Why Centralized Database Management Is Redundant and Outdated

The history of data management has largely been one of centralization. For the longest time, this was the most convenient solution, given the interoperability and communication capabilities throughout history. 

However, as the nature of business changes, so too must the way companies collect, maintain, and distribute data. As such, centralized databases are a poor fit for the dynamic needs of today’s markets. Adaptability and interconnectivity are the new rules of the game and below are the reasons why centralized databases aren’t up to the task.

A centralized database structure consists of a single server that stores and fetches relevant data for all company, department, and user needs. As the sole provider of info, the server needs to deal with a tremendous amount of requests and deliver in an expected timeframe.

In case of a traffic spike (which is more likely in such a high-throughput environment), a bottleneck situation can occur, limiting system performance. In worse cases, entire systems may crash as the database becomes paralyzed.

To improve resistance to overload scenarios, businesses can scale their databases. However, regardless of whether the scaling is horizontal or vertical, scaling introduces new problems (e.g., downtime, maintenance requirements, growing complexity, etc.) without addressing the inherent predisposition toward bottlenecks. 

Since scaling tends to follow greater traffic anyway, these solutions are just kicking the can down the road. Mind you, you can’t kick that can indefinitely, either; continually expanding or upgrading servers creates diminishing returns over time.

Centralized databases aren’t always 100% centralized. Rather, they tend to be configured in clusters useful for specific functions (HR data, marketing data, and the like). This sounds like decentralization on paper, but it’s more of a fractalized centralization. Each cluster serves its related client base, and access by clients from other clusters is restricted or limited. In other words, they are data silos.

The issue is that the separation of data into silos leads to a walled garden system where transparency and interoperability become challenging. The risk of data becoming inconsistent between silos grows over time, and moving data from one silo to another oftentimes proves difficult. As a result, departments that need to cooperate have a hard time coordinating efforts, while top management struggles to get a holistic view of the organization’s data.

Since a central data station contains all the information, it can dedicate less of its CPU power per request on average. This is why it typically takes more time to find and access data. As a result, users have to deal with sub-optimal response times. Application usability and user satisfaction ultimately take a hit because of this delay.

The problem is exacerbated when third parties (like KYC or security applications) add detours to the data flow. Not only does such complexity layering add latency, but it also opens new opportunities for data corruption or loss mid-transit. In situations where rapid, corruption-free data is essential (like in a payment or telecoms app), such risks are especially undesirable.

Another consequence of delayed responses is reduced adaptability on a local level. All clients rely on the state of the centralized data hub to receive and act on information. Any upsets in the database that creates latency will increase the time for clients to make informed decisions based on their specific needs.

Data loss can be a tricky obstacle because it happens in many different ways. Anything from malicious breaches to poor infrastructure or human error opens the door for data disappearance. The 2021 OVH data center fire serves as a stark reminder of how centralized data can disappear abruptly, leaving nothing but ashes in its wake.

Centralized databases are especially vulnerable to data loss due to the single point of failure problem. All the information is on a singular server, so there are far fewer redundancies to help replace lost data. And organizations that don’t have any backup servers or other database recovery measures have no way to replace lost information. 

The centralized infrastructure places enormous responsibility on the server to run without a hitch. Since all clients rely on it to feed them data, they are essentially blind without the database.

As the sole holder of the only available instance of data, centralized databases that collapse have a cascading effect. In certain situations, downtime may affect millions of people and permanently damage the reputation of a company. Given such high stakes, miscalculations or incorrect data become disproportionately dangerous.

Inery’s decentralized database management solution can counter the biggest pitfalls of the legacy alternative. IneryDB uses the blockchain to turn house-of-card databases into robust, flexible networks. 

Through the Inery ecosystem, data exchange is transparent and immutable, enabling a reliable source of truth. It also tears down garden walls that clog up communication lines between servers and clients.

All transactions on IneryDB are peer-to-peer, meaning that data travels the shortest distance possible. Queries yield results with minimal latency, while data corruption becomes next to impossible. IneryDB lets users reliably and safely CRUD data with a throughput of over 10,000 transactions per second.

Moreover, IneryDB offers a network resilient to power outages and other downtime causes. All data on the blockchain is doled out across the network nodes, effectively removing the single point of failure inherent to centralized solutions. Meanwhile, bottlenecks become a moot issue since the workload is distributed among the nodes in the blockchain.

Through Inery and its decentralized solution, databases can respond better to the mercurial needs of today’s businesses. So take a quantum data leap with Inery, and experience the pure power of decentralization in a sustainable, highly secure, and cost-effective way.