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Data services
Data services
Data services
Data services
Data services
Data services
Data services
Data services
Data services
Data services
Data services
Data services
Data services
Data services
Data services
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Data services

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  • 1. DATA SERVICES
  • 2. Integrated Data Services  Data services are created to integrate as well as to service-enable a collection of data sources.  The underlying sources are often heterogeneous in nature.  Consumers of a data service see what appears to be one coherent, service enabled data source rather than being faced with a hodgepodge of disparate schemas and data access APIs.
  • 3. Integrated Data Services Cont..
  • 4. Integrated Data Services Cont..
  • 5. Integrated Data Services Cont..  Uses Xquery in a graphical user interface to integrate and generate result data in an optimized manner: for $cust in ics:getAllCustomers( ) where $cust/State = ’Rhode Island’ return $cust/Name    Supports read, create, update, and delete methods for operating on data instances. Also supports relationship methods to navigate from one object instance (for example, a customer) to related object instances (for example, complaints) Method’s visibility can be designated as being one of accessible by outside applications, usable only from within other data services, or private to one particular data service.
  • 6. Cloud Data Services  Cloud Data Services is a universal platform for data storage and management  Benefits of Cloud Data Services: Pay-as-you-go model  Availability  Scalability 
  • 7. Major Providers of Cloud Data Services
  • 8. Cloud Data Models    Key-value stores Sparse tables RDBMS
  • 9. Cloud Data Models contd..   Key-value stores :The simplest kind of data storage services is key-value stores that offer atomic CRUD operations for manipulating serialized data structures (objects, files) that are identifiable by a key. Examples present in market are: • • Amazon S3 Dynamo21
  • 10. Cloud Data Models contd..    Sparse tables: Storage management services for structured and semi-structured data. A sparse table is a collection of data records, each one having a row and a set of column identifiers, so that at the logical level records behave like the rows of a table. Example of Sparse Tables: o SimpleDB
  • 11. Cloud Data Models contd..   RDBMSs: Users can install an entire database system in the cloud. However, there is also a push toward providing a database management system itself as a service. In that case, administrative tasks such as installing and updating DBMS software and performing backups are delegated to the cloud service provider. Example of Sparse Tables: o Amazon RDS
  • 12. Advance Technical Issue  Data service updates and transactions    Must have transactional properties Data services that integrate data from multiple sources face additional challenges Another challenge, faced both by singlesource and multisource data services, is the mapping of updates made to the external model to correspondingly required updates to the underlying data source.
  • 13. Advance Technical Issue contd…   Data consistency in the cloud Data services security
  • 14. Emerging Trends     Query formulation tools. Data service query optimization Data summaries Cloud data service security
  • 15. Thank You

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