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Dealing with Common Data Requirements in Your Enterprise

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To view recording of this webinar please use below URL:

http://wso2.com/library/webinars/2016/11/dealing-with-common-data-requirements-in-your-enterprise/


Today’s enterprises are challenged with fast growing data requirements. Unlike in the past, where organizations relied on a single database or isolated data silos, today’s enterprises need to cope with multiple data sources and complex access control requirements. They also need to analyze large amounts of data in order to gain insights into their business functions.

This webinar will discuss how the WSO2 platform can help deal with common enterprise data requirements such as data as service transactions, aggregation of corporate entities and management of fragmented data sources to build an efficient enterprise data management strategy.

Publié dans : Technologie
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Dealing with Common Data Requirements in Your Enterprise

  1. 1. Dealing with Common Data Requirements in your Enterprise Nipun Suwandaratna Senior Solutions Engineer - WSO2 WSO2 Solution Architecture Best Practices Webinar Series - 2016
  2. 2. Agenda ● Organizational Data ● Common Data Challenges of Modern Organizations ● Integrating with Different Messaging Infrastructures ● Data Services ● Data Analytics & Visualization ● High Availability ● Q&A
  3. 3. Organizations & Data
  4. 4. Old School DB Systems Files Organization
  5. 5. Modern Data Ecosystem External Systems External Users
  6. 6. Organizational Data ● Master data Eg: Customer data, employee records, Supplier details, Product related data etc. ● Transactional data The data that master data participates in… transactions, discounts on bills etc. (changes constantly) ● Meta-data Data about data
  7. 7. Common Data Challenges Organizations Face ● Work with multiple Data Transports and Data Formats ● Data Transformation and Validation ● Exposing data as services ● Secure and managed data access ● Federated data stores ● Data/Entity Aggregation ● Data Analytics ● Visualization of Data
  8. 8. Data Transports & Formats Formats of data, their storage and transport mechanisms vary among different systems ● Transports: HTTP, HTTPS, FTPS, SFTP, TCP, UDP, WebSocket, POP, IMAP, SMTP, JMS, AMQP, MQTT ● Formats & protocols: JSON, XML, SOAP, WS-*, HTML, EDI, HL7,Text, JPEG, MP4, binary formats
  9. 9. Integration
  10. 10. Integrating with Messaging Infrastructures
  11. 11. Message Transformation ● Protocol and Format conversion and Message Translation ○ eg: SOAP to REST and XML to JSON and translate the output from one system to match the input format required by the other system ● Enrich Content ○ eg: Add or remove data fields; may require accessing a separate data source ● Wrap Content ○ eg: Include additional message header fields or encryption source to query required data ● Data Validation ○ eg: Validate input data against a schema
  12. 12. Enterprise Service Bus
  13. 13. Message Transformation Example Protocol / Content-Type Conversion
  14. 14. Data Services
  15. 15. Exposing Data-As-Services Why ? ● Decouple data from the infrastructure and the data sources and expose them through standard web services interfaces. ● Ability to incorporate multiple data sources/entities into a single data model (Data Federation)
  16. 16. Secure & Managed Data Access ● Transport and Application level security ● Authentication, authorization, confidentiality, integrity and encryption - with HTTP(S) Basic Auth, WS-Security, WS-Trust, WS-SecureConversation, WS-Policy, WS-Policy Attachment and WS-SecurityPolicy ● Authorization deals with defining who can access what ● Role based access control ● Fine-grained authorization with XACML ● Throttling access to data
  17. 17. Federated Data Stores ● Expose data from multiple data sources through a single service ● Facilitates entity aggregation
  18. 18. Data/Entity Aggregation
  19. 19. WSO2 Data Services Features Ref: http://wso2.com/products/data-services-server/
  20. 20. Analytics
  21. 21. Data Analytics ● Batch Analytics Analyze a set of data collected over a period of time. Suitable for high volumes of data. ● Real-Time Analytics Continuous processing of input data in real time. Suitable for critical systems where immediate actions is required e.g: Flight radar systems ● Interactive Analytics Obtaining fast results on indexed data by executing ad-hoc queries ● Predictive Analytics Predict future events by analyzing historical and current data
  22. 22. Big Data What is Big data ? “Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them” - Ref: Wikipedia
  23. 23. Big Data Analysis Why ? ● Make informed Business decisions - make decisions based on patterns emerging from analyzing historic data ● Improve customer experience - discover customer preferences, purchasing patterns and present the most relevant data ● Process Improvements - identify areas of the business process that needs improvement
  24. 24. Big Data Analysis Example Better customer experience in airline seat reservation/allocation img ref: http://staticcontent.transat.com/airtransat/infovoyageurs/content/EN/seating-plan-a310-300(1).png
  25. 25. Real Time Analytics ● Identify most meaningful events within an event cloud ● Analyze the impact ● Acts on them in real time
  26. 26. Real Time Analytics Example City Transport Control System - Analyzing traffic, monitor movement of busses, generate alerts based on traffic, speed & route
  27. 27. Predictive Analytics & Machine Learning Approaches: ● Machine Learning Machine learning is the science of getting computers to act without being explicitly programmed - http://online.stanford.edu/ ● Other approaches such as statistical modeling
  28. 28. Predictive Analytics Example e-Commerce sites use predictive analytics to suggest the most relevant merchandize, increasing sales opportunity
  29. 29. WSO2 Data Analytics Ref:wso2.com
  30. 30. Data Visualization
  31. 31. Data Visualization Contd. What is Data Visualization ? ● View data in a constructive and comprehensible format ● Facilitates interaction with data - drill into the data for visual analysis ● Detect patterns (e.g: sales patterns) that may go un-noticed unless data is properly visualized
  32. 32. High Availability
  33. 33. High Availability of Data
  34. 34. CONTACT US !

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