Struggling with cluttered data? You need a better database.

5,400. That is how many databases have migrated onto AWS as of February 2018. 

A Database is essentially a collection of information that can be easily accessed, updated and if managed properly, can save loads of time and effort. AWS offers a wide range of solutions that are fully managed and can be launched in moments with the help of a few clicks. A developer builds applications on top of a managed database that drastically speeds up the process of provisioning a database server.

Databases have evolved since their inception in the 1960s, beginning with hierarchical and network databases, then progressed in the 1980s with object-oriented databases, and today with SQL, NoSQL, and cloud databases. 

About Amazon DynamoDB

Amazon’s DynamoDB is a reliable service that provides a managed database service along with NoSQL. It comes with a feature of a managed in-memory caching as a service and a completely managed petabyte-scale data warehouse service. 

Pertaining to the challenges of modern data and the numerous choices available for databases, a company needs to pay a lot of attention when choosing the correct nature of the database. 

Developers today are breaking their complex applications into smaller pieces and finding the best use case to solve real-time pragmatic challenges. One of the most important services they need is, to be able to use multiple databases and data models within the same application.


From a developers’ point of view, there are two things that  are needed in their application:

  •  A well-architected framework
  •  An effectively scalable application

How exactly does one decide on choosing “the correct database”?

  1. The foremost step is to understand the usage of your database as per the scope of your application.
  2. Next thing is to identify the data needs of your solution.
  3. Breaking down your solution into independent software services in the next important step while finding the correct database for your product. 
  4. Grouping your services further help in pointing your data to the right database. 
  5. The last and most important step is to evaluate your cost and resource efficiency.  

Let’s talk about a theory that will help us get through these troubles. 

THE CAP THEORY

This theory is the reason why all the database options today exist. CAP stands for consistency, availability and partition tolerance. The principle of this theory is that there is always a tradeoff between these three factors while choosing a correct database. 

  • Consistency means that any read request will return the most recent write.
  • Availability means that the non-responding node must respond in a reasonable amount of time.
  • Partition Tolerance means that the system will continue to operate despite network or node failures.

Note: According to CAP, at any given time, only two of these 3 requirements can be satisfied at once.

While Relational Databases traditionally feature strong consistency and high availability at the expense of partition tolerance. They are optimized for writes.

On the other hand, Non-relational Databases have been developed to serve Availability and Partition Tolerance or Consistency and Partition Tolerance. They are optimized for reads.

Developers today have the choice of relational, key-value, document, graph, in-memory, and search databases, each of which solve a specific problem or group of problems.

Relational Databases

Amazon Relational Database Services (RDS)

RDS helps users by providing additional services like lining up, operation and scaling an on-line database within the cloud

RDS allows developers to concentrate on their applications, instead of their DB, and focus on performance, availability, security, and compatibility.

RDS is also fully responsible for managing Amazon Aurora. Surprisingly, it remains to be the fastest-growing service in AWS history. 

Amazon Aurora is a MySQL and PostgreSQL compatible relational database engine. 

Amazon Aurora is up to five times faster than standard MySQL databases and three times faster than standard PostgreSQL databases.

It provides the security, availability, and reliability of commercial databases at 1/10th the cost.

Amazon DynamoDB

When Amazon wanted to push their limits from a leading commercial database and was unable to sustain the scalability, availability, and performance needs their growing Amazon.com  business demanded. They decided to build a nonrelational database that scales out beyond the limits of a relational database.

Amazon found that almost 70% of their operations were key-value lookups, they realized that these access patterns could be served better with a different kind of database.

All this led to building up of Amazon DynamoDB. 

DynamoDB is a perfect example of a key-value and Document type database. 

A key-value database offers features that are highly partitionable and also allow unmatchable horizontal scaling as compared to other databases. The consistent performance accounts for the reason why the Snapchat Stories feature, that includes Snapchat’s largest storage write workload, moved to DynamoDB.

The data in an application tier is typically represented as a JSON Document. This makes document databases intuitive for developers to use. 

Tinder, for example, uses a flexible schema model of  DynamoDB to achieve efficiency.

Amazon Neptune

Use cases of social networking, recommendation engines, fraud detection, and knowledge graphs involve management in graph databases.

A graph database serves the purpose of building and running applications that work in highly connected datasets. 

Amazon Neptune is one of the fastest, most reliable, fully-managed graph databases. It is optimized for storing billions of relationships and querying the graph within milliseconds. Because of its purpose-built, high-performance graph database engine. 

Recently Neptune users started building knowledge graphs, making in-game offer recommendations, and detecting fraud.

Amazon ElastiCache

There are many use cases that require microsecond response times and can have large spikes in traffic coming at any time such as leaderboards, session stores, and real-time analytics. 

These are the in-memory databases.

Amazon ElastiCache supports two open-source in-memory caching engines namely Redis and Memcached.

Redis which is a fast, open-source, in-memory data store, and cache that delivers the ease-of-use along with availability, reliability, and performance suitable for the most demanding applications. All these features make it an ideal candidate to power high-performance use cases such as web, mobile apps, gaming, ad-tech, and IoT. 

Memcached, on the other hand, is a  widely adopted memory object caching system

Using Memcached all the popular tools that we use today will work seamlessly with the service.

Amazon Elasticache along with Redis and Memcached helped in building workloads for McDonald’s that could not have been built with the help of single disk-based data stores. 

Amazon Elasticsearch is a powerful, high-performance search engine for full-text search use cases. It has been built for providing near real-time visualizations and analytics of machine-generated data by indexing, aggregating, and searching semi-structured logs and metrics. 

It is a search-based database where many applications can output logs to help developers troubleshoot issues. 

Expedia is an example which uses more than 150 Amazon ES domain for a variety of mission-critical use cases.  

Amazon Timestream

Another database service that has been driven by the rise of IoT devices, IT systems, and smart industrial machines, time-series data is popularly known as Amazon Timestream.

It is a fast, scalable, and fully managed time-series database service for IoT and operational applications that had made it easy to store and analyze trillions of events per day at 1/10th the cost of relational databases.

This database efficiently stores and processes this data by time intervals.

Another feature of Timestream is that it does not have servers to manage and along with automates rollups, retention, tiering, and compression of data, one can manage data at the lowest possible cost.

Features of an Amazon Database: 

  • Amazon databases are easy to administer as Amazon RDS makes it simple to travel from project conception to deployment. 
  • Accessing the capabilities of a ready-on-production database, one can easily use AWS Management Console, the AWS RDS Command-Line Interface, or easy API calls.
  • Scalability has been another feature that can be achieved for your database with solely a couple of mouse clicks or an API decision, typically within no period of time.
  • Amazon provides database services that have enhanced responsibility for crucial production databases, as well as automatic backups, information snapshots, and automatic host replacement.
  • Amazon databases have been built to withstand the foremost stringent information applications. 
  • Amazon relational database services have made it simple to regulate network access to your information which has been providing encryption at rest and encryption in transit.
  • AWS provides very low rates just for the resources you truly consume. Additionally, one benefits from the choice of On-Demand evaluation.

While the mismanagement of any business-critical apps can end badly, not giving Oracle, SQL Server, or NoSQL databases the support that powers them, can turn out to be particularly devastating.

A managed service provider is great support arm for organizations of all sizes that prefer a full team of database experts, rather than one person, managing their databases.

For organizations looking to free up their Internal Senior Database Administrators to focus on more strategic initiatives, an MSP can make that possible.

To know more about MSP, check out our success stories and how we manage our resources for our clients.