In most cases, storing data in a relational format makes perfect sense. However, not every data set is suited to the strictly enforced structure of an RDBMS. In addition, some data sets have specific requirements by the companies and organizations that use them. Stepping in to fill this void, AWS1 (Amazon Web Services) has designed and developed a suite of cloud-based database offerings that are purpose-built to satisfy modern data requirements.
When it comes to databases, AWS has you covered with offerings to satisfy the needs of basic to the most complex requirements. Starting with EC2 (elastic compute cloud), AWS allows customers to move into the cloud with a familiar interface that they’ve had in years past. The EC2 service essentially will enable customers to build-your-own server. Customers have complete control over this ” server-as-a-service ” from the operating system to CPU, memory, and networking options, customers have complete control over this “server-as-a-service”. For those more hesitant about giving up the fine-tune control of their servers, EC2 will allow customers to deploy database software from virtually any vendor while bringing along their existing licensing agreements.
Despite EC2 being a fantastic service, it does not take advantage of the best AWS offers for databases. RDS, or Relational Data Service, is a service that gives customers the freedom to choose from traditional database offerings such as PostgreSQL, MySQL, MariaDB, Oracle and SQL Server. However, if you’re looking for a database that offers enhanced performance and availability at a fraction of the price – AWS offers its own Aurora database. One of the most cost-effective solutions, Aurora offers the performance of a commercial database at 1/10th the cost. In addition, it is a fully managed service – meaning traditional database management tasks such as hardware provisioning, software patching, setup, configuration, and backups are all taken care of by AWS. Alongside Aurora and RDS, Redshift is another service unique to AWS that is a petabyte-scale, custom-tailored data warehouse designed to analyze massive data sets, both structured and unstructured. It perfectly complements businesses with several operational databases and sources, providing the tools necessary to aggregate and analyze their data for enhanced business intelligence and insights.
What about the RDBMS alternatives?
Figure 1. AWS Database Offerings (https://aws.amazon.com/products/databases/)
In addition to offering numerous traditional and modern solutions for typical relational data sets, AWS has an extensive offering of databases that are purpose-built to serve alternative and often more complex needs. One of those needs are NoSQL (not only SQL) databases. These non-tabular databases store data differently than a relational database. AWS offers several flavors of NoSQL databases including Key-Value Pair, Document, Wide-Column, Graph, and more (See Figure 1 above). NoSQL databases were originally created to fulfill the needs of data sets that are constantly evolving and fluctuating. Modern applications require more scalable and dynamic methods of storing data that don’t always fit into the rigid structure that an RDBMS demands. The type of data being stored should drive the solution.
Rather than forcing an RDBMS to store documents, AWS offers DocumentDB. It is perfectly suited for content management and business workflows that rely heavily on varying types of document files. Trying to do this with a traditional relational database is not recommended and can lead to huge storage demands.
Amazon ElastiCache is another great example of AWS’s focus on building workload-specific solutions. Data is not always required to be kept in long-term constructs. Often, data needs to be momentarily held in a dynamic and rapid fashion, to be used for its purpose and then released. In-Memory databases like Amazon ElastiCache offer the speed and scalability necessary to satisfy the intense performance requirements of applications like video games, where users interact in real-time, without sacrificing customer experience.
Some data sets have strict management requirements and need to have every action and change auditable. Amazon Ledger Database Services (QLDB) maintains an immutable, cryptographically verifiable log of data changes. Use cases for ledger databases span across many industries from finance and being able to reconcile transactions to records management and even tracking a supply chain to ensure goods aren’t lost or stolen. These are just some of the many offerings that can take your databases to the next level.
Modern data types demand purpose-built data solutions. Competition between cloud vendors is providing a continuous stream of these solutions with undeniable benefits. Consider cloud for your next data-centered user story.
Database Administrator at Definitive Logic
1At Definitive Logic, we are vendor-neutral. We use a wide range of market-leading technologies to meet our customers’ needs. When we share real-life examples, we’ll often name a specific product or vendor. These mentions aren’t an endorsement. They just provide context. In this article, I name AWS because of its prominent role in the market.
Suggested content for you
In the first article in this series, we talked about the ways recommender engines can be leveraged to improve citizen service. In this article we’ll...
In the vigorous competition of the free market, online platforms have made innovation into an operational discipline. For these firms, being...
Acquisition innovation is critical to mission success. To attract revolutionary commercial companies that normally avoid the federal government,...