This document provides an overview of SQL and NoSQL databases. It discusses how relational databases using SQL emerged as the dominant data storage approach but faced challenges in scaling to big data workloads. NoSQL databases were developed to address these scaling needs by using non-relational data models like key-value, document, and column-oriented structures that are better suited to distributed architectures. The document outlines the history and characteristics of SQL and relational databases and how NoSQL databases address needs like scalability that drove their emergence in the big data era.
Course Title: Database Programming with SQL
Course Code: DEE 431
TOPICS COVER:
Database Terminologies
Drawbacks of Traditional System
Data processing Modes
Application of DBMS
Types of Database
Histroy of Database
Characteristics of Database
Advantages and Disadvantages of Database
Types of database architecture: 1 Tier, 2 Tier, 3 Tier
Representing Non-Relational Databases with Darwinian NetworksIJERA Editor
The Darwinian networks (DNs) are first introduced by Dr Butz [1] to simplify and clarify how to work with Bayesian networks (BNs). DNs can unify modeling and reasoning tasks into a single platform using the graphical manipulation of the probability tables that takes on a biological feel. From this view of the DNs, we propose a graphical library to represent and depict non-relational databases using DNs. Because of the growing of this kind of databases, we need even more tools to help in the management work, and the DNs can help with these tasks.
HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENTIJCSEA Journal
Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that the two models are integrated in one system to eliminate the limitations of the individual systems. The system is
implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having these two databases in one system can enhance storage and management of big data bridging the gap between relational and NoSQL storage approach.
The aim of this paper is to evaluate, through indexing techniques, the performance of Neo4j and
OrientDB, both graph databases technologies and to come up with strength and weaknesses os each
technology as a candidate for a storage mechanism of a graph structure. An index is a data structure that
makes the searching faster for a specific node in concern of graph databases. The referred data structure
is habitually a B-tree, however, can be a hash table or some other logic structure as well. The pivotal
point of having an index is to speed up search queries, primarily by reducing the number of nodes in a
graph or table to be examined. Graphs and graph databases are more commonly associated with social
networking or “graph search” style recommendations. Thus, these technologies remarkably are a core
technology platform for some Internet giants like Hi5, Facebook, Google, Badoo, Twitter and LinkedIn.
The key to understanding graph database systems, in the social networking context, is they give equal
prominence to storing both the data (users, favorites) and the relationships between them (who liked
what, who ‘follows’ whom, which post was liked the most, what is the shortest path to ‘reach’ who). By a
suitable application case study, in case a Twitter social networking of almost 5,000 nodes imported in
local servers (Neo4j and Orient-DB), one queried to retrieval the node with the searched data, first
without index (full scan), and second with index, aiming at comparing the response time (statement query
time) of the aforementioned graph databases and find out which of them has a better performance (the
speed of data or information retrieval) and in which case. Thereof, the main results are presented in the
section 6.
Course Title: Database Programming with SQL
Course Code: DEE 431
TOPICS COVER:
Database Terminologies
Drawbacks of Traditional System
Data processing Modes
Application of DBMS
Types of Database
Histroy of Database
Characteristics of Database
Advantages and Disadvantages of Database
Types of database architecture: 1 Tier, 2 Tier, 3 Tier
Representing Non-Relational Databases with Darwinian NetworksIJERA Editor
The Darwinian networks (DNs) are first introduced by Dr Butz [1] to simplify and clarify how to work with Bayesian networks (BNs). DNs can unify modeling and reasoning tasks into a single platform using the graphical manipulation of the probability tables that takes on a biological feel. From this view of the DNs, we propose a graphical library to represent and depict non-relational databases using DNs. Because of the growing of this kind of databases, we need even more tools to help in the management work, and the DNs can help with these tasks.
HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENTIJCSEA Journal
Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that the two models are integrated in one system to eliminate the limitations of the individual systems. The system is
implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having these two databases in one system can enhance storage and management of big data bridging the gap between relational and NoSQL storage approach.
The aim of this paper is to evaluate, through indexing techniques, the performance of Neo4j and
OrientDB, both graph databases technologies and to come up with strength and weaknesses os each
technology as a candidate for a storage mechanism of a graph structure. An index is a data structure that
makes the searching faster for a specific node in concern of graph databases. The referred data structure
is habitually a B-tree, however, can be a hash table or some other logic structure as well. The pivotal
point of having an index is to speed up search queries, primarily by reducing the number of nodes in a
graph or table to be examined. Graphs and graph databases are more commonly associated with social
networking or “graph search” style recommendations. Thus, these technologies remarkably are a core
technology platform for some Internet giants like Hi5, Facebook, Google, Badoo, Twitter and LinkedIn.
