Moving data to the public cloud and finding a new location for the data warehouse are two of the most difficult issues IT leaders will have to deal with in 2021. To develop and compete, businesses must move away from their current systems and move their data warehouse to the public cloud.
Sadly, there is a widespread misperception that data virtualization could in some way facilitate this migration. It arguably says more about the complexity of the issue and the acute need than it does about a real solution. Everything else may just be a superior answer given how poorly the traditional method of database transfer performs in terms of ROI.
Consider data virtualization’s basic idea first. It is actually the concept of combining all databases into a solitary “uber database.” The different data sets will then be combined by this new database system’s component queries. In actuality, it is more of a data integration system than a database. It requires interacting with it using a brand-new query language that is similar to SQL.
Let’s look at some common misconceptions in this area and the top four reasons why moving an enterprise to the cloud won’t be solved by data virtualization.
1. Hardly Anyone Speaks Esperanto, Despite It Being a Great Language
Keep in mind Esperanto? The issue of translation across languages all over the world was meant to be resolved by the synthetic language. Anywhere they went, Esperanto speakers would be able to communicate with ease. If only everyone spoke Esperanto, there would be no need to study Mandarin, English, or any of the hundreds of other languages. That ends the theory. Esperanto is still effectively spoken only by a tiny group of linguists more than a century after it was first introduced.
How does this relate to data virtualization? In essence, data virtualization is a contemporary solution to the same problem. Its creators believed that by developing a single database language, the necessity for various database languages would soon become obsolete. It also brought the requirement for database migrations.
For each particular SQL dialect, the labour market of today has an abundance of talent. Database suppliers invest a lot of money in training their clients about their products. They have little motivation to instruct clients in an usually less powerful language (more on this below). Not to mention a language that works well with their rival’s platform.
As a result, the market for data virtualization languages still lacks a critical mass of skilled workers. The concept of a universal language is intriguing from a conceptual standpoint, much like Esperanto. Its implementation is still difficult to achieve.
2. Standardization robs an advantage of competition
Any data virtualization system only operates as effectively as the most restricted system it supports. The ramifications of this are extensive. Data virtualization solutions make sure that consumers are restricted to functionality that is shared by all current platforms in order to deliver on their promise.
Since database systems vary greatly, this restriction may significantly hinder innovation. Enterprises won’t be able to leverage robust database features like stored procedures or complex analytics capabilities until they are incorporated in the data virtualization solution. They won’t be able to use these capabilities until they are accessible in all rival database systems.
Again, this predicament is not brand-new. The history of SQL’s standardisation efforts to control suppliers is a long and rocky one. Nevertheless, it appears that every vendor has veered off course over time, sometimes to fix particular architectural issues with their system. But more frequently, to give their clients a competitive advantage in the shape of fresh features.
Vendors of data virtualization face a conflict as a result of this. If it affords them a distinct competitive edge, enterprise customers will cheerfully step outside the bounds and employ exclusive features of almost any database. Thus, data virtualization can be seen as a problem rather than a fix.
3. You Must First Migrate in Order to Solve Your Migration Problem, Right?
Another issue is that the entire company must agree to and carry out this change in order to employ data virtualization solutions. At first glance, this can resemble quitting any unhealthy habit, like eating poorly. It is obvious that changing your routine now would help your health in the long run.
But quitting a nasty habit and switching to a data virtualization system are very different things. The latter doesn’t merely compel businesses to alter in the future. They have to restart as a result. The organisation must shift to a data virtualization solution before it can benefit from it.
Even worse, switching to a data virtualization solution can be more difficult than switching to another database. Adopting this technology might need lowering or even simplifying existing capabilities due to its limitations. Not only is this a difficult technological problem, but the business can find it undesirable.
Although data virtualization has a wide range of benefits, it also has a high barrier to entry. The trade-offs must be understood by businesses. They must also consider their own track record of putting in place business procedures years in advance. It might not be wise to make this kind of investment.
4. Risky and Uncertain Return
This leads us to our final point. As we just showed, there is not much of an effort difference between migrating to a data virtualization solution and migrating to a new destination database. Each and every application must be rewritten or modified in both situations.
This may seem familiar to many enterprise data warehouse (EDW) owners. Most IT directors who are in charge of a data warehouse appliance have attempted to leave it at some point in the past. They can view the requirement to shift to a data virtualization system in order to switch databases systems as double hazard.
Existing applications may not function as expected even if the transfer to the data virtualization solution is successful. Instead, additional funds and time must be set aside to allow for project completion and engineering work.
The decommissioning of the legacy database is under intense time constraint for the majority of IT leaders. Increased danger results from complicating the transfer with a second migration.
An alternative to data virtualization is database virtualization.
The idea of data virtualization is intriguing, especially for businesses that have the freedom to start from scratch and don’t see database technology as a source of competitive advantage. It does not, however, address the issue of replatforming from on-prem to cloud.
In contrast to data virtualization, database virtualization offers a very different approach. The fundamental idea of virtualization is borrowed by database virtualization. It uses a platform that resembles a hypervisor to connect applications and databases. Real-time hypervisor emulation of the legacy database’s functionality.
The legacy system’s capabilities are added to the new destination database through database virtualization. In other words, a modern cloud database may “speak” legacy languages thanks to database virtualization. Therefore, current applications can be supported by the new stack without the need for pricey, drawn-out, and dangerous migrations.
Database virtualization, as opposed to data virtualization, addresses the “last mile” issue by linking existing applications to the new platform. There is no need for revisions. Because of this, manual migrations are effectively replaced by database virtualization.
