AWS Database Services: Uses and Benefits

Amazon Web Services offers a variety of AWS database options to its customers. Depending on the type of data and the client’s pricing limits, these diverse database types serve all users appropriately.

AWS Database is one of the services provided by Amazon Web Services. These databases are available in several variants. Each variant serves a different purpose. In this article, we will be discussing the types of AWS Database services and their associated uses.

The Need For Databases

A database is a collection of data that is stored digitally and can be accessed digitally. An example of a database will be employees record. With a good database, you can easily filter out the required information and easily perform complex calculations. Data becomes information through databases. AWS also provides database services.

AWS Databases serves even more specific purposes with the help of different types of databases. Over here we will discuss the different types of AWS Database.

Types of AWS Database Services

Depending upon the requirement, you can avail of AWS database services. There are 8 different AWS databases: Relational, Key-value, In-memory, Document, Wide column, Graph, Time series and Ledger. Let us discuss them in detail. 

1. Relational Database

This database uses SQL that allows data storage in interconnected tables. The simple structure of relational databases allows effective working with most data types. Since the data within such databases are interconnected, updates in one database are reflected in connected databases. Relational databases are highly secure as you can limit data access to specific users. 

AWS provides three variants of the relational database. These are Amazon Aurora, Amazon RDS and Amazon Redshift. 

For more information, you can also explore this article: Amazon Relational Database Service (RDS) 

Check out Relational database systems on Coursera

Amazon Aurora

Aurora is a MySQL and PostgreSQL-compatible cloud database. This AWS database is managed by RDS which automates the process of database setup, patching, backups and other time-consuming tasks such as hardware provisioning. It has a self-healing storage system that is auto-scalable up to 128 TB per database instance.

Amazon RDS

Amazon Relational Database Service provides resizable capacity while it automates administration tasks such as hardware provisioning, patching, backups and database setup. Amazon RDS is secure and allows faster performance. RDS provides six database engines including Amazon Aurora, MySQL, SQL Server MariaDB, Oracle database and PostgreSQL. You can easily migrate the existing database to RDS through AWS Database Migration Service. 

Amazon Redshift 

This AWS database uses SQL for analyzing structured and semi-structured data across operational databases, data warehouses and data lakes. This is done using AWS-designed hardware and ML to deliver price-effective performance. Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes using AWS-designed hardware and ML to deliver the best price-performance at any scale.

Redshift is capable of handling large data sets and database migrations. It allows 16 petabytes of data on the cluster and it can handle analytic workloads on big data datasets that are stored by the column-oriented DBMS principle. Amazon Redshift uses compression and parallel processing for decreasing command execution time. Due to the reduced command execution time, redshift can perform action on billion rows in one go. 

Check out AWS online course 

2. Key-value Database

A key-value database is a non-relational database that uses the key-value method for data storage. Key and value can be anything from a simple to a compound object. Data is stored as a collection of key-value pairs where the key is a unique identifier. Amazon DynamoDB is one such key-value database. 

Amazon DynamoDB

Amazon DynamaoDB is a NoSQL database. It is a serverless database that can run high-performance applications. Like others, DynamoDB is highly secured with its provision for continuous backups, in-memory caching, data export tools and automated multi-region replication. It can work with more than 10 trillion requests per day and it can overcome peaks in the excess of 20 million requests every second.

3. In-memory databases

In-memory databases are used by applications that need real-time access to the data. Data latency reduces to microseconds from the millisecond latency. There are two AWS databases that are in-memory. These are  Amazon ElastiCache and Amazon MemoryDB for Redis. 

Amazon ElastiCache

This AWS database is used for real-time use cases. It is capable of providing microsecond latency which accelerates the performance of databases and applications. It is also used for caching which leads to reduced pressure on the backend database. Amazon ElastiCache can store non-durable datasets in memory. 

Amazon MemoryDB for Redis

Amazon MemoryDB for Redis is a Redis-compatible database that can achieve microsecond read and single-digit millisecond write latency. It can provide Multi-AZ durability for modern applications. There is no need for underlying infrastructure and cache management. This AWS database uses flexible data structures and APIs of Redis to make development easy and agile. It can be used as a primary database.

4. Document Database

The document database can store and query semistructured data such as JSON-like documents. It is a developer-friendly database since it can store and query data using the document model format used in application code.

