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What data is too risky for the cloud
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What data is too risky for the cloud?
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======
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In this four-part series, we've been looking at the pitfalls every organization should avoid when transitioning operations to the cloud—specifically into hybrid multi-cloud environments.
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[In part one][1], we covered basic definitions and our views on hybrid cloud and multi-cloud, making sure to show the dividing lines between the two. In [part two][2], we discussed the first of three pitfalls: why cost is not always the obvious motivator for moving to the cloud. And, in [part three][3], we examined the viability of moving all workloads to the cloud.
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Finally, in part four, we're looking at what to do with data in the cloud. Should you move data into the cloud? How much? What data works in the cloud and what creates too much risk to move?
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### Data… data… data
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The crucial factor influencing all your decisions about data in the cloud is determining your bandwidth and storage needs. Gartner projects that "data storage will be a [$173 billion][4] business in 2018" and a lot of that money is wasted on unneeded capacity: "companies globally could save $62 billion in IT costs just by optimizing their workloads." Stunningly, companies are "paying an average of 36% more for cloud services than they actually need to," according to Gartner's research.
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If you've read the first three articles in this series, you shouldn't be surprised by this. What is surprising, however, is Gartner's conclusion that, "only 25% of companies would save money if they transferred their server data directly onto the cloud."
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Wait a minute … workloads can be optimized for the cloud, but only a small percentage of companies would save money by moving data into the cloud? What does this mean?
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If you consider cloud providers typically charge rates based on bandwidth, moving all of your on-premises data to the cloud soon becomes a cost burden. There are three scenarios where companies decide it's worth putting data in the cloud:
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* A single cloud with storage and applications
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* Applications in the cloud with storage on premises
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* Applications in the cloud and data cached in the cloud, with storage on premises
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In the first scenario, bandwidth costs are reduced by keeping everything with a single cloud vendor. However, this creates lock-in, which often is contrary to a CIO's cloud strategy or risk prevention plan.
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The second scenario keeps only the data that applications collect in the cloud and transports out the minimum to on-premises storage. This requires a carefully considered strategy where only applications that use minimal data are deployed in the cloud.
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In the third scenario, data is cached in the cloud with applications and storage of that data, or the "one truth," stored on premises. This means analytics, artificial intelligence, and machine learning can be run on premises without having to upload data to cloud providers and then back again after processing. The cached data is based only on application needs and can even be cached across multi-cloud deployments.
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For more insight, download a Red Hat [case study][5] that describes Amsterdam Airport Schiphol's data, cloud, and deployment strategies across a hybrid multi-cloud environment.
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### Data dangers
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Most companies recognize that their data is their proprietary advantage, their intellectual capacity, in their market. As such, they've thought very carefully about where it will be stored.
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Imagine this scenario: You're a retailer, one of the top 10 worldwide. You've been planning your cloud strategy for some time now and have decided to use Amazon's cloud services. Suddenly, [Amazon buys Whole Foods][6] and is moving into your market.
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Overnight, Amazon has grown to 50% of your retail size. Do you trust its cloud with your retail data? What do you do if your data is already in the Amazon cloud? Did you create your cloud plan with an exit strategy? While Amazon might never leverage your data's potential insights—the company even has protocols against this—can you trust anyone's word in today's world?
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### Pitfalls shared, pitfalls avoided
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Sharing just a few pitfalls we have seen in our experience should help your company plan a safer, more secure, and persistent cloud strategy. Understanding that [cost is not the obvious motivator][2], [not everything should be in the cloud][3], and you must manage your data effectively in the cloud are all keys to your success.
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/18/8/data-risky-cloud
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作者：[Eric D.Schabell][a]
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选题：[lujun9972](https://github.com/lujun9972)
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译者：[译者ID](https://github.com/译者ID)
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校对：[校对者ID](https://github.com/校对者ID)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]:https://opensource.com/users/eschabell
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[1]:https://opensource.com/article/18/4/pitfalls-hybrid-multi-cloud
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[2]:https://opensource.com/article/18/6/reasons-move-to-cloud
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[3]:https://opensource.com/article/18/7/why-you-cant-move-everything-cloud
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[4]:http://www.businessinsider.com/companies-waste-62-billion-on-the-cloud-by-paying-for-storage-they-dont-need-according-to-a-report-2017-11
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[5]:https://www.redhat.com/en/resources/amsterdam-airport-schiphol-case-study
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[6]:https://www.forbes.com/sites/ciocentral/2017/06/23/amazon-buys-whole-foods-now-what-the-story-behind-the-story/#33e9cc6be898
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那些数据对于云来说风险太大?
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======
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在这个由四部分组成的系列文章中，我们一直在关注每个组织在将操作转换到云时应避免的陷阱 - 特别是混合多云环境。
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在第一部分中，我们介绍了基本定义以及我们对混合云和多云的看法，确保显示两者之间的分界线。 在第二部分中，我们讨论了三个陷阱中的第一个：为什么成本并不总是成为迁移到云的明显动力。 而且，在第三部分中，我们研究了将所有工作负载迁移到云的可行性。
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最后，在第四部分中，我们将研究如何处理云中的数据。 您应该将数据移动到云中吗？ 多少？ 什么数据在云中起作用，是什么造成移动风险太大？
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### 数据... 数据... 数据...
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影响您对云中数据的所有决策的关键因素是确定您的带宽和存储需求。 Gartner预计“数据存储将在2018年成为[1730亿美元] [4]业务”，其中大部分资金浪费在不必要的容量上：“全球公司只需优化工作量就可以节省620亿美元的IT成本。” 根据Gartner的研究，令人惊讶的是，公司“为云服务平均支付的费用比他们实际需要的多36％”。
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如果您已经阅读了本系列的前三篇文章，那么您不应该对此感到惊讶。 然而，令人惊讶的是，Gartner的结论是，“如果他们将服务器数据直接转移到云上，只有25％的公司会省钱。”
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等一下......工作负载可以针对云进行优化，但只有一小部分公司会通过将数据迁移到云来节省资金吗？ 这是什么意思？
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如果您认为云提供商通常会根据带宽收取费率，那么将所有内部部署数据移至云中很快就会成为成本负担。 有三种情况，公司决定将数据放入云中是值得的：
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具有存储和应用程序的单个云
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云中的应用程序，内部存储
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云中的应用程序和缓存在云中的数据，以及内部存储
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在第一种情况下，通过将所有内容保留在单个云供应商中来降低带宽成本。 但是，这会产生锁定，这通常与CIO的云战略或风险防范计划相悖。
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第二种方案仅保留应用程序在云中收集的数据，并将最小值传输到本地存储。 这需要仔细考虑的策略，其中只有使用最少数据的应用程序部署在云中。
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在第三种情况下，数据缓存在云中，应用程序和存储的数据，或存储在内部的“一个事实”。 这意味着分析，人工智能和机器学习可以在内部运行，而无需将数据上传到云提供商，然后在处理后再返回。 缓存数据仅基于应用程序需求，甚至可以跨多云部署进行缓存。
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要获得更多信息，请下载红帽[案例研究] [5]，其中描述了跨混合多云环境的阿姆斯特丹史基浦机场数据，云和部署策略。
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### 数据危险
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大多数公司都认识到他们的数据是他们在市场上的专有优势，智能能力。 因此，他们非常仔细地考虑了它的储存地点。
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想象一下这种情况：你是一个零售商，全球十大零售商之一。 您已经计划了一段时间的云战略，并决定使用亚马逊的云服务。 突然，[亚马逊购买了Whole Foods] [6]并且正在进入你的市场。
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一夜之间，亚马逊已经增长到零售规模的50％。 您是否信任其零售数据的云？ 如果您的数据已经在亚马逊云中，您会怎么做？ 您是否使用退出策略创建了云计划？ 虽然亚马逊可能永远不会利用您的数据的潜在见解 - 该公司甚至有针对此的协议 - 你能相信今天世界上任何人的话吗？
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### 陷阱分享，避免陷阱
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分享我们在经验中看到的一些陷阱应该有助于您的公司规划更安全，更安全，更持久的云战略。 了解[成本不是明显的激励因素] [2]，[并非一切都应该在云中] [3]，并且您必须在云中有效地管理数据才是您成功的关键。
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--------------------------------------------------------------------------------
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via: https://opensource.com/article/18/8/data-risky-cloud
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作者：[Eric D.Schabell][a]
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选题：[lujun9972](https://github.com/lujun9972)
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译者：[geekmar](https://github.com/geekmar)
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校对：[geekmar](https://github.com/geekmar)
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本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
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[a]:https://opensource.com/users/eschabell
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[1]:https://opensource.com/article/18/4/pitfalls-hybrid-multi-cloud
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[2]:https://opensource.com/article/18/6/reasons-move-to-cloud
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[3]:https://opensource.com/article/18/7/why-you-cant-move-everything-cloud
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[4]:http://www.businessinsider.com/companies-waste-62-billion-on-the-cloud-by-paying-for-storage-they-dont-need-according-to-a-report-2017-11
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[5]:https://www.redhat.com/en/resources/amsterdam-airport-schiphol-case-study
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[6]:https://www.forbes.com/sites/ciocentral/2017/06/23/amazon-buys-whole-foods-now-what-the-story-behind-the-story/#33e9cc6be898
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