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ATP (Autonomous Transaction Processing) Database Partha Pratim Mahanta Solution Engineering Hub

ATP (Autonomous Transaction Processing) Database Partha Pratim Mahanta Solution Engineering Hub. Agenda. General Introduction to Autonomous Data Trasaction Processing ( ATP ) ATP Business use case Data Loading to ATP Configure OCI-CLI. Both Services use Oracle Database 18c.

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ATP (Autonomous Transaction Processing) Database Partha Pratim Mahanta Solution Engineering Hub

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  1. ATP (Autonomous Transaction Processing) Database Partha Pratim MahantaSolution Engineering Hub Oracle Confidential – Internal

  2. Confidential – Oracle Internal/Restricted/Highly Restricted

  3. Agenda • General Introduction to Autonomous Data Trasaction Processing ( ATP ) • ATP Business use case • Data Loading to ATP • Configure OCI-CLI

  4. Both Services use Oracle Database 18c • ATP has been designed to execute a high volume of transactions – OLTP • The primary goal of ADW is to achieve fast complex Analytics

  5. Autonomous Transaction Processing is a fully managed database tuned and optimized for transaction processing or mixed workloads with the market-­leading performance of Oracle Database. Higher Availability ( 99.95% uptime guarantee) Lower Cost & Increased Productivity Lower risk

  6. Understand your customer • Data Sets • Net-banking data • Credit card transactions • International spends • Holidaying schedules and choice of destination • Customer support interaction - feedback , complaints (Optional ) • Key Benefits • Getting deep insight into customer expenses and making precise customer expense prediction • Build data on targeted customers and understand their frequent needs. • Establish relationship between customers and retailers • Retaining the highly potential but annoyed customers based on complaints and feedbacks ( Optional ) • Offers - bringing a mutual gain to both investor and the financial institution

  7. Revenue Performance Analysis Discover business insight about drop in revenue for the 4G plan even though the number of transactions for the plan type remain high, determine course of action to fix this issue (alert customers about card expiry). Additionally, find causes for a potential revenue leakage and fraud. • Key Benefits • Top Management will have clear view on revenue trends • Corrective actions from fraud analysis • Joining various data sets enable you to generate rich insights and help determine actions to take for course corrections • Enable top management to get business information near real time

  8. Data Loading to ATP

  9. Case 1 Data Loading via DBMS_CLOUD

  10. ATP DB Provision

  11. Connectivity with SQL Developer

  12. Object Storage

  13. Case 2 Data Loading via DATA PUMP

  14. Table “employee” with 7 million data at source

  15. Export dump from the Source

  16. Upload the Dump file “employee.dmp” to the target ( ADW ) “Object Storage”

  17. “Employee” table not available in ADWC

  18. Configure the wallet and test the connection to ADW with both Consumer group “High” and “Low”

  19. Execute the Data Pump Credential

  20. Crete the “employee” table in ADW

  21. Table “Employee” import of total 7 million data to ADW

  22. Case 3 Data Loading via ODI

  23. File Uploaded to DIPC VM

  24. ODI Mapping To Extract And Load Data

  25. ODI Run Time Statistics for 9 million data

  26. Test Case 4 Data Loading via SQL Developer

  27. Import Data Using SQL Developer

  28. Troubleshooting data loads

  29. Troubleshooting data loads

  30. Connecting to various Consumer Group via SQL Plus

  31. Configure OCI CLI

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