Scaling Database Modernisation with MongoDB - Infosys

Scaling Database Modernisation with MongoDB - Infosys

• 1.
  MongoDB An open sourcealternative to proprietary databases
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  Traditional databases dominatedthe world for more than 30 years… 2  ATOMIC  CONSISTENT  ISOLATION  DURABILITY
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  …but in recenttimes business expectations from data have changed drastically… 3 Nightly batch Real-time VS Linear Scale Exponential Scale VS Structured Polymorphic VS
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  Traditional databases areunable to meet the new age needs of our customers. 4 Top 3 pain points with traditional Relational Databases CostScalability Agility
• 5.
  5 How do wecater to these needs without compromising on what RDBMS is best at
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  The databases ofthe modern age cater to these needs Features of Modern Databases Schema-free and unstructured data formats Flexibility to accommodate changes and various data types Horizontal scaling on commodity servers Low cost Low Complexity Consistent Multi platform experience Avoids platform lock-in Aligns to Next Gen Architecture Denormalized data Higher speed of retrieval Higher performance Built in Replication, High Availability and Automated Failover No Add-on's Low Complexity Open Source Low License & Storage Cost Lower Cost 6
• 7.
  …but the choicesare many Key-Value Databases Caching Data User Session and Preferences Shopping Cart Data Graph Databases Social and other networks Real time Routing Fraud Detection Document Databases Agile / Web App Product Catalog Transactional systems Columnar Databases Large Data Set /Big Data IoT/ Sensor data New Age Applications 7
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  MongoDB stands outas one of the best alternatives. 8 Familiar relational syntax | Ease to add to any application | Multiple documents in 1 or many collections Similarity to relational transactions Multi Document ACID guarantee with Mongo 4.0 − Snapshot isolation, − All or nothing execution to maintain consistency − No performance impact for non-transactional operations Expressive Query Language Strong Consistency Secondary Indexes Flexibility Scalability Cost
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  …its agile andflexible architecture helps accelerate speed to value 9 Speed & Flexibility to Develop Speed to Production Speed to Insight
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  However it isimportant to understand and mitigate the migration challenges…
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  • Stakeholder alignment •Application knowledge with migration team • Lack of skills on new technologies There are key challenges to such migrations… PROCESS PEOPLE TECHNOLOGY • A radical change on how you look at data models • Limitation of automated tools for migration • Maintain equal or better performance metrics • Long downtime for applications • Inaccuracies in the migration impact assessments • Lack of documented application knowledge …and above all, it is extremely difficult to come out of complex contracts
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  Infosys’s robust migrationmethodology addresses these challenges 12 02 Benefit Realization Model 05 Tools and Accelerators 03 Impact Analysis 04 Migration Roadmap 01 Risk Management 06 Validation Framework DATA MODERNIZATION FRAMEWORK Use case Suitability Analysis Applications profiling Risk Identification and mitigation strategy Evaluate the current cost of ownership Estimate the realization and proposed cost to organization Benefit Realization Report Analysis on the impacts of changes within the applications and supporting apps Validation framework to evaluate the success of data modernization and app remediation iCIA – Infosys Code Impact Analyzer - for application remediation NoSQL modeler – Recommends the data model iDSS - Infosys Data Service Suite – for Data Migration iDTW - Infosys Data Testing Workbench – for test & validation TCO Calculator – To evaluate the benefit realization model Infosys Data Migration validator for MongoDB – Validation of schema and data migrated Create a migration plan and roadmap evaluating the risk and benefit
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  …and automated throughtools and accelerators 13 Analysis Tools Infosys NoSQL Modeler Migration Tools iDSS - Infosys Data Service Suite Testing Tools iDTW - Infosys Data Testing Workbench Project Management Accelerators Codified Project and Risk Management Frameworks 20-30% savings On data model design 60-70% savings On data migration 30-40% savings On testing 30-40% savings On project management
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  Infosys NoSQL Modeler– overview 14 EXTRACTION ANALYSIS PERSISTENCE PROCESSING DEPLOYMENT Source RDBMS Entity Design Query Pattern Entity Cardinality Entity and Relationship Read and Write Query Entity Change frequency and Cardinality Rules Drools SQL Lite Target Data-Model Generation Deployment Scripts Target NoSQL Process Rules
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  16 Step 1 –RDBMS TO MONGO Step 2 – MONGO TO MONGO(Aggregation) Infosys Data Services Suite (iDSS) overview
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  We have donethis for many of our clients… 18 A fortune 100 technology conglomerate Order Orchestration Platform for a leading CSP Leading telecommunication service provider • Built a single consolidated view of customers, devices and contracts • Real time view of customer entitlements across all categories • Increased end customer loyalty by providing an a unified, real time view of all their entitlements • Supports 15+ million transactions per day • Enables richer data insights on customer behavior • Oracle licensing and support had become expensive • Infosys Architect team performed the product evaluation and comparison for the Oracle replacement. MongoDB was selected as target data store • Enhanced Order orchestration platform that can support extreme order volume during events like new Apple IPhone launch where order volume is 50x. • Reduce client dependency on Oracle licensing. Move towards sunset of Oracle DB over next couple of years. • Client has the requirement to store call data records which can be easily retrieved to be presented to police and justice department for judicial investigation • Current data model was rigid and enhancements require frequently changing the database schema • Developed and implemented a solution to migrate more than 1TB of data from oracle to Mongodb using Kafka
• 19.
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