The cloud data platform is great for eliminating the burden of scaling up IT infrastructure in an organization. However, the way these platforms achieve that differs. Different architectural and deployment strategies affect some aspects of a business in terms of cost, reliability, and resources. From a cloud agnostic platform to on-demand architecture, cluster data architecture, and more, here are some key considerations to help you choose the right cloud analytics infrastructure for your business.
The four key considerations to help you choose the right cloud analytics infrastructure are:
- Cloud offerings
There are advantages to choosing both proprietary and native cloud offerings. Proprietary offerings offer a much deeper level of integration with the vendor’s tooling ecosystem than native cloud offerings. However, doing that makes your business more dependent on the vendor’s cloud infrastructure.
On the other hand, native cloud offerings may reduce deployment times and also offer cost savings. But they come with tighter integrations, unlike the proprietary cloud offerings.
Query workloads at your business can help determine what type of architecture you should go for.
Pre-allocated cluster architecture is best when your business has a consistent workload but is not recommended if the workloads are unpredictable. You don’t want to end up paying for a larger cluster during non-peak times after you’ve already utilized the pre-allocated cluster.
In situations where a business has an inconsistent or unpredictable workload, it is crucial to stick with a serverless architecture.
The type of architecture you choose will impact the costs incurred in an organization. If your organization prefers cost predictability, then pre-allocated cluster architecture can offer you peace of mind. The cluster remains open for use for that allocated time.
There is no cost predictability with serverless architecture since they charge according to the time but the cluster is only active when in use. When there are no workloads, the cluster shuts down. The biggest advantage here is that the business saves money as they only pay for what they use.
The organization’s needs will help determine which one to choose between these. If you’re looking for cost predictability rather than cost optimization, it is best to choose a pre-allocated cluster architecture and vice versa.
The workload for business intelligence analytics is continuous and always ongoing, while the workload for data science is one-off.
Platforms that run well with data warehousing are great at collecting data fast and conducting quick table scans. Platforms that run well with data lake architectures offer business operations directly on the data stored in the cloud.
Cloud data platforms that are optimized for data warehouse workloads are great for businesses that have more intense business intelligence analytics.
Businesses that are more reliant on data science and have intense data science analytics should opt for lake house architecture.
It is crucial for you to match the cloud platform architecture and the strategies for deployment with a business’s operational needs, skill set, and use cases.