The key to understanding graph database systems, in the social networking context, is they give equal
prominence to storing both the data (users, favorites) and the relationships between them (who liked
what, who ‘follows’ whom, which post was liked the most, what is the shortest path to ‘reach’ who). By a
suitable application case study, in case a Twitter social networking of almost 5,000 nodes imported in
local servers (Neo4j and Orient-DB), one queried to retrieval the node with the searched data, first
without index (full scan), and second with index, aiming at comparing the response time (statement query
time) of the aforementioned graph databases and find out which of them has a better performance (the
speed of data or information retrieval) and in which case. Thereof, the main results are presented in the
section 6.
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
The following was presented at the Semantic Technology conference in March of 2006 in San Jose California. This case study examines the extension of the National
Information Exchange Model NIEM to include K-12
education metadata. NIEM’s compliance with ISO/IEC
11179 metadata standards was found to be critical for
cost-effective system interoperability. This study indicates
that extending the NIEM can be compatible with newer
RDF and OWL metadata standards. We discuss how this
strategy will dramatically lower data integration costs and
make longitudinal data analysis more cost-effective. We
make recommendations for state education agencies,
federal policy makers, and metadata standards
organizations. The conclusion discusses the possible
impacts of recent innovations in collaborative metadata
standards efforts.
NOSQL Database Engines for Big Data Managementijtsrd
We are living in the digital world and last two decades have seen significant expansion in the information on internet technology. In present digital world the IOT is most popular term means computers, mobile phones and physical devices like sensors are connected to internet. With the rapid outreach of internet it is very important to focus on technological advancements for managing huge amount of data with easy access. Mrs. Yasmeen "NOSQL Database Engines for Big Data Management" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18608.pdf
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]Usman Tariq
In this PPT, you will learn:
• About data modeling and why data models are important
• About the basic data-modeling building blocks
• What business rules are and how they influence database design
• How the major data models evolved
• About emerging alternative data models and the needs they fulfill
• How data models can be classified by their level of abstraction
Author: Carlos Coronel | Steven Morris
Big Data is used to store huge volume of both structured and unstructured data which is so large and is
hard to process using current / traditional database tools and software technologies. The goal of Big Data
Storage Management is to ensure a high level of data quality and availability for business intellect and big
data analytics applications. Graph database which is not most popular NoSQL database compare to
relational database yet but it is a most powerful NoSQL database which can handle large volume of data in
very efficient way. It is very difficult to manage large volume of data using traditional technology. Data
retrieval time may be more as per database size gets increase. As solution of that NoSQL databases are
available. This paper describe what is big data storage management, dimensions of big data, types of data,
what is structured and unstructured data, what is NoSQL database, types of NoSQL database, basic
structure of graph database, advantages, disadvantages and application area and comparison of various
graph database.
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations.
The following was presented at the Semantic Technology conference in March of 2006 in San Jose California. This case study examines the extension of the National
Information Exchange Model NIEM to include K-12
education metadata. NIEM’s compliance with ISO/IEC
11179 metadata standards was found to be critical for
cost-effective system interoperability. This study indicates
that extending the NIEM can be compatible with newer
RDF and OWL metadata standards. We discuss how this
strategy will dramatically lower data integration costs and
make longitudinal data analysis more cost-effective. We
make recommendations for state education agencies,
federal policy makers, and metadata standards
organizations. The conclusion discusses the possible
impacts of recent innovations in collaborative metadata
standards efforts.
NOSQL Database Engines for Big Data Managementijtsrd
We are living in the digital world and last two decades have seen significant expansion in the information on internet technology. In present digital world the IOT is most popular term means computers, mobile phones and physical devices like sensors are connected to internet. With the rapid outreach of internet it is very important to focus on technological advancements for managing huge amount of data with easy access. Mrs. Yasmeen "NOSQL Database Engines for Big Data Management" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-6 , October 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18608.pdf
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]Usman Tariq
In this PPT, you will learn:
• About data modeling and why data models are important
• About the basic data-modeling building blocks
• What business rules are and how they influence database design
• How the major data models evolved
• About emerging alternative data models and the needs they fulfill
• How data models can be classified by their level of abstraction
Author: Carlos Coronel | Steven Morris
This Presentation is about NoSQL which means Not Only SQL. This presentation covers the aspects of using NoSQL for Big Data and the differences from RDBMS.
