its all about amazone database basic to advance

1. Amazon
DATABASES
CS-A
BATCH 2
GROUP 3
Prof. Anil Kadu

2. • Introduction to Amazon's database
• Objectives
• Amazon web services
• Scalability and availability
• Data security and privacy
• Literature Review
• References
Table of Content

3. COMPANY GROUP
Introduction to Amazon Platform:
•Amazon.com is one of the world's largest e-
commerce platforms, serving tens of millions of
customers globally.
•The platform offers a wide range of products, from
books and electronics to clothing and household
items.
•At Amazon, reliability, scalability, and performance
are critical to ensure an exceptional shopping
experience for customers.

4. COMPANY GROUP
Introduction to
Amazon Database:
• Amazon Database Services Overview: Amazon offers database
services for secure data storage and organization.
• Similar to a digital storage system, it's used for managing
various data types.
• Secure Data Storage: Amazon offers secure digital storage for
various data types, ensuring data confidentiality.
• Scalable Solutions: Businesses can easily scale resources to
manage growing data needs efficiently.
• Cost-Effective: Pay-as-you-go pricing minimizes upfront costs,
making it budget-friendly.
• Global Accessibility: Data centers worldwide provide low-
latency access, benefiting users globally.

5. • The objective of exploring the topic of Amazon databases is to understand their scalability, flexibility, and how
they can effectively handle large volumes of data, enabling businesses to grow and adapt to changing
demands.
• Another objective is to recognize the high availability and durability provided by Amazon databases, ensuring
that data is always accessible, protected, and can be reliably recovered in case of failures, thus ensuring the
smooth operation and continuity of businesses.
Objective

6. TWO TYPES OF DATABASES
RELATIONAL
DATABASES
NON-RELATIONAL
DATABASES

7. • A relational database organizes data into rows and columns, which collectively form a table.
• Structured Data Management : Relational databases organize data into structured tables with rows and
columns.
• ACID Properties : Relational databases follow ACID (Atomicity, Consistency, Isolation, Durability) properties,
ensuring data integrity and reliability.
• Multi-User Support : They provide concurrent access for multiple users or applications, ensuring data
consistency.
• Scalability Challenges : Scaling relational databases can be complex, especially when dealing with large
datasets or high traffic loads.
• Use Cases : Relational databases are suitable for a wide range of applications, including e-commerce, content
management systems, and financial systems.
Relational Databases

8. Amazon Web
Services
Amazon Aurora
Amazon RDS
Amazon Redshift

9. Amazon Aurora
• Amazon Aurora is a fully managed relational database service that combines
the performance and availability of a traditional enterprise database with the
simplicity and cost-effectiveness of a cloud database.
• Amazon Aurora is based on MySQL and PostgreSQL, but it adds a number of
features that make it more scalable, reliable, and secure.
• Aurora Storage Layer is durable and scalable.
• Aurora Storage Layer can store data for multiple Aurora DB
clusters.
• Aurora Replicas are identical copies of the database.
• Aurora Replicas are spread across multiple availability
zones.
• When you write data to an Aurora DB cluster, it is written
to all of the replicas.
• This ensures that your data is always available, even if one
of the replicas goes down.

10. Amazon RDS
• Amazon Relational Database Service (Amazon RDS) is a fully
managed relational database service that makes it easy to set up,
operate, and scale a relational database in the cloud.
• Amazon RDS provides a Number of Features that make it a good
choice for businesses of all sizes.
• High Availability: It ensures high availability through multi-Availability
Zone deployments, reducing downtime risk.
• Scalability: Amazon RDS allows easy vertical and horizontal scaling for
accommodating growing workloads.

11. Amazon Redshift
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in
the cloud. It is designed to help you quickly and easily analyze large amounts
of data using standard SQL.
• Automatic scaling : Amazon Redshift can automatically scale up or down
based on your workload, so you only pay for the resources that you use.
• Point-in-time restore : You can restore your data to any point in time
within the past 90 days, so you can easily recover from accidental data
loss or corruption.
• Data encryption : Your data is encrypted at rest and in transit, so you can
be confident that your data is secure.
• Integration with Amazon S3 : You can easily import data from Amazon S3
into Amazon Redshift, and you can also export data from Amazon Redshift
to Amazon S3.

