2022年2月8日 4分钟阅读

Today, the cloud provides unlimited resources that can be provisioned on a whim. 活着是多么美好的时光啊! 不幸的是,随着富足而来的是粗心和浪费. Many of the database vendors today have not had to learn about efficient processing and having to manage a system with competing priorities. They instead rely on brute scale and isolation of work to accomplish their goals. 但不是Teradata. We are the gold standard of efficiency and operations at scale.

但是为什么? 让十大电子游艺平台首选跳上回程机器去寻找答案吧. 十大电子游艺平台首选回到1979年, back to when the founders of Teradata were embarking on a journey that would change the arena of business intelligence forever.


Back then the major database vendors of the day were focused on processing transactions faster so companies could run their business cheaper. 这些人有不同的想法. What if you could bring all that data that was gathered and apply analytics at scale to understand your business and run it better.

The idea was to allow companies to take all that data and store it together in a massive (a terabyte of data!!) system to then run deep and broad analytics across the entire spectrum of the enterprise. The sheer audacity of that was viewed with skepticism, who would ever need a terabyte of data? 当然, 让这一切成为现实, 他们明白这样的系统必须是可扩展的, 因此自我管理, so that even the largest systems would be operated just like the smallest ones.


这是1979年. Looking at the “modern” technology of the day, they have Intel 086 chips, running at the speed of .5 MIPS. 磁盘驱动器容量可达200mb,重达50磅. 网络能够达到11 MB/s的速度. 这的确是一项艰巨的任务.

但所有这些都带来了巨大的成功. 在资源有限的情况下, 十大电子游艺平台首选必须找到一种方法来最小化“闲聊”操作, 最大化本地处理, 并在每一步消除瓶颈.

不谈那些血淋淋的细节, 他们重写了关于如何在存储中管理数据的书, they leverage the idea of parallelism and relational set operations, and they created a system of shared nothing units of processing that can be put together with linear scalability.

The last part of the puzzle was realizing that better processing means less resources. This meant the need for aggressive optimization in the query planning but also the need for a rich set of self-managed optimization techniques like indexing that are constantly in synch with the base data. 这工作!


奔向80年代末, the initial Teradata systems proved that companies could not only store all their transactions in a single system, but they could run unimagined analytics and gain great insights on how to change and improve their business. 事实上, 它运行得如此之好,以至于出现了另一个问题, keeping up with the need to effectively run ever increasing and varying workloads in a fixed amount of capacity.

你看到, 当时, we did not have the luxury of “infinite resources” and new compute and storage capacity had to be bought, 运, 然后安装——这个过程可能需要几周或几个月. This meant companies either had to buy for planned capacity to manage their peak seasons (thus over-configuring) or they had to buy for their normal workload (and thus be under-configured in the peaks).

This led Teradata to create what has becomes the industry leading 工作负载管理 范式. 知道不是所有查询都是相等的, Teradata has worked tirelessly to have a system that is easy to define a company’s priorities and then the optimizer ensures that high priority workloads get the critical resources necessary for the job.

We make it possible to get the most out of a system by managing the workloads so that we don’t encounter the same volatility – we smooth out the peaks and valleys to ensure that we are efficient in how we meet the business needs. But is also means that companies can now get more workload accomplished without having to add more hardware resources, 因此成本更高.


回到现在, 正如之前提到的, 十大电子游艺平台首选生活在一个富足的世界,一切都应该是美好的. 不幸的是, 在“无限”云资源的世界里, 不可预测的需求很容易让你的预算超支. The reality is that companies are still having to work with planning and budgets that cannot “scale infinitely.” Companies not only need to run workloads and meet user demand at scale across the enterprise, 但他们需要效率.

All this taken together is why Teradata has a proven to have the 每个查询的总成本最低 这就是重要的度量标准. Teradata brings out the ability to satisfy the demand predictably