Global businesses have high expectations from Big data, which can uncover even the most safely hidden patterns. With that in mind, organizations have moved quickly to new data technologies to get a sustainable competitive edge.
However, legacy infrastructures are helpless in the face of technological advancements such as data lakes and analytics platforms. That is why a growing number of companies are aiming skyward - up into the cloud.
Today, cloud computing accounts for more than 300 billion U.S. dollars in revenue and is showing very few signs of slowing down. It means that a cloud data architecture is imperative in the digital economy to provide the speed and agility as well as quickly access and unify data.
Although traditional database architecture is still used to handle the tight integration of similar structured data types, the diversity in the stored data cripples the local parameters. Also, on-premise architecture is quite expensive to build and maintain, let alone the fact that it just cannot keep up with the speed and flexibility required for modern datasets in the current era of big data.
Therefore, a cloud-based data warehouse architecture is the most efficient use of data warehouse resources. Cloud data architectures include the rules, models, and policies that describe the way data is gathered and stored in the cloud within an organization.
Unlike classic architectures, cloud ones provide the following benefits:
All flavors of cloud computing architecture contain two basic components - frontend and backend architecture with cloud infrastructure.
The front-end architecture, also known as the client-facing side, includes everything visible to the end-user - from UI to client infrastructure.
The backend part is responsible for data storage and management, servers, OS, security, as well as other behind-the-scenes procedures. This part of your cloud architecture also contains security protocols to keep the cloud service safe.
To create cloud computing architecture, both back- and front-end architecture must be connected within a network and integrated.
Any company has three different ways to migrate its data into the cloud:
You cannot build a reliable data architecture overnight. Just like the house construction, all elements should be carefully mounted on top of each other to form a seamless body. That is why data architects have to leverage a combo of time-tested practices when building and reaping the perks of a cloud data architecture.
Also, you won’t find a ‘one-size-fits-all’ way to construct secure and efficient cloud computing architecture. The mileage can vary so does the business use cases for each organization. However, there are some general tips and tricks to set you up for success.
Understand your Cloud architecture
It sounds like a no-brainer, yet not all organizations are aware of the different types of cloud services.
In particular, all cloud architectures can be divided into four groups:
The choice of your cloud model should depend on the unique requirements of your organization.
Don’t stick to one cloud storage regimen
Choosing on storage type doesn’t bode well for all databases. Most cloud vendors provide a wide choice of storage options. Thus, AWS offers Elastic File System, Simple Storage (S3), Elastic Block Storage, and a Glacier archive backup and storage gateway. Combining various alternatives will bring the perfect price-value ratio and functional benefits for your databases.
Within the cloud continuum, you need a secure way to access your data. Therefore, while setting up a cloud computing architecture from scratch, you should prioritize reliable methods of access. Antivirus programs, encryption controls, and other data security functionality will help keep your information safe.
Robust security controls for your architecture and servers will ensure a seamless back-and-forth of your data without other people being at risk of breaking into the data. In most cases, it’s a firewall that guards your cloud server.
Performance and scale-oriented design
Among other things, your architecture design should be able to adjust to evolving changes. The latter usually comes in the form of regular or sudden peaks of workloads.
Therefore, all pieces of infrastructure should be expanded to handle the increased load. In this case, vertical and horizontal scaling is the only type of scalability that applies to cloud computing. The first one is easier since it can be done by adding more RAM, using faster storage, or more powerful processors (CPUs).
Easy-to-use design and data processing
Your cloud infrastructure will become user-friendly, provided you wrap it up in a user-centered frontend with robust UX and user interface. When you pair it with performant data processing and storage on the backend, you'll have a high-quality cloud computing solution.
Clear business case
Companies shouldn’t dive into cloud agreements without preliminary consideration. Each data architecture targets a certain business problem. Hence the selected cloud components must align with your business value.
Experimenting and testing
The catalog of data architecture patterns is massive. From sharding to event sourcing to asynchronous request-reply, they define how the data can be structured, indexed, and made accessible for searching. Sometimes it’s easier and less expensive to swap your data pattern for another one if things don't work out.
Don’t fear the unstructured data
Some cloud services are guilty of creating a separate silo of unstructured data storage. As a result, organizations end up with two data storages - the on-premises one and cloud storage. However, both on-premises and cloud solutions contain several benefits for taming unstructured data.
Thus if you migrate it in the cloud, cloud tools can unearth hidden relationships and minimize the dread linked with defining data cubes and other structures.
Companies with on-premises data infrastructure are bogged down by system complexity, silos, and the limited capabilities of legacy architectures. That is why a growing number of organizations are building their data architectures in the cloud.
Cloud data architecture is essential when the company has massive amounts of data to comb through. However, before mulling over the transition, make sure you have a clear business objective to get ahead of changing markets and create a data migration plan.