By Prangya Pandab - October 07, 2022 4 Mins Read
Businesses should carefully assess whether cloud databases and analytics solutions effectively address their current needs while balancing complexity, cost, flexibility, and performance.
The database a company chooses has a substantial impact on productivity, costs, and business value if they intend to migrate its analytical workload to a single or multi-cloud vendor, on-premises, or a hybrid architecture.
Here are some essential questions IT teams should ask before settling on a cloud database, given the abundance of databases and analytics providers available on the market.
Some SaaS platforms demand that all data be loaded into a particular cloud. This prevents the consumer from readily switching to a different cloud or taking advantage of more affordable computing when it becomes available, locking them into a single solution. Cloud-native may mean cloud-only, and a SaaS solution may not be able to execute workloads elsewhere. Businesses are essentially forced to load their data onto a single cloud and analyze it with a single engine. Here, billing convenience could be beneficial. However, the company will only be able to use one kind of cloud deployment.
Enterprises increasingly use external data and data lakes, but analytics solutions can differ substantially in handling data storage and workloads. Businesses need access to data from the database and other sources. Even if the data isn’t added to the cloud database, they should be able to provide users with access to other data sources. The amount of time it takes to load the data onto the database and the cost a business will incur while it is stored in the cloud can significantly impact it. Most modern systems integrate the data lake and the data warehouse for analytics; therefore, storing all data in a single database type is problematic.
Cloud-only databases typically provide node-based optimization when users are attempting to fix slow-running queries. Many cloud-based systems will add additional nodes for more compute power if the queries run slowly. However, analytical workloads are not universal – the performance of databases can be affected by quarterly reports, poorly written queries, a successful marketing campaign that generates more data, and other factors.
It is crucial to be aware of the alternatives for accelerating queries because of this. Businesses need to look for systems with massively parallel architecture, workload management, node scaling, query optimization, and separation of storage and compute.
When shifting analytics to the cloud, costs can easily get out of hand, and businesses can be shackled by long-term agreements. To avoid unpleasant surprises at the end of the month, companies must ensure that the cloud database enables users to spin down compute when it is not in use and can set precise spending limitations. IT teams should be aware of the auto-scaling capabilities of the database when complex or time-consuming queries are conducted or when a lot of concurrent workloads are placed on the system. If the cloud database automatically spins up more nodes, those extra nodes are automatically charged in monthly segments.
It is also essential to have shared storage since it enables teams to use the same data without duplicating it. More copies mean more storage space, and this costs more. Not to mention, many providers charge a per-megabyte egress fee for data removed from their platform. Platforms that would charge businesses to obtain their data back should be avoided.
The truth is that the pace of change in technology, business, and regulatory environment makes it impossible to foresee the analytics and storage needs of the future. Today, companies may shift data to the cloud. Tomorrow, they may shift it back on-prem, or they would prefer to use both. This is precisely why companies should select scalable, flexible, and future-ready databases.
Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.
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