3 reasons why the centralized cloud is failing your data-driven business
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I recently heard the phrase: “A second for a human is good – for a machine, it’s an eternity”. This got me thinking about the profound importance of data speed. Not just from a philosophical but practical point of view. Users don’t really care how far the data travels, just how fast it gets there. In event processing, the speed at which data is ingested, processed, and analyzed is almost imperceptible. Data speed also affects data quality.
Data comes from everywhere. We are already living in a new era of data decentralization, fueled 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% of it being noise, but all of this data still needs to be analyzed. Data is important, it is geo-distributed and we need to make sense of it.
For enterprises to gain valuable insights into their data, they need to move away from the cloud-native approach and embrace the new edge-native. I’ll also discuss the limitations of the centralized cloud and three reasons why it fails data-driven businesses.
The disadvantages of the centralized cloud
In the business context, data must meet three criteria: fast, actionable and available. For more and more companies that operate globally, the centralized cloud cannot meet these demands in a cost-effective way, which brings us to our first reason.
It’s damn overpriced
The cloud was designed to collect all data in one place so that 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 nearly 2.5 quintillion bytes of data every day. Depending on who you ask, there could be over 75 billion IoT devices in the world, all generating huge amounts of data and requiring real-time analysis. Aside from the biggest enterprises, the rest of the world will be mostly out of the centralized cloud.
It can’t evolve
Over the past two decades, the world has adapted to the new data-driven world by building giant data centers. And within those clouds, the database is essentially “overclocked” to operate globally over immense distances. The hope is that the current iteration of distributed databases and connected data centers will overcome the laws of space and time and become geodistributed multi-master databases.
The trillion-dollar question becomes: how do you coordinate and synchronize data across multiple regions or nodes and synchronize while maintaining consistency? Without consistency guarantees, apps, devices, and users see different versions of the data. This, in turn, leads to unreliable data, data corruption, and data loss. The level of coordination needed in this centralized architecture makes scaling a herculean task. And only then can companies even consider analysis and insights from that data, assuming it’s not already outdated by the time they’re done, which brings us to the next point.
Unbearably slow at times.
For companies that don’t depend on real-time information for business decisions, and as long as resources are in the same data center, in the same region, everything scales as expected. If you don’t need realtime or geocast, you have permission to stop playing. But on a global scale, distance creates latency, and latency diminishes timeliness, and a lack of timeliness means companies aren’t acting on the most up-to-date data. In areas such as IoT, fraud detection, and time-sensitive workloads, hundreds of milliseconds are not acceptable.
A second for a human is fine – for a machine, it’s an eternity.
Native edge is the answer
Edge native, compared to cloud native, is designed for decentralization. It is designed to ingest, process and analyze data closer to where it is generated. For business use cases that require real-time information, edge computing helps companies get the insights they need from their data without the prohibitive write costs of centralizing data. Additionally, these edge-native databases won’t need application designers and architects to revamp or redesign their applications. Edge native databases provide multi-regional data orchestration without requiring expert knowledge to create these databases.
The value of data for business
The value of the data degrades if no action is taken. When you look at the data and move it to a centralized cloud model, it’s not hard to see the contradiction. Data loses value the moment it is transferred and stored, it loses much-needed context by being moved, it cannot be changed as quickly due to all the movement from source to central, and by the time you take action finally on it — there is already new data in the queue.
The periphery is an exciting space for new ideas and breakthrough business models. And, inevitably, every on-premises system vendor will pretend to be at the edge and build more data centers and create more PowerPoint slides on “Now serving the edge!” – but that’s not how it works. Sure, you can build a centralized cloud to make quick data decisions, but that will incur exorbitant costs in the form of writes, storage, and expertise. It’s only a matter of time before global data-driven enterprises can afford the cloud.
This global economy requires a new cloud, a distributed cloud rather than a centralized one. The cloud-native approaches of yesteryear that worked well in centralized architectures are now an obstacle for global data-driven enterprises. In a world of dispersion and decentralization, companies must look to the periphery.
Chetan Venkatesh is the co-founder and CEO of Macrometa.
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