Handling enterprise-grade databases with AWS
For large companies or those with massive data analytics needs, there are times when basic cloud computing services just won’t help. The open-source options might be too unreliable or not fast enough, the on-premise alternatives require too much maintenance, or there are just too many complex variables for an internal IT staff to worry about.
Amazon Aurora is a cutting edge relational database that was built for the cloud and has the computing prowess to keep up with the most performance-driven data analytics projects. While a normal cloud database can run using open source options (including the one from Amazon called RDS or the Relational Database Service), Aurora is a major leap forward because it is essentially an enterprise-grade relational database that runs in the cloud, yet it still provides the same intuitive interface of Amazon RDS (and in fact runs on top of RDS).
A relational database for enterprise use is a different beast from a normal relational database. The tables are far more complex, but most importantly there is a need for the exceptional speed, reliability, and security that Aurora provides. A pharmaceutical company may be creating a new prescription drug and there’s a need to develop it quickly. A government entity may be doing Big Data analytics on a new citywide infrastructure change, such as replacing bridges. An automaker may need to run analytics on the materials used in a new electric vehicle that will need to meet government standards yet be light enough for a better MPG rating.
One thing is clear: The needs are much greater than for normal cloud computing services. In some cases, a company may have a need for up to 64 TB of data storage per database instance or for continuous backup of all data which means there is little margin for error. The reliability needs might be for 99.99% up-time availability. When the Big Data project is related to new drug discovery, the safety of human drivers in a new car, or related to bridges in a city, compromise is not an option.
Interestingly, while the Amazon Aurora service is enterprise-grade in terms of performance, scaling, reliability, and security, it is not enterprise-grade in terms of cost. Companies pay a fraction of the cost for this service compared to what they would pay for an on-premise solution or for a competing product that requires a minimum number of instances.
In terms of speed, Amazon stats that Aurora is up to five times faster than a normal MySQL or PostgreSQL database instance but it one-tenth the cost.
Benefits of Amazon Aurora
Even with all of the power and performance, the three key benefits to using Aurora are related to simplicity, cost, and security. As mentioned, Aurora runs on top of Amazon RDS so it is the same web interface you might already be using. The heavy lifting and complexity when it comes to an enterprise-grade database in the cloud is usually related to the provisioning, maintenance, scaling, patching, backups, and updating that’s required, yet RDS handles all of that. For your staff, the initial setup looks and functions similar to an open-source database on RDS.
And, the database instances are all self-healing, auto-scaling, and fault-tolerant thanks to the connection between Aurora and Amazon S3 (Simple Storage Service), the object storage platform that works in tandem with the enterprise relational database instances.
Cost plays an important role here because normally scaling up your Big Data project would require an enormous investment in the infrastructure. With Amazon Aurora, it’s possible to add up to 15 read replicas per instance simply by choosing that option. There is no infrastructure management, planning or development involved to achieve this high performance throughout. As you scale up, Amazon S3 also scales to meet the storage needs, up to 64 TB per instance.
Scaling down is just as important — companies don’t lose the investment they made to handle the biggest projects while it sits idle waiting for the next massive deployment.
Endpoint security is a critical component of any Big Data project, especially in the age of data breaches and exposed user information that is often sold on the Dark Web. If a company like Ford is experimenting with Big Data projects with materials or components inside a new and unannounced vehicle, and the data hacked and exposed, it can be a major setback.
Aurora uses technologies like network isolation, encryption at rest using key encryption, and encryption during data transmission using SSL. It’s also important to note that, since Amazon Aurora uses S3 for storage, that service is also highly secure — the underlying data used for the Big Data project is archived automatically in the same cluster. There is little opportunity for data leaks when the database itself and the storage are so closely linked.
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