A database is a collection of data stored systematically with the ability to manipulate. A database is a complete Data management tool provided to perform operations on it.
Classification of
Databases -
Here are some popular types
of databases.
- Distributed databases: A distributed database is a database in which information is gathered from multiple sources at different locations. In this database, the data is not present in one place but distributed at various locations. It helps in case a local system fails rest of the database will remain functional.
- Relational databases: This type of database defines database relationships in the form of tables. It is also called Relational DBMS and is the most popular DBMS type in the market. Some examples of RDBMS systems are- MySQL, Oracle, and Microsoft SQL Server database.
- Object-Oriented database: This database offers storage for all data types, as data is stored in the form of objects. The objects held in the database have attributes and methods that define what to do with the data. PostgreSQL is an example of an object-oriented relational DBMS.
- Cloud databases: Cloud database is optimized and built for a virtualized environment. There are too many advantages of a cloud database, some of which can pay for storage capacity and bandwidth. It also offers scalability on-demand, along with high availability.
- Data warehouses: A Data warehouse is an information system that contains historical and commutative data from single or multiple sources. The Data Warehouse concept simplifies the reporting and analysis process of the organization. Data Warehouse facilitates a single version of truth for the company, decision-making, and forecasting.
Database over the
cloud? Why Do we need it?
A cloud database is a
collection of data that is entirely managed and organized by IT system and
hosted on a public, private, hybrid cloud computing platform. It is similar to
an on-premise database when considered in overall design and functionality but
having being present at a remote location managed by a service provider. The
main difference lies in how the database is deployed and managed.
Cloud databases can store any
data type based on the requirement and may appear the same to the end user and
applications compared with local databases. Depending on the particular
database software used, cloud databases can store structured, unstructured, or
semi-structured data, just as their on-premises counterparts do.
The main reason for using
cloud databases is that company hosting the database has the responsibility to
manage the underlying system infrastructure, installations, data protection,
etc. the end user is not responsible for any of such activities. That reduces
the routine management work traditionally done by IT operations workers and
database administrators (DBAs). A DBA can then take on other tasks, such as
optimizing databases for applications and tracking the usage and cost of cloud
database systems.
Most IT companies are now
shifting to database deployment over the cloud as it is economically cheaper.
In a recent report on cloud databases published in December 2021, Gartner
forecasted that they would account for 50% of total database management system
(DBMS) revenues worldwide in 2022.
Databases offered by Amazon Web Services-
- Amazon Aurora : Amazon Aurora is an RDBMS service that combines the
speed and availability of high-end commercial databases with the simplicity and
cost-effectiveness of open-source databases. Aurora is fully compatible with
MySQL and PostgreSQL, allowing existing applications and tools to run without
requiring modification.
- Amazon RDS : Amazon Relational Database Service (RDS) is a
managed relational database service that provides six familiar database engines
to choose from, including- Amazon Aurora, MySQL, MariaDB, PostgreSQL, Oracle,
and Microsoft SQL Server. Amazon RDS handles routine database tasks, such as
provisioning, patching, backup, recovery, failure detection, and repair. Amazon
RDS makes it easy to use replication to enhance availability and reliability
for production workloads. Using the multi-AZ deployment option, you can run
mission-critical workloads with high availability and built-in automated
failover from your primary database to a synchronously replicated secondary
database. Read Replicas can scale out beyond the capacity of single database
deployment for read-heavy database workloads.
- Amazon Redshift : Amazon Redshift uses SQL to analyze structured
and semi-structured data across data warehouses, operational databases, and
data lakes, using AWS-designed hardware and machine learning to deliver the
best price performance at any scale.
- Amazon DynamoDB : Amazon DynamoDB is a NoSQL database that supports
key-value and document data models. Developers can use DynamoDB to build
modern, serverless applications that can start small and scale globally and
supports petabytes of data with tens of millions of read and write requests per
second. DynamoDB is designed to run high-performance, internet-scale
applications that would overburden traditional relational databases.
Comparison –
Amazon Aurora – It is an RDBMS service designed to provide very
high unparalleled performance with high availability at a global scale with the
support of MySQL and PostgreSQL.
Amazon
RDS - is an RDMS service
similar to amazon aurora and is compatible with MySQL, PostgreSQL, MariaDB,
Microsoft SQL Server, and Oracle. RDS performs less when compared to 'Aurora'
as later includes the provision of external database services like SQL servers.
Amazon Redshift – It is a data warehousing solution provided by amazon
which can scale up to a petabyte of data in comparison to 'Aurora' which has a
hard limit of 64Tb of data, Though redshift takes more time in scaling and
allows autoscaling and multiple nodes, instances in a single cluster.
Amazon DynamoDB – It is a non-RDBMS service based on Key-value pair and no-SQL, provides single-digit milliseconds performance
Amlgo Labs with it's world-class Engineers and Analysts, helps
you to decide the best technique and approach for your organization & task. Our assistance
facilitates you to understand and decide which database would suit your
requirements most depending on the data and performance you require from it.
Let’s start with Analysing
the behavior of data and its sources, if the data at hand is a key value
pair similar to social media data or unstructured data, then we can opt
for Amazon DynamoDB as it supports the
Key-value-pair and works with NoSQL language, which makes easier to manipulate
data and quick access from the keys. Although it has a limitation of 400kb in
item size but it works fine for most cases.
If we have row-column
structured data we can choose between the three RDBMS services provided by
Amazon web services.
The first category would be
between Amazon Redshift and Amazon
aurora or Amazon RDS. If our
requirements include managing Petabytes of data with high scalability, we can
opt for amazon Redshift as it provides fine data
warehousing solutions and facilitates the visualization tools easily.
The second category would be where we require high performance and have fewer data. We can decide between Amazon Aurora & Amazon RDS. However, their services differ significantly. Amazon aurora is an amazon product with better interoperability with other products like S3, EC2, and they will be significantly faster as they are designed to avail advantages of the amazon hardware, on the other hand, Amazon RDS is dependent on other Servers like SQL server or Oracle Server and requires licensing and have low performance compared with aurora. This choice is more management-specific and cost dependent. Based on the above scenarios we can choose the cloud database which will satisfy our needs best.
Although many
other factors like customer side management, costing, use cases, data access,
and manipulation rate will affect the final decision, the above suggestions can
act as a starter to choose or shortlist the options.
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