Amazon DocumentDB

This AWS database allows flexible indexing and powerful ad hoc queries. The Amazon DocumentDB can support millions of document read requests every second by scaling storage independently. It can achieve extreme durability with automatic replication, strict network isolation and continuous backup.

5. Wide column databases

Wide column/column family databases is a NoSQL database that is used for storing enormous amounts of data that can be collected. Its architecture uses sparse matrix, multi-dimensional mapping including row-value, column-value, and timestamp in a tabular format that is meant for massive scalability. Amazon Keyspaces is one such wide column database.

Amazon Keyspaces

It is Apache Cassandra compatible AWS database service on which you can run your Cassandra workloads on AWS. There is no need to install, maintain and operate the software. Amazon Keyspaces is serverless so there is no need for managing or patching servers. You can continuously back up the table data using point-in-time recovery.  

Read more on The Complete SQL Course on Udemy

6. Graph Database 

Graph databases are used for applications that require navigation and querying millions of relationships among highly connected graph datasets. Amazon Neptune is a type of Graph Database.

Amazon Neptune

Amazon Neptune allows you to build and run applications that are based on large interconnected data sets. It can store large collections of relationship data with lower latency access. RDF, Gremlin and SPARQL are some of the graph models and languages supported by Neptune. This AWS database is capable of executing more than 100,000 queries every second.

7. Time-Series Database

Time-series databases can efficiently collect, synthesize as well as derive insights from the data that changes over time and with queries spanning time intervals.

Amazon Timestream

Amazon Timestream is an AWS database that is used for operational and IoT applications. Timestream can store trillions of events every day. This database keeps recent data in memory and moves historical data to the cost-optimized storage tier. You can access the present and historical data using its purpose-built query engine. You can identify patterns and trends in data in almost real-time. 

8. Ledger Database

This database provides a centralized and trusted authority for maintaining scalable, immutable and cryptographically verifiable records of transactions for every application. Amazon Ledger Database Services (QLDB) is a type of ledger database. 

Amazon Ledger Database Services (QLDB) 

With Amazon QLDB, you can create accurate records and keep track of all transactions. With this database, the application becomes resilient against data manipulation. Data in Amazon QLDB is written to an append-only journal, providing the developer with full data lineage. Moreover, data in Amazon QLDB’s journal is immutable and verifiable, meaning you can trust the data in your ledger.

In this article, we have discussed different types of AWS database services. It is important to go through all features and benefits of the database types before choosing one for your requirements.

FAQs

Q1.  What functionality does Amazon QLDB support?

Ans. Amazon QLDB supports transactions with familiar SQL-like API, ACID semantics, and flexible document data model. It also provides a complete history of application data changes.

Q2. Explain high availability in Amazon QLDB.

Ans. Amazon QLDB replicates multiple copies of the ledger across availability zones in a region. 

Q3.  What is time-series data?

Ans. Time series data is a sequence of data points that have been recorded over a time interval to measure events that change over time. This data is used for deriving insights into the performance of an application, detection of anomalies, and for identifying optimization opportunities. Time series data is generated in extremely high volumes from multiple sources. 

Q4: How can I send data to the Amazon Timestream?

Ans. You can send the data to Amazon Timestream by using data collection services such as AWS IoT Core, Telegraf,  through the AWS SDKs and  Amazon Kinesis Data Analytics for Apache Flink.

Q5. What is Redis?

Ans. Redis is an in-memory, open-source and key-value data store that utilizes flexible data structures such as lists, sets, strings, sorted sets, hashes, etc.  It offers an ultra-fast performance and high throughput. 

Q6. Is MemoryDB compatible with Redis?

Ans. Yes. MemoryDB is compatible with Redis. It supports the same set of Redis data types, commands and parameters. You can use application code, clients, and tools with MemoryDB that you use with Redis. MemoryDB supports all Redis data types including sets, strings, lists, sorted sets, hashes, hyperlogs, streams and bitmaps. MemoryDB supports more than 200 Redis commands except for Redis admin commands.

Q7. Which are the best database available in AWS?

Ans. The following are the best database options including DocumentDB, DynamoDB, Neptune and Quantum Ledger Database.

Q8. Why is RDS expensive?

Ans. AWS RDS is expensive since it is a managed service that maintains database servers for you. It is offered on Demand meaning you will pay exactly for the time till your servers are running. RDS supports hourly billing. When you purchase the RDS Reserved capacity, you will have to commit for a 1-year or 3-year period.