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 an 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, providing single-digit milliseconds performance.
Success is a matter of choices, and every choice you make, makes you!
Amlgo Labs with its 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 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 less 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, and 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.