This document discusses trends in database technologies and big data. It begins by questioning whether relational databases are always the most efficient way to store data and whether companies like Facebook use traditional databases. It then covers object-relational mapping (ORM), which provides a bridge between object-oriented programming languages and relational databases. The document also discusses NoSQL databases as an alternative for handling large, unstructured "big data" and provides examples like document stores, key-value stores, and graph databases. It concludes by defining big data and discussing the types, analysis, and market size of big data technologies.

1. A PEEK INTO THE FUTURE OF
DATA
ORM
NoSQL
Big Data
Presented by:
PRATEEK CHAUHAN
10ESKCS738

2. BEFORE STARTING….
• Are relational tables the most efficient way to
manage data?
• Do companies like Facebook, Twitter really use
traditional relational DBMS to manage data?

3. ORM
O
OBJECT
M
R
RELATIONAL
MAPPING

4. WAYS TO ACCESS DATABASE
• Using a GUI based DBMS
• Using a console based DBMS
• Using database embedded with applications
(most important).

5. THE BRIDGE ?
APPLICATION
PROGRAMMING
INTERFACE
(API)
DATABASE
THE BRIDGE

6. THE BRIDGE: JDBC
•Standard Java API for database-independent connectivity
between the Java programming language and a wide range of
databases.
•JDBC provides a flexible architecture to write a database
independent applications that can run on different platforms and
interact with different DBMS without any modification.
•JDBC includes APIs for each of the task commonly associated
with database usage:
Making a connection to a database.
Creating SQL statements.
Executing SQL queries in the database.
Viewing & modifying the resulting records.

7. JDBC
Pros of JDBC
• Clean and simple SQL
processing
• Good performance with
small data
• Very good for small
applications
• Simple syntax so easy to
learn
Cons of JDBC
• Complex if it is used in large
projects
• Large programming
overhead
• No encapsulation
• Hard to implement MVC
concept
• Query is DBMS specific

8. The Problem

9. The Problem
•
•
•
•
Mapping member variables to columns
Mapping Relationships
Handling data types (esp. Boolean)
Managing changes to object state

10. The Problem
Relational
Object
Mapping!

11. Saving without ORM
•
•
•
•
•
Database Configuration
The Model Object
Service method to create the model object
Database Design
DAO method to save the object using SQL
queries

12. The ORM Way
• JDBC Database Configuration – ORM specific
Configuration
• The Model object – Annotations
• Service method to create the model object –
Use the ORM framework API API
• Database Design – Not Needed !
• DAO method to save the objects using SQL
queries – Not Needed !

13. THE ONLY DISADVANTAGE
• Boilerplate code
=> XML configuration files
=> XML system files
=> Extra classes like POJO, etc.

15. NoSQL: THE NAME
• SQL: In general, “Traditional Relational DBMS”.
• Past decade: RDBMS isn’t the best solution.
• NoSQL: “No SQL”=> Not using traditional
RDBMS

16. ISSUES WITH RDBMS
• Primary issue: big package, has all the
features, but sometimes we don’t need all of
them:
COMPROMISES
• Convenient
• Multi-user
SIMILAR
• Safety
• Persistent
BOOSTS
• Reliable
• MASSIVE (big
data)
• Efficient

17. NoSQL SYSTEMS
Alternative to traditional RDBMS
Pros
• Flexible Schema
• Quicker/ Cheaper to
setup
• Massive scalability:
handle
big data
• Relaxed Consistency:
higher performance &
availability
Cons
• No declarative query
language: more
programming
• Relaxed Consistency:
fewer guarantees

18. Example: Social-Network Graph
Each record: User ID1, User ID2 …
Separate records: User Id, name, age, gender …
A
B
I
G
H
C
F
D
K
J
E
L

19. Example: Social-Network Graph
• TASK: Find all friends of given users.
• TASK: Find all friends of friends of given user.
• TASK: Find all women friends of men friends of
given user.
• TASK: Find all friends of friends of…. friends of
given user.

20. INCARNATIONS OF NoSQL
• MapReduce Framework: OLAP (big operations)
• Key-Value Store: OLTP (small operations)
• Document Stores
• Graph database systems

21. MapReduce Framework
• Originally from Google, open source: Hadoop.
• Two main functions:
1. Map: divides the problem into sub problem.
2. Reduce: operates upon the sub problems and
combines output to give record.
• Current implementations:
1. Hive: SQL like language
2. Pig: statement language

22. Graph Database Systems
•Data Model: nodes and edges.
•Nodes may have properties.
•Edges may have labels or roles.
•Example: neo4j, FlockDB, Pregel
Friends
ID: 3
ID: 1
Friends
Likes
Likes
ID: 2

24. AGAIN, SOME QUESTIONS…
• What is the maximum file size you’ve dealt so
far?
• What is the maximum download speed you
get?
• How much time required to just transfer data?

25. What is Big Data?
• Every day, we create 2.5 quintillion bytes of data — so
much that 90% of the data in the world today has been
created in the last two years alone.
• From the beginning of recorded time until 2003,
 We created 5 billion gigabytes (exabytes) of data.
• In 2011, the same amount was created every two days
• In 2013, the same amount of data is created every 10
minutes.
THIS IS “BIG DATA”

26. What is Big Data?-FINALLY..
• Big- Data’ is similar to ‘Small-data’ but bigger
• But having data bigger it requires different
approaches:
– Techniques, tools, architecture
• With an aim to solve new problems
– Or old problems in a better way

27. Type of Data
• Relational Data (Tables/Transaction/Legacy
Data)
• Text Data (Web)
• Semi-structured Data (XML)
• Graph Data
– Social Network, Semantic Web (RDF), …
• Streaming Data
– You can only scan the data once

28. What to do with these data?
• Aggregation and Statistics
– Data warehouse and OLAP
• Indexing, Searching, and Querying
– Keyword based search
– Pattern matching (XML/RDF)
• Knowledge discovery
– Data Mining
– Statistical Modeling

29. MARKET SIZE

30. Big Data Analytics Technologies
• NoSQL: non-relational database solutions such as
Hbase, Cassandra, MongoDB, Riak, CouchDB, and
many others.
• Hadoop: It is an ecosystem of software
packages, including MapReduce, HDFS, and a whole
host of other software packages.

31. Summarizing…
• Key enablers for the appearance and growth
of ‘Big-Data’ are:
+ Increase in storage capabilities
+ Increase in processing power
+ Availability of data

32. THANK YOU