12. Comparative Analysis

13. Feature Amazon Aurora Amazon RDS Amazon Redshift
Database engines MySQL and PostgreSQL
MySQL, PostgreSQL, Oracle, MariaDB,
SQL Server
PostgreSQL
Data storage Shared storage layer Dedicated storage Dedicated storage
Availability zones Multi-AZ deployment Single-AZ deployment Multi-AZ deployment
Automatic scaling Automatic scaling Manual scaling Manual scaling
Performance
Up to 5 times the throughput of a
traditional MySQL database
Up to 30 times the throughput of a
traditional MySQL database
Up to 100 times the throughput of a
traditional PostgreSQL database
Cost-effectiveness
Pay for the storage and compute
resources that you use
Pay for the storage and compute
resources that you use
Pay for the storage and compute
resources that you use
Security Encryption at rest and in transit Encryption at rest and in transit Encryption at rest and in transit
Migration support
Supports migration from MySQL and
PostgreSQL databases
Supports migration from MySQL,
PostgreSQL, Oracle, MariaDB, and SQL
Server databases
Supports migration from PostgreSQL
databases

14. • Also known as NoSQL Databases
• Flexible Data Models : NoSQL databases support various data models, including document, key-value,
column-family, and graph databases, providing flexibility for different data types and structures.
• Schema-less : Unlike relational databases, NoSQL databases are typically schema-less, allowing developers to
store data without a predefined schema.
• Scalability : NoSQL databases are designed for horizontal scalability, making it easier to handle large volumes
of data and high traffic loads.
• High Availability : Many NoSQL databases offer built-in high availability and fault tolerance features, ensuring
data accessibility even in the presence of hardware failures.
• Use Cases : NoSQL databases are commonly used for applications like content management systems, social
media platforms, real-time analytics, and sensor data storage.
Non-Relational Databases

15. DynamoDB
• Fully Managed NoSQL Database : Amazon DynamoDB is a fully managed
NoSQL database service that offers seamless scalability, automatic backups,
and high availability. It eliminates the operational overhead of database
management.
• Scalability at Any Scale : DynamoDB can effortlessly handle workloads that
range from a few requests per second to millions of requests per second.
Its auto-scaling feature adjusts capacity to match traffic patterns.
• Low Latency and High Throughput : DynamoDB provides single-digit
millisecond latency for read and write operations, making it suitable for
real-time applications like gaming and IoT. It supports high-throughput
workloads with ease.
• Flexible Data Models : It supports both document and key-value data
models, enabling developers to store and query data in a way that best fits
their application requirements.

16. Classification on the Basis of
Requirements

17. Requirement Best database service
Relational database engine
Amazon Aurora, Amazon RDS, Amazon
Redshift
Data warehouse engine Amazon Redshift
Lots of storage space Amazon Aurora
More moderate amount of storage space Amazon RDS
Small amount of storage space Amazon Redshift
High performance Amazon Aurora
More moderate level of performance Amazon RDS
Good level of performance on a budget Amazon Redshift
Database service that can scale up and down as needed Amazon Aurora
Database service with a predictable level of scalability Amazon RDS
Database service that can scale up to very large sizes Amazon Redshift

18. Scalability
• You can adjust the computing power and memory
of your database system, with a maximum limit of
32 CPUs and 244 GiB of RAM.
• For Amazon Aurora, the database storage can
automatically grow as your data needs increase,
up to a maximum of 64 TB. Alternatively, you can
set a maximum size yourself.
• MVSOL, MariaDB, Oracle, and PostgreSQL engines
can scale up to 32 TB of storage, while SQL Server
supports up to 16 TB.
• You can create copies of your main database called
replicas, which can handle high-volume read
requests from applications.

19. Availability
• Always Accessible: Amazon's databases are
designed to be highly available.
• They use a Multi-Availability Zone
deployment, which means your data is
automatically replicated across different
locations.
• Automatic Backups and Recovery: Amazon's
databases regularly take backups of your
data and transaction logs.

20. Data Security and Privacy
• They have a number of physical, electronic,
and procedural safeguards in place to
protect customer data.
• They use industry-leading encryption
features to protect data in transit and at
rest.
• Customers have control over how their data
is stored and who can access it.
• Amazon does not access or use customer
data without their permission, except as
required to prevent fraud or abuse, or to
comply with law.

21. AUTHORS
RESEARCH PAPER
NAME ABSTRACT
A STUDY ON AWS
ARCHITECTURE
Prangya Prachi Samantaray
Dr. Aswini Kumar Mohanty
Dr.Arati Pradhan
• In the current era, cloud computing has become integral, offering
enhanced IT resource utilization and services.
• This growth has led to increased adoption of cloud solutions, with a
focus on inter-cloud communication and security.
• This paper centers on the hybrid cloud concept, particularly in the
context of Amazon Web Services (AWS), discussing its integration
with on-premises infrastructure and addressing key issues.
Millions of Tiny
Databases
Marc Brooker
Tao Chen
Fan Ping
• In 2013, a project was initiated to build a database for Amazon
EBS that required high availability and strong consistency.
• Recognizing the constraints of the CAP theorem and distributed
systems, the approach shifted to creating a distributed system with
numerous databases.
• Physalia, a transactional key-value store, was developed to provide
high availability and strong consistency for cloud control systems.
• It strategically places data based on transaction patterns and
infrastructure knowledge.
• This paper discusses its role in Amazon EBS and its broader
applicability to distributed system challenges.

