Modernization is on the minds of IT decision makers, and with good reason — legacy systems cannot keep up with the realities of today’s business environment. Additionally, many organizations are discovering their modernization advantage: their developer teams, and the databases that underpin their applications.
“Legacy modernization is really a strategic initiative that enables you to apply the latest innovations in development methodologies and technology to refresh your portfolio of applications,” says Frederic Favelin, EMEA Technical Director, Partner Presales at MongoDB.
His remarks came during an episode of Google Cloud’s podcast series “The Principles of a Cloud Data Strategy.”
“This is much more than just lift and shift,” Favelin continues. “Moving your existing application and databases to faster hardware or onto the cloud may get you slightly higher performances and marginally reduce cost, but you will fail to realize the transformational business agility and scale, or development freedom without modernizing the whole infrastructure.”
The ‘Innovation Tax’
For many organizations, databases have proliferated, leading to a complex ecosystem of resources — cloud, on-premise, NoSQL, non-relational, traditional. The problem, Favelin says, is organizations have deployed non-relational or no-SQL databases as “band aids to compensate for the shortcomings of legacy databases.”
“So they quickly find that most non-relational databases excel at just a few specific things — niche things — and they have really limited capabilities otherwise, such as limited queries, capabilities, or lack of data consistency,” says Favelin.
“So it’s at this point that organizations start to really feel the burden of learning, maintaining and trying to figure out how to integrate the data between a growing set of technologies. This often means that separate search technologies are added to the data infrastructure, which require teams to move and transform data from database to dedicated search engine.”
Add the need to integrate increasingly strategic mobile capabilities, and the environment gets even more complex, quickly. In addition, as organizations are striving to deliver a richer application experience through analytics, they sometimes need to use complex extract, transform, and load (ETL) operations to move the operational data to a separate analytical database.
This adds even more time, people and money to the day-to-day operations. “So at MongoDB, we give this a name: innovation tax,” Favelin says.
Toward a modern ecosystem
Favelin says a modern database solution must address three critical needs:
- It should address the fastest way to innovate, with flexibility and a consistent developer experience. It must be highly secure, have database encryption, and be fully auditable.
- Next is the freedom and the flexibility to be deployed on any infrastructure– starting from laptops, moving to the cloud, and integrating with Kubernetes. It must be scalable, resilient, and mission critical with auto scaling.
- Finally, to offer a unified modern application experience means that the developer data platform needs to include full text search capabilities, must be operational between transactional workloads and analytical workloads, while bringing the freshness of the transactional data to the analytical data in order to be as efficient as possible to serve the best experience for the users.
“The MongoDB developer data platform helps ensure a unified developer experience,” Favelin says, “not just across different operational database workloads, but across data workloads, including search mobile data, real time analytics and more.”
Check out “The Principles of a Cloud Data Strategy” podcast series from Google Cloud on Google podcasts, Apple podcasts, Spotify, or wherever you get your podcasts.Get started today with MongoDB Atlas on Google Cloud on Google Marketplace.
Cloud Architecture, Databases