What is NoSQL? How does it come to the picture? What are the types of NoSQL? Some basics of different NoSQL types? Differences between RDBMS and NoSQL. Pros and Cons of NoSQL.
What is MongoDB? What are the features of MongoDB? Nexus architecture of MongoDB. Data model and query model of MongoDB? Various MongoDB data management techniques. Indexing in MongoDB. A working example using MongoDB Java driver on Mac OSX.
HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENTIJCSEA Journal
Relational database systems have been the standard storage system over the last forty years. Recently,
advancements in technologies have led to an exponential increase in data volume, velocity and variety
beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational
database for data storage and management. Some core features of database system such as ACID have
been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and
management of extremely voluminous data of diverse components known as big data, such that the two
models are integrated in one system to eliminate the limitations of the individual systems. The system is
implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having
these two databases in one system can enhance storage and management of big data bridging the gap
between relational and NoSQL storage approach.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
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2. Topics
What is SQL? What is NoSQL?
Why have relational databases been
successful?
Why did NoSQL databases emerge?
How are their data models different?
3.
4. SQL & relational databases
Relational databases are software
applications that store data
Data is stored in tables that have rows &
columns : think excel spreadsheets
FirstName LastName Age
Zipcode
Gender
Bob
Smith
45
38444
M
Jane
Happy
23
15122
F
Fred
Jones
55
92102
M
Johnny
Appleseed
26
90025
M
5. SQL & relational databases
Relational databases typically have
many tables that are “related” to one
another
6. SQL & relational databases
Relational databases support access to
data in tables through a language called
“SQL” – Structured Query Language
SQL supports “set” based operations on
tables – selection, projection, joining
SQL is based on relational algebra
7. SQL & relational databases
Relational databases were developed in
the late 1970s at IBM
They have been the dominant approach
to data management in the enterprise
through the early 2000’s
Examples include
Oracle
Sybase
MySQL
Postgress
8.
9. NoSQL databases
NoSQL are software applications that
store data
They, not surprisingly, do not use SQL or
the relational model (interrelated tables)
They are “less strict” about data
definition
They were developed in a “big-data”
world for applications needing massive
scalability (clustering)
12. RDBMS value - persistence
During the 90’s and 2000’s as pc’s
became ubiquitous, distributed
computing took off.
In the 1990’s, client-server and n-tier
architectures dominated enterprise
development
The late 90’s and 2000’s saw the
dominance of the web and distributed
applications that broke out of enterprise
13. RDBMS value - persistence
In this distributed world where
applications needed to keep data
around for
Many users
Extended periods
RDBMS emerged as the defacto choice for
persisting data.
14. RDBMS value - concurrency
Another challenge that distributed
applications presented was
concurrency:
many users viewing and potentially updating
the same data at the same time
Concurrency is notoriously difficult to
get right for even the best engineers.
Relational databases “helped” by
controlling data access with transactions
15. RDBMS value - integration
Enterprise application eco-systems
necessitate multiple integrated software
applications. Example
Customer Service app
Biz Intel app
E-Commerce app
Inventory management apps
Common approach was to use a shared
rdbms database integration approach.
16. RDBMS value – SQL
RDBMS providers all supported a core
SQL standard
In theory this would allow developers to
switch reliance on different RDBMS
providers without problems
In fact, different providers (Oracle,
Sybase, Microsoft) developed different
“dialects” or SQL extensions (pl SQL vs.
T-SQL)
17.
18. Crack #1– impedance mismatch
Impedance mismatch is the difference
between the relational model and inmemory data structures
19. Crack #1– impedance mismatch
In the late 1990s people believed that
impedance mismatch would lead to
RDBMS being replaced by databases
that replicated in-memory structures to
disk (OODBMS)
While the 1990s saw the rise of OO
programming languages, OODBMS
never took gained real traction
20. Crack #1– impedance mismatch
OODBMS didn’t gain traction because
Impedance mismatch had been made easier
to deal with by Object-Relational (OR)
mapping frameworks like Hibernate, iBatis,
& Cocoon
There was a growing professional divide
between application developers and
database administrators
The value of RDBMS as an app integration
mechanism was large
21. Crack #2– SOA
The 2000’s saw a shift in how enterprise
applications interacted
Historically, many applications interacted
through a shared RDBMS.
