![Problems With RDBMS
• Should know the entire schema upfront
• Every record have same properties [rigid structure]](https://image.slidesharecdn.com/nosql-220126121545/85/No-SQL-3-320.jpg)
![Problems With RDBMS
• Should know the entire schema upfront
• Every record have same properties [rigid structure]
• Scalability is costly](https://image.slidesharecdn.com/nosql-220126121545/85/No-SQL-4-320.jpg)
![Problems With RDBMS
• Should know the entire schema upfront
• Every record have same properties [rigid structure]
• Scalability is costly
• Scaling relational database is hard](https://image.slidesharecdn.com/nosql-220126121545/85/No-SQL-8-320.jpg)
![Problems With RDBMS
• Should know the entire schema upfront
• Every record have same properties [rigid structure]
• Scalability is costly
• Scaling relational database is hard
• Normalization](https://image.slidesharecdn.com/nosql-220126121545/85/No-SQL-10-320.jpg)
![Problems With RDBMS
• Should know the entire schema upfront
• Every record have same properties [rigid structure]
• Scalability is costly
• Scaling relational database is hard
• Normalization
• Altering schema on a running database is expensive](https://image.slidesharecdn.com/nosql-220126121545/85/No-SQL-11-320.jpg)
![Problems With RDBMS
• Should know the entire schema upfront
• Every record have same properties [rigid structure]
• Scalability is costly
• Scaling relational database is hard
• Normalization
• Altering schema on a running database is expensive
• Horizontal scaling is a problem](https://image.slidesharecdn.com/nosql-220126121545/85/No-SQL-12-320.jpg)
No SQL
- 1. No SQL
- 2. Problems With RDBMS • Should know the entire schema upfront
- 3. Problems With RDBMS • Should know the entire schema upfront • Every record have same properties [rigid structure]
- 4. Problems With RDBMS • Should know the entire schema upfront • Every record have same properties [rigid structure] • Scalability is costly
- 8. Problems With RDBMS • Should know the entire schema upfront • Every record have same properties [rigid structure] • Scalability is costly • Scaling relational database is hard
- 10. Problems With RDBMS • Should know the entire schema upfront • Every record have same properties [rigid structure] • Scalability is costly • Scaling relational database is hard • Normalization
- 11. Problems With RDBMS • Should know the entire schema upfront • Every record have same properties [rigid structure] • Scalability is costly • Scaling relational database is hard • Normalization • Altering schema on a running database is expensive
- 12. Problems With RDBMS • Should know the entire schema upfront • Every record have same properties [rigid structure] • Scalability is costly • Scaling relational database is hard • Normalization • Altering schema on a running database is expensive • Horizontal scaling is a problem
- 14. Problems with single server architecture • Load
- 15. Problems with single server architecture • Failure – Single point of failure
- 16. Problems with single server architecture • Maintenance – Downtime during manual maintenance
- 18. Advantage of distributed database • Speed – Files are retrieved from the nearest location • If one site fails the system can still run.
- 19. What is NoSQL ? Also known as • non-relational database • non SQL • not only SQL NoSQL are non-tabular databases and store data differently than relational tables.
- 20. Student ID Name Course 123 john ML 234 Doe DL 456 Eric Python Student table
- 21. No SQL
- 22. Benefits of NoSQL • When compared to relational databases, NoSQL databases are more scalable and provide superior performance, and their data model addresses several issues that the relational model is not designed to address. • Schema-less • Data are replicated to multiple nodes(so, identical & fault tolerant) and can be partitioned: no single point of failure down nodes can easily be replaced • Cheap, easy to implement(open source) • Its all object oriented programming that is easy to use and flexible. • Running well on clusters – geographically distributed scale-out structure.
- 23. Types of NoSQL Databases • Key- value stores • Document databases • Graph databases • Column databases
- 24. Key Value Databases Key value databases, also known as key value stores, are database types where data is stored in a “key-value” format and optimized for reading and writing that data. The data is fetched by a unique key or a number of unique keys to retrieve the associated value with each key. The values can be simple data types like strings and numbers or complex objects. eg: Riak, redis server.
- 26. Document Databases • Documents are composed of field-and-value pairs and have the following structure: • Documents can contain many key value pairs, key array pairs or even nested documents • It follow the JSON format. • eg: MongoDB, Cosmos DB
- 27. Graph Databases • Graph database are NoSQL database which use the graph data model comprised of vertices and edges • Vertices which is an entity such as person, place, object or relevant piece of data. • Edges represent the relationship between two nodes • eg: neo4j, infinite graph
- 28. Column Databases • A columnar database is a database management system (DBMS) that stores data in columns instead of rows. • Very similar to relational databases, but still different. • Most known because of Google Big Table representation. • Mostly used in Data analytics. • eg: BigTable, Cassandra, Hbase.
- 29. Row Databases
- 30. Column Databases
- 31. CAP Theorem Suppose three properties of a distributed database system. • Consistency: All the replicas have same values. • Availability: Reads and writes always succeed. • Partition- tolerance System will continue to work unless there is total network failure If few of the nodes fails then the system will work & answer the query.
- 32. CAP Theorem • Eric Brewer’s CAP theorem says that if you want consistency, availability and partition tolerance, you have to settle for two out of the three. Distributed system
- 33. X=10 X=10 X=10
- 34. Then user can’t access the data until all the servers are in sync Guaranteeing Consistency X=10 X=10 X=10
- 35. Then user can’t access the data until all the servers are in sync Guaranteeing Consistency X=10 X=10 X=10 Guaranteeing Availability User will not get updated all the time.
- 36. Then user can’t access the data until all the servers are in sync Guaranteeing Consistency X=10 X=10 X=10 Guaranteeing Availability User will not get updated all the time. Guaranteeing Partition Tolerance Consistency and availability both can’t be possible
- 37. Base Property of Transation • Basically Available – failure will not halt the system • Soft state – state of the system will change over the time • Eventually consistency – system will become consistent over time
- 38. Real world Use case of NoSQL DB • User profile store All Web applications require user profiles and the ability to log in. A global user profile store is an example of where the key value characteristics of NoSQL come into play. • E-Commerce E-commerce companies use NoSQL for store huge volume of data and large amount of request from user. • Mobile Applications Since NoSQL database store the data in schema-less for the application developer can update the apps without having to do major modification in database. • Session storage NOSQL is suitable for storing such web application session information very is large in size.
- 39. NewSQL Databases • NewSQL is a new approach to relational databases that want to combine transactional ACID guarantees the goods RDBMS and the horizontal scalability of NoSQL. • They maintain ACID guarantees.
- 40. Advantages of NewSQL • It introduces new implementation to traditional relational databases. • It brings together the advantages of SQL and NoSQL. • Fragmentation is supported • Replication- copies of databases stored in remote site. • Secondary indexes – accessing database records using a value other than a primary key. • Crash recovery- recovers to a consistent state.