores  Document Stores  Column Stores  Graph Stores 10
    • 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
    • Key Value DataBases & Cloud Project Voldemort MemCachedDB Tokyo Tyrant 12
    • 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
    • Document Data-Bases & Cloud 14
    • 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
    • Column-Store Data-Bases & Cloud 16
    • Key Factors while Making a Choice  Application Architecture Requirements  Platform choices  Non-Functional Requirements  Consistency  Availability  Partition  Security  Data Redemption 17
    • Different Requirements = Different Solutions 18
    • 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
    • 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
    • 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
    • Scenario 4  Scale First  Huge Data-Set  Analytical Requirements  Consumer Facing  High Availability over Consistency Column Data-Stores (Google App Engine, Hbase, Cassandra) 22
    • 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
    • CAP Theorem 24
    • Conclusions  One Size does not Fit all  Many choices  No-SQL DB's providing Alternatives  RDBMS serve useful purpose 25
    • 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.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