This approach – shared integration
RDBMS – has serious problems
Overly complex schema
Cant change tables or add indices easily
Database has to preserve integrity
22. Crack #2– SOA
Interactions between applications shifted
to web-services
Web-services constituted protocols for
moving documents (XML, JSON) over
HTTP using SOAP or REST based
approaches
SOA allowed applications to
encapsulate data and expose it through
services
23. The Final Crack #3– Clusters
The internet saw several large web
properties dramatically increase in scale
Websites started tracking activity and
structure in a very detailed way
Social gestures
Social links
Log data
Purchase gestures
Increasing numbers of users appeared
using more devices
24. The Final Crack #3– Clusters
The problem with scaling out (clustering)
is that RDBMS are not designed to run
on clusters.
Oracle RAC & MS SQL Server all use
the concept of a shared disk sub-system
Still single point of failure and scaling
limitation
The final crack – mismatch between
RDBMS & clusters
25. NoSQL Emergence
The emergence of NoSQL was really
about needing databases that run on
clusters
One exception is Graph databases
Though problems with shared database
integration and impedance mismatch
existed, it was the need for scale that
drove the emergence of NoSQL
databases
26.
27. Aggregate Data Models
A key characteristic of NoSQL
databases is that they do not use the
Relational data metamodel (relations &
tuples)
There are four types of data
metamodels in the NoSQL eco-system
Key-value
Document
Column-family
Graph
28. Aggregate Data Models
Key-value, document, and columnfamily NoSQL databases share a
common characteristic of their data
models called “aggregate orientation”
We ill not cover graph based data metamodels in this presentation
29. Aggregates
The relational model takes information
you want to store and divides it into
rows.
Rows are lists of simple data values.
Rows are the unit of data operation
Aggregate orientation recognizes that
often times data units can be more
complex and can have nested lists and
record structures
30. Aggregates
The relational model takes information you
want to store and divides it into rows.
In RDBMS rows are lists of simple data
values.
In RDBMS rows are the unit of data
operation
Aggregate orientation recognizes that often
times data units can be more complex and
can have nested lists and record structures
With Aggregates, aggregates are the unit
of data operation
33. Consequences of Aggregate
Orientation
Relations capture data elements and
relations, but not aggregates.
Aggregates are really “chunks” of data
that are typically retrieved and operated
on as an interaction unit.
Aggregates are about how the data is
being used.
RDBMS do not have knowledge of
aggregate structure and cant use it to
store and distribute data
34. Consequences of Aggregate
Orientation
So, RDBMS are aggregate-ignorant. Is
that a bad or good thing? Its both
Its good if you need to access and use
the data in many different ways – if you
don’t have a primary structure for
manipulating your data
Its bad if you want to run on a cluster.
Aggregates are great on clusters
because you can distribute them across
nodes
35. Consequences of Aggregate
Orientation
Aggregate orientation allows you to
operate many logical data items (in the
aggregate) by updating the aggregate
atomically
Aggregate oriented NoSQL databases
can be said to support transactions on
single aggregates, but not across
aggregates
36. Key-Value & Document Data
Models
Both types of databases have a key or
Id that is mapped to an aggregate data
structure in a virtual table
With key-value NoSQL dbs, we can only
access the aggregate by looking up its
key
With document databases we can also
look up aggregates by fields in the
aggregate
37. Key-Value & Document Data
Models
Examples of Key-Value NoSQL dbs are
Redis
Examples of Document NoSQL dbs are
Mongodb
Couchbase
SimpleDB
38. Column-Family Data Models
These NoSQL databases where
influenced by Google’s BigTable
The Columnar is a two-level aggregate
structure
There is a key (row identifier) that maps to
the aggregate of interest
The aggregate is a map of more detailed
values – these are referred to as columns
40. Column-Family Data Models
Column-family dbs organize columns
into families
The data is row-oriented
Each row is an aggregate (eg. Customer
with id 1234)
The data is column-oriented
Each column family defines a record type
(customer profile)
But, columns can also be dynamic and
unique (to model lists)