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Danny Chan for MongoDB Builders

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🌐 Get started: What is MongoDB Operational Data Layer? (Part 1)

✨ Operational Data Store (Layer):

  • Same as Data Fabric, Operational Data Hub
  • Layer between existing data sources and consumers
  • Don't need to replace legacy systems
  • Combine data from multiple systems into a single hub


πŸ” Collect data sets from different systems

  • Provide a complete picture of data


πŸ”₯ Ad-hoc analytical tools

  • Real-time analytics
  • Up-to-the-minute view of the whole business
  • Without interfering with operational workloads


πŸ’‘ Example:

  • Day-to-day responsibilities
  • Required Service Level Agreements (SLAs)


πŸ—οΈ Foundation for re-architecting

  • Iterative approach to digital transformation
  • Parallel to legacy and new systems
  • Legacy system continue to work without interruption


πŸš€ Legacy Modernization:

  • Build new business functions faster
  • Scale to millions of users
  • Data consumers access Operational Data Layer (ODL)


πŸ’Ž Benefit:

  • Access entire data set
  • Customer single view
  • Artificial intelligence processes


πŸ’» Data as a Service:

  • Operational Data Layer gathers all important data in one place
  • Applications and analytics get the full picture of enterprise data


☁️ Cloud of Operational Data Layer:

  • Deployed on the same cloud provider
  • Same regions as its consuming systems
  • Gradual, non-disruptive approach to cloud migration


πŸ” Challenge:

  • 70 different systems in 15 different screens


πŸ”‘ Solution:

  • Single view
  • Use only one screen to access all the information
  • Real-time representation
  • Customer 360
  • Single views of products, financial assets, entities relevant for business
  • Data from multiple sources


πŸš€ Faster customer call times

  • Analyze customer data for cross-sell and upsell opportunities


πŸ“‹ Use Case:

  • Customer service representatives
  • Fraud and risk systems
  • Sales and marketing staff
  • Quantitative analysts
  • User online account


πŸ’ͺ Mainframe Offload:

  • Single point of failure
  • Taken offline for maintenance
  • Easier to serve mainframe data to new digital channels without straining legacy systems


πŸ€” Challenge:

  • Requires a complete view of enterprise data
  • Warehouse or a Hadoop-based analytics
  • Can't meet today's demand for real-time analytics
  • Loaded in daily or weekly batches
  • Long-running queries taking hours


πŸ” Solution:

  • For real-time decisions
  • Up-to-the-minute state of data
  • Low latency analytics queries
  • Ad-hoc questions
  • Example:
    • Recommendations for customers
    • Personalizing content based on user info
    • Machine learning on enterprise data to extract new insights
    • Improve operational efficiency



πŸš€ Modernization:

  • Exposing existing data to new applications
  • Don't have potential impact to legacy systems



πŸ’Ό Data Steward:

  • Get data from application database then store to operational data layer
  • Use ETL (Extract, transform, load), CDC (change data capture)
  • Data must fulfill requirements
  • Frequency of data transfer
  • Without affecting current producing & consuming applications


πŸ€– Reference:

https://www.mongodb.com/resources/basics/implementing-an-operational-data-layer
Implementing an Operational Data Layer

https://www.mongodb.com/resources/solutions/use-cases/mainframe-modernization-reference-architecture
Mainframe Modernization Reference Architecture


Editor

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Danny Chan, specialty of FSI and Serverless

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Kenny Chan, specialty of FSI and Machine Learning

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