Advantages of DBaaS
DBaaS offers some advantages over traditional methods to deploy database systems, including the following:
- Abstract physical architecture from your organization – The vendor and its partners provide the hardware needed for running the workload, removing the need for your organization to maintain servers.
- Reduce hardware and database software upgrade costs – As the infrastructure is no longer on-premises, organizations no longer have to invest in database servers or plan time consuming hardware upgrades.
- Reduce capital expenditures – As-a-Service software allows an organization to move some expenses from capital expenditures to operating expenditures. Doing so makes future spending more predictable, lowers capital costs, and reduces your need to spend money on hardware and software.
- Streamline database management – DBaaS providers handle many routine database management and administration tasks, and this too can lower operating costs.
Disadvantages of DBaaS
There are also potential disadvantages with DBaaS compared to on-premises databases.
- Savings are not automatic. Some companies have run the numbers and found DBaaS significantly more expensive for specific workloads. Savings seems to be best achieved on ephemeral workloads rather than persistent ones, as the cost-benefit of DBaaS is amplified when analytical workloads are not running.
- Lack of control. With managed databases, an organization’s IT team may not have complete access to a server’s features, as they too, are abstracted away. Users must rely on the cloud provider to fully manage the infrastructure effectively.
- Reliance on fast internet. If an organization’s internet connection is weak or experiences outages, the organization won’t have access to its database until the problem is repaired. In addition, organizations may experience slow query times due to internet speed.
- Security. Some buyers of IT prefer to have direct influence over the safety of the servers running its databases. Although public cloud security is known to be fairly strong, the vendor is in charge of securing the database platform and underlying infrastructure. Security is also abstracted away on some level.
Categories of DBaaS
What to look for in a DBaaS?
Beyond the types of DBaaS, there are other major differences between DBaaS providers, including:
DBaaS Deployment Options
Does your DBaaS also offer non-SaaS deployment? Some DBaaS vendors require you to lock into a specific storage place in one particular cloud. This locks the customer into one cloud, not allowing the freedom to move to a different cloud easily or take advantage of lower-cost cloud computing when available. Some vendors offer no solution for on-premises analytics or deploying in Kubernetes. Weed out vendors who don’t support all of your deployment needs.
Does your solution offer a license that allows you to easily move between multiple clouds or on-premises, or are separate licenses required for each deployment? What are the costs to maintain DEV, TEST, BACKUP and PRODUCTION? Take a look at total costs to understand which vendors will meet your needs.
Data Lake Capability
Do you often have locally-stored Parquet, Orc, AVRO, JSON or TEXT files that you need to incorporate into your analytics? When choosing your DBaaS vendor, explore how it can bring together a data lake’s scale and economics with the predictability and reproducibility as a data warehouse. In addition, consider how well your solution understands external table workloads and how much data movement is required.
Ability to Optimize
Does your DBaaS operate in a limited compute package? All analytics is not the same, nor should it be considered the same. Make sure that the database that you select has options to properly manage all types of workloads and service level expectations. Solutions that do node-based optimization (simply adding generic nodes when your workload calls for it) may cause you to miss out on methods to keep your cloud costs lower while improving query performance at the same time. The capability to use specialized nodes, and the ability to tune slow queries is paramount.
Depth of Analytics
Can you leverage your DBaaS for more than just descriptive analytics? Today’s data-centric companies have analytical needs that reach beyond standard SQL databases. For example, some workloads call for advanced analytics like geospatial, or time-series function. Predictive analytics is becoming increasingly imperative to data science teams, so consider how machine learning is supported. Consider how your solution can support a wide range of analytical use cases and a wider team of professionals as your cloud database gains success in your organization.
What is Database as a Service (DBaaS)?
In this video, we highlight the advantages of DBaaS and what to look for in DBaaS.
Vertica and DBaaS
Vertica Accelerator is Vertica-as-a-Service (DBaaS) that delivers a unified, high-performance advanced analytics and machine learning platform with automated cloud setup and help with onboarding. It runs in your own AWS cloud account, with automation from the Vertica management plane. Vertica Accelerator is one of the deployment methods offered by the Vertica analytical database. Vertica also offers on-premises deployment, Kubernetes deployment, and more.
Vertica provides the flexibility of private and public cloud deployment – not just a proprietary cloud, but any cloud. Our database seamlessly connects on-premises environments to public clouds for a hybrid data cloud experience. By implementing hybrid cloud, you can increase flexibility, performance, and scalability. It offers you a way to maintain complete control of your data while leveraging modern cloud technologies.
Vertica Accelerator helps you create a strategy for more predictable pricing with our flexible deploy-anywhere license. It’s the best way to place workloads in the right place for price/performance and avoid single-point-of-failure scenarios.
With Vertica Accelerator, you can finally get machine learning into production. Vertica supports cluster-optimized ML algorithms, R, and Python. Data scientists and analysts can build their models using their preferred tools and languages, then leverage Vertica to power them on bigger data sets. In-database machine learning addresses every step in the ML process.
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Before I joined Vertica, I worked in the cloud services world, first for a reseller and then for a Cloud Services Provider (CSP). During my time in this space I […]