Businesses are increasingly virtualizing desktop applications, servers, and storage, so it should be no surprise that databases aren’t the exception. Virtualizing databases offers some undeniable advantages, such as less physical hardware, savings in energy, and simplifying database management.
But why virtualize a database in the first place? Picture this: An enterprise has a huge database that is shared among developers. If one developer makes a change in data, another developer thousands of miles away burns the midnight oil trying to figure out why the code is not working, only to find out the issue is not about programming.
So, what's to be done? The answer is database virtualization.
As the name implies, database virtualization decentralizes a shared database by acting as a representation of a concrete database. Taking a cue from the developer example, what this means is that each developer will get a unique copy of a given database, and any changes will be stored separately without creating any burden on the primary database.
So, when a developer queries the database, they are essentially interacting with the source database only to read information. If they attempt to modify the data, those changes will be stored separately instead of impacting the original data.
Database virtualization brings together two essential elements of DevOps -- speed and versatility. Here are a few of the benefits companies are realizing as they move to virtualization:
Reduced infrastructure costs: Database virtualization can help you avoid costly investments in extra servers, operating systems, power, application licenses, network switches, tools, and storage.
Less complex: As developers work with just one database image, scaling up or down becomes fast and simple, leading to less complexity.
Lower labor costs: Database virtualization makes a database administrator’s job much easier because it simplifies the backup process, allowing them to manage more databases at a time.
Optimum server utilization: As database virtualization decouples the data from processing, usage spikes can be shared across multiple nodes, leading to an optimal server utilization rate.
Service quality: Since the database isn’t being utilized centrally, data can move faster without downtime, resulting in improved service and performance.
Availability: Unlike physical or centralized databases, virtual database nodes can see all the data, which allows them to reduce unplanned downtime as processes can simply be moved to another server. This means less disruption and more availability.
Greater flexibility: With database virtualization, resources can be allocated and reallocated as per need.
Data quality: By avoiding replication, database virtualization helps in enhancing the data quality.
Despite all the benefits, you may encounter problems if you don’t consider key factors while implementing database virtualization. Here’s what you need to know:
Hardware: Though virtualized databases don’t require much physical infrastructure, they do need sufficient processing power. Any shortcomings here may lead to significant performance degradation.
Licenses: Before you transition from a physical to a virtual database, you must consider the environment and the number of instances and processors needed, to compare the license costs.
Skillset: Virtualized databases, like any new technology, might require additional skillsets be added to your team.
Accountability: Before taking a leap to database virtualization, fix accountability. Many database administrators have no idea how deep the virtualization layer is because usually it’s the job of IT administrators. When any issues with a virtual database occur, long delays in problem resolution will result if no one knows who is accountable for support, remediation, and oversight.
Here are some best practices you can implement to achieve successful database virtualization:
As more users demand different things and more layers and objects are added, the entire process can become complex. Data virtualization solutions need to evolve accordingly. If this doesn’t happen, the entire data virtualization development becomes less agile, less performant, and more challenging to manage.
It’s always advisable to socialize data virtualization concepts and capabilities before you get started, and leverage any standards, processes, data definitions, and business rules that have already been defined. Consulting with a data governance function, or creating one if you don’t have it, can help.
You must also generate usage guidelines for data virtualization technologies to be used in various scenarios. A single approach often doesn’t work for all; having some best practices in place definitely helps.
You must ensure that your data developers have the required skill set to operate virtual databases. They must be aware of data virtualization capabilities and should have basic training on the technology.
Since data virtualization can do many things -- deploy web services, query operational systems, provide integrated data for analysis -- organizations often struggle to determine who’s responsible for supporting the platform. It’s better to split the responsibility of specific tasks to each team so everyone is clear about what they need to do.
Instead of implementing everything at once, it’s always a good idea to take a phased approach to implement data virtualization. Start with abstracting the data sources first, then layer the BI applications on top, and gradually implement the more advanced federation capabilities of data virtualization.
Data virtualization is key to simplifying data analytics and digital transformation. Until recently, not only has there been a shortage of inexpensive data virtualization platforms, but the existing platforms have also been incredibly complex.
That all seems to be changing. Amazon Web Services (AWS) Athena, with its Presto engine, has received wide acceptability as a mature data lake solution on top of Amazon Simple Storage Service (S3). Now, the AWS Athena Query Federation (AFQ) can simplify connecting various data sources and allow Athena to be used for data virtualization. When Athena SQL is combined with AFQ Connectors, an organization can mix and match data from any source without needless duplication of data.
Trianz has built its own AFQ Connectors to break down the data barriers by providing the ability to connect and query databases across on-prem and other public cloud environments. These connectors support SQL, Java Database Connectivity (JDBC), and Open Database Connectivity (ODBC) across public/private cloud, hybrid-cloud, and on-premises IT infrastructure types.
Virtualization is working for many organizations, but it is always advisable for teams to do abundant research before pulling the trigger. Consider aspects like organizational system and performance requirements, up-front investment costs, ongoing maintenance costs, and necessary internal resources.
Formulating a solid plan with the help of experts early on will help you design a quickly performant virtual environment that is easier to manage and scale.
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