22. AUTHORS
RESEARCH PAPER
NAME ABSTRACT
Database Security
Management for Healthcare
SaaS in the Amazon AWS
Cloud
Fabio Bracci,
Antonio Corradi,
Luca Foschini
• The rise of healthcare Cloud computing as an evolution of
networking, SaaS, and security best practices.
• Describes how to make healthcare SaaS services compliant in the
Cloud, ensuring data security standards are met.
• Encryption & security key management functions can be easily
added without compromising system performance.
• Future work involves conducting experiments on larger-scale
Cloud deployments and exploring different Cloud providers like
OpenStack.
Amazon Relational
Database Service Delivers
Enhanced Database
Performance at Lower Total
Cost
Carl W. Olofson,
Matthew Marden
• Amazon RDS supports six database engines the open source
MySQL, PostgreSQL, and Maria DB, as well as Oracle, Microsoft
SQL Server, and Amazon’s own cloud-native RDBMS, Aurora.
• They required databases that they could cost-effectively scale to
match business demand in a cost-effective but high-performing
manner.
• It is a cloud-native database service aligned with cloud strategies,.
IDC's study demonstrates the value of running various database
workloads and engines in RDS.

23. AUTHORS
RESEARCH PAPER
NAME ABSTRACT
Analysis of research on
amazon AWS cloud
computing seller data
security
Muhammad
Talha, Mishal
Sohail, Hajar
Hajji
• Big data and cloud computing are intertwined; major cloud
companies like Google and Amazon handle vast amounts of big
data.
• There are two types of RDS databases: Online Transaction
Processing (OLTP) and Online Analytical Processing (OLAP) Amazon
uses AWS cloud computing to implement data analysis and mining,
with a high level of security protection
• Significance of this big data in the amazon dataset is beyond
collection and aggregation.
• Its true value emerges during processing, analysis, visualization,
and application. Core big data concerns encompass data origin,
analysis, and commercialization.
Dynamo: Amazon’s
Highly Available Key-
value Store
Avinash Lakshman,
Alex Pilchin,
Swaminathan
Sivasubramanian
• Amazon.com faces a massive-scale reliability challenge due to the
global e-commerce operation's size.
• This paper introduces Dynamo, a highly available key-value
storage system.
• Dynamo prioritizes availability over consistency and employs
object versioning and application-assisted conflict resolution.

24. AUTHORS
RESEARCH PAPER
NAME ABSTRACT
Using the Amazon Metric
to Construct an Image
Database based on what
people do
Robert Columb ,
Theodor G Wheld
• The General approach of constructing an image database based on
people's activities or behaviors, using methods is commonly
employed in data analysis and computer vision.
• Defining scope & objective Data collection
• Data Annotation
• Dimensionally Reduction
• Data Construction Data Analysis
• Improvement and Application
A Feasible Schema Design
Strategy for Amazon
DynamoDB: A Nested
Normal Form Approach
Wai Yin Mok and the
University of Alabama
• Designing a schema for Amazon DynamoDB involves making
decisions about the structure of your tables and the organization of
your data to optimize performance, scalability, and cost-
effectiveness.
• First understand the DataModel ,Start with Entities, Denormalize it,
Hierarchical Data Modelling identify the PK,CK and FK, batch
operation and transaction ,estimate and optimize capacity units
,testing and optimization

25. AUTHORS
RESEARCH PAPER
NAME ABSTRACT
Research Paper on AWS
Cloud Infrastructure vs
Traditional On-Premise
Akshay Kushwaha
• This paper highlights the advantages of AWS Cloud over on-
premises solutions and introduces AWS services.
• AWS, launched in 2006, has seen widespread adoption, with 77%
of enterprises having cloud applications or infrastructure.
• It offers a wide range of global cloud products with over one
million active enterprise customers, featuring on-demand services
and over 140 offerings.
• This enables rapid response to changing business needs for
startups, SMBs, and the public sector.

26. Questions ?

27. THANK YOU
CS-A
BATCH 2
GROUP 3
Prasanna Bhalerao [35]
Yash Bhalerao [36]
Atharva Chawle [50]
Chirag Belani [51]
Kalyani Chopade [52]