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3 reasons why centralized cloud doesn’t make your data-driven business


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I recently heard the saying, “A second is good for a human – for a machine, it’s eternity.” It made me reflect on the profound importance of data rate. Not only from a philosophical point of view but also a practical one. The user doesn’t care how far the data has to travel, as long as it gets there quickly. In event processing, the rate at which data is entered, processed, and analyzed is almost imperceptible. Data rate also affects data quality.

Data comes from everywhere. We live in a new era of data decentralization, powered by next-generation devices and technologies, 5G, Computer Vision, IoT, AI/ML, not to mention current geopolitical trends around data privacy. The amount of data generated is huge, 90% noise, but all that data still has to be analyzed. The data matters, it is geographically distributed and we must understand it.

For businesses to gain valuable insights into their data, they must move from the native cloud approach and embrace the new edge native. I will also discuss the limitations of the centralized cloud and the three reasons it fails in data-driven business operations.

Disadvantages of centralized cloud

In an enterprise context, data must meet three criteria: fast, available, and readily available. For more and more businesses operating on a global scale, the centralized cloud is not able to meet these needs in a cost-effective way – the first thing we gave was the reason.

It’s so expensive

The cloud is designed to collect all the data in one place so we can do something useful with it. But moving data takes time, energy, and money – time is latency, energy is bandwidth and cost is storage, consumption, etc. The world generates almost 2.5 trillion bytes of data whether every day. Depending on who you ask, there could be more than 75 billion IoT devices in the world – all of which generate huge amounts of data and require real-time analytics. Apart from the largest enterprises, the rest of the world will essentially be priced out of the centralized cloud.

It is not scalable

Over the past two decades, the world has adapted to the new data-driven world by building huge data centers. And inside these clouds, the database is essentially “overclocked” to run globally over vast distances. It is hoped that the current iteration of connected distributed databases and data centers will transcend the laws of space and time and become a geographically distributed, multi-population database. physical.

The Trillion Dollar Question Becomes – How do you orchestrate and synchronize data across multiple regions or nodes and sync while maintaining consistency? Without guarantees of consistency, apps, devices, and users will see different versions of data. As a result, it leads to unreliable data, corrupted data, and data loss. The level of coordination required in this centralized architecture makes scaling a Herculean task. And it’s only later that businesses can even look at the analysis and insights from this data, assuming it’s not out of date by the time they’re complete, bringing us to the next point.

It’s slow

Sometimes it’s annoyingly slow.

For businesses that don’t depend on real-time insights to make business decisions, and as long as the resources are in the same data center, in the same region, everything should be fine. scale as designed. If you have no need for real-time or geographically distributed distribution, you have the right to stop reading. But on a global scale, distance creates latency, and latency reduces timeliness, and lack of timeliness means businesses don’t operate on the latest data. In areas like IoT, fraud detection, and time-sensitive workloads, 100 milliseconds is unacceptable.

One second for humans is good – for machines, it’s eternity.

Edge native is the answer

Original Edge, compared to the original in the cloud, is built for decentralization. It is designed to ingest, process and analyze data closer to where it was created. For business use cases that require real-time insights, edge computing helps businesses get the insights they need from their data without the cost of centrally writing data. whether serious. Additionally, these edge-native databases won’t need application designers and architects to re-architect or redesign their applications. Edge native databases provide multi-region data orchestration without requiring specialized knowledge to build these databases.

Value of data to business

Data depreciates if left untouched. When you look at the data and move it to a centralized cloud model, it’s not hard to see inconsistencies. Data becomes less valuable over time it is transferred and stored, it loses much needed context when it is moved, cannot be modified quickly as all the processes move from source to center and to the end. same you acted on it – there was new data in the queue.

The edge is an exciting space for new ideas and disruptive business models. And, sure enough, every on-premises system vendor will claim an advantage and build more data centers and create more PowerPoint slides of “Now serving Edge!” – but that’s not how it works. Sure, you can put together a centralized cloud to make quick data decisions, but it will come at an exorbitant price in terms of recording, storage, and expertise. It’s only a matter of time before data-driven, global businesses won’t be able to afford the cloud.

This global economy requires a new cloud – one that is distributed rather than centralized. The cloud-native approaches of years past have worked well in centralized architectures that are now a barrier to global, data-driven business. In a distributed and decentralized world, companies need to look for an edge.

Chetan Venkatesh is the Co-Founder and CEO of Macrometa.

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