Difference Between Relational Database Management System and Not Only SQL

1. RDBMS Vs. NoSQL
 SQL Database
 SQL Standard
 SQL Characterstics
 SQL Database Example
 NoSQL Database
 NoSQL Database Definition
 General Characterstics
 NoSQL Database Example
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2. What is RDBMS
 RDBMS stands for Relational Database Management System.
 Relational Database Management System stores data in the form of rows and columns
 RDBMS is the basis for SQL, and for all modern database systems like MS SQL Server,
IBM DB2, Oracle, MySQL, and Microsoft Access.
 A relational database has following major components:
 Table, Record / Tuple, Field & Column /Attribute.
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3. SQl Charachteristics
 Data stored in columns and tables
 Relationships represented by data
 Data Manipulation Language
 Data Definition Language
 Transactions
 Abstraction from physical layer
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4. Transactions – ACID Properties
 Atomic – All of the work in a transaction completes (commit) or none of it completes
 Consistent – A transaction transforms the database from one consistent state to another
consistent State. Consistency is defined in terms of constraints.
 Isolated – The results of any changes made during a transaction are not visible until the
transaction has committed.
 Durable – The results of a committed transaction survive failures
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5. Example of SQL /RDBMS
 Oracle: An object-relational database management system (DBMS) that is written in the C++ language.
 IBM DB2: A family of database server products from IBM.
 Sybase: A relational database server product for businesses that is primarily used on the Unix operating
system.
 MS SQL Server: An RDBMS for enterprise-level databases that supports both SQL and NoSQL
architectures. MS SQL Server was developed by Microsoft.
 Maria DB: An enhanced, drop-in version of MySQL.
 PostgreSQL: An enterprise-level, object-relational DBMS that uses procedural languages such as Perl and
Python in addition to SQL-level code.
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6. Limitation of SQL
 Scalability: Users have to scale relational database on powerful servers that are expensive
and difficult to handle. To scale relational database it has to be distributed on to multiple
servers. Handling tables across different servers is a chaos.
 Complexity: In SQL server’s data has to fit into tables anyhow. If your data doesn’t fit
into tables, then you need to design your database structure that will be complex and again
difficult to handle.
 RDBMS is a great tool for solving ACID problems when data validity is crucial, when you
need to support dynamic queries.
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7. NoSQL Introduction
 No SQL stands for Not only Sql.
 Next Generation Databases mostly addressing some of the points: being non-relational, distributed,
opensource and horizontal scalable.
 The original intention has been modern web-scale databases.
 The movement began early 2009 and is growing rapidly. Often more characteristics apply as: schema-
free,easy replication support, simple API, eventually consistent / BASE (not ACID), a huge data
amount, and more
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8. NoSQL Database
 MongoDB: The most popular open-source NoSQL system. MongoDB is a document-oriented
database that stores JSON-like documents in dynamic schemas. Craigslist, eBay, and Foursquare
use MongoDB.
 CouchDB: An open source, web-oriented database developed by Apache. CouchDB uses the
JSON data exchange format to store its documents; JavaScript for indexing, combining, and
transforming documents; and HTTP for its API.
 HBase: An open source Apache project that was developed as a part of Hadoop. HBase is a
column store database written in Java. It has capabilities similar to those that BigTable provides.
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9.  Oracle NoSQL Database: Oracle’s NoSQL database.
 Cassandra DB: A distributed database that excels at handling extremely large amounts of
structured data. Cassandra DB is also highly scalable. Cassandra DB was created at
Facebook. It is used by Instagram, Comcast, Apple, and Spotify.
 Riak: An open source, key-value store database written in Erlang. Riak has built-in fault-
tolerance replication and automatic data distribution that enable it to offer excellent
performance.
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10. Architecture of NoSQL
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11. Architecture NoSql
 It Consist Two Layer User Interface and
 Data Modeling and Storage System
 The key concepts of the NoSQL movement is to have DBs focus on the task of high-
performance scalable data storage, and provide low-level access to a data management
layer in a way that allows data management tasks to be conveniently written in the
programming language of choice rather than having data management logic spread across
Turing-complete application languages, SQL, and sometimes even DB-specific stored
procedure languages.
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12. NoSQL Distinguishing Characteristics
 (Scalability) Large data volumes.
 Flexibility (query handling ,eg.FB)
 It is an unstructured way of storing data.
 NoSQL databases are the collection of key-value pair, documents, graph databases or wide-
column stores which do not have any standard schema definitions that has to be adhered to.
 It is highly and easily Scalable replication and distribution.
 Queries need to return answers quickly
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13. • ACID transaction properties are not needed – BASE
• CAP Theorem
• Open source development
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14. CAP Theorem
 Consistency
 Availability
 Partition tolerance
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15. Consistency
 all nodes see the same data at the same time .
 client perceives that a set of operations has occurred all at once .
 More like Atomic in ACID transaction properties
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16. Availability
 Node failures do not prevent survivors from continuing to operate .
Every operation must terminate in an intended Response.
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17. Partition Tolerance
 The system continues to operate despite arbitrary message loss .
 Operations will complete, even if individual components are unavailable.
 But Not all of the C , A , P can be Satisfied Simultaneously
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18. Structure of CAP
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19. NoSQL Database Types
 There are a variety of types:
 Column Store – Each storage block contains
 Data from only one column
 Document Store – stores documents made up of tagged elements
 Key-Value Store – Hash table of keys
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20. Map Reduce
 Technique for indexing and searching large data volumes
 Two Phases, Map and Reduce
 Map
 Extract sets of Key-Value pairs from underlying data
 Potentially in Parallel on multiple machines
 Reduce
 Merge and sort sets of Key-Value pairs
 Results may be useful for other searches
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21. Example working of Map_Reduce
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22. Working of Map_Reduce
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23. Query Example
 SQL Schema Statements
 CREATE TABLE users (user_id
Varchar(30), age Number, status char(1),
 DROP TABLE users
 MongoDB Statements
 db.createCollection("users") or
 db.users.insert( { user_id: "abc123", age:
55, status: "A" } )
 db.users.drop()
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24. Insert Satament for Rdbms and NoSql
 SQL INSERT Statements
 INSERT INTO users(user_id, age,
status) VALUES("bcd001", 45, "A")
 MongoDB insert() Statements
 db.users.insert( { user_id: "bcd001", age:
45, status: "A" } )
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25. Retrieving statements in SQL & MongoDB
 SQL SELECT Statements
 SELECT * FROM users
 MongoDB find() Statements
 db.users.find()
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27. Thank You !
Have a Good Day ….
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