3 Reasons Why Centralized Cloud Is Failing Your Data-Driven Business
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I recently heard the phrase, "A second for a human is fine; 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 care much about how far the data has to travel, just that it gets there quickly. 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 for data-driven businesses.
The disadvantages of the centralized cloudIn 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.
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register here It's really expensiveThe 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. Apart from the largest enterprises, the rest of the world will be mostly out of the centralized cloud.
It can't evolveOver 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.
It's slowUnbearably slow at times.
For businesses 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 real time or geo...
Join leaders July 26-28 for Transform AI and Edge Week. Hear high-level leaders discuss topics around AL/ML technology, conversational AI, IVA, NLP, Edge, and more. Book your free pass now!
I recently heard the phrase, "A second for a human is fine; 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 care much about how far the data has to travel, just that it gets there quickly. 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 for data-driven businesses.
The disadvantages of the centralized cloudIn 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.
EventTransform 2022
Sign up now to get your free virtual pass to Transform AI Week, July 26-28. Hear from the AI and data leaders of Visa, Lowe's eBay, Credit Karma, Kaiser, Honeywell, Google, Nissan, Toyota, John Deere, and more.
register here It's really expensiveThe 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. Apart from the largest enterprises, the rest of the world will be mostly out of the centralized cloud.
It can't evolveOver 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.
It's slowUnbearably slow at times.
For businesses 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 real time or geo...
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