Organizations rely heavily on databases today compared to years ago when most of the data were stored in excel documents alone. whether you need a database to store either your OLAP or OLTP transactions or need a database to store inventory and financial transactions for your e-commerce store you will require a database that can scale up or down instantly and is reliable, secure and cost-effective.
According to AWS, 94 % of their customers use 10+ databases and analytics solutions. Now the question arises which databases are for which use case.? The two major database classes today are relational vs non-relational databases or SQL vs NoSQL databases.
In this post, we will look into AWS’s complete set of non-relational purpose-built databases, their use cases and properties.
Advantages of serverless.
- Scalable
- Lower cost
- Easier to use
- Reliable
Why do users choose serverless
- Agility
- Performance
- Cost
- security
- Dynamo dB– is a fast and flexible NoSQL database service for applications that need consistent single-digit millisecond latency at scale. It’s serverless meaning you don’t need to manage servers. DynamoDB offers built-in security, continuous backups, automated multi-Region replication, in-memory caching, and data import and export tools.
Dynamo dB is multi-regional, and resilient with a 99.999% SLA.
Uses cases of Dynamo DB.
Delivers seamless retail experiences- dynamo dB processed 105 million requests a second for 2022 amazon prime day.
Scale-out gaming platforms-dynamo dB helps out game providers by storing player data and session history to help out the developers focus on security patches and innovation.
2. Document DB-serverless fully managed document database that can scale up to 64TiB of data per cluster. Recently AWS launched document dB elastic clusters that scale document workloads of virtually any size and scale. With document dB elastic clusters, you get:
- Elastically scale to millions of writes and reads per sec with petabytes of storage
- Zero impact on app availability or performance
- Continually monitor workloads and with metrics provided via amazon cloud.
3. Amazon Neptune– fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. Amazon Neptune core is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency.
amazon neptune free tier
Amazon Neptune comes with 750hrs of t3. medium or t4g. medium instance free for 30 days.
Neptune supports acid transactions automated backups, snapshots and encryption both at rest and in transit.
One use case of Neptune is a product recommendation engine in an e-commerce website.
Another use is Neptune can be used in machine learning using graph neural networks to improve the accuracy of most graphs by over 50 % when compared to non-graph use cases.
It can also be used in social networks to prioritize recommendations to users based on their likes family members on the site or friends who live close to them.
4. amazon Elasticache-fully managed microsecond in-memory caching service. Elasticache can be used for caching which accelerates application and database performance. Elasticache supports 3 engines: Elasticache for Redis or Memcached. you can use either depending on your use case and application requirements.
Elasticache for Redis is a super fast in-memory data store that provides sub-millisecond latency to power internet-scale real-time applications. it is secure fully managed and easily scalable. it is used for real-time transactions and analytical processing.
Elascticache Memcached is a Memcached compatible in-memory key value that can be used as a cache or datastore.
5. Amazon time stream–serverless time series database that makes it easier to store trillions of events data faster and at 1/10th the cost of relational databases. Amazon Timestream saves you time and cost in managing the lifecycle of time series data, its purpose-built query engine lets you access and analyzes recent and historical data together with a single query. It has the following features:
- Data storage tiering
- Purpose-built adaptive query engine.
- Built-in time-series analytics
- Data is encrypted both at rest and in transit.
- Integrates with popular data collection visualization and machine learning tools.
Amazon timestream is currently not available in all AWS regions so check if your region is covered before considering it.
6. Amazon Key Spaces –wide column Cassandra compatible database service. it is scalable highly available and above all serverless. Amazon key spaces ensure 4 nines of availability SLA within a region.
7. Amazon MemoryDB. for Redis is a Redis-compatible, durable, in-memory database service that delivers ultra-fast performance. it is built for modern applications with microservice architectures.
Amazon MemoryDB is a high-performance database for microservice applications eliminating the need to manage a cache and database.
It has the following features. Fully managed and thus very simple to use.
- Supports encryption through AWS (key management service) KMS
- Multi-AZ availability-MemoryDB stores data across multiple AZs to ensure fast recovery and restart.
- Can scale both horizontally and vertically.
- Ultra-high performance- can support up to 160 million requests per second
some of the companies that use this database include Netflix, Disney, capital one, and drop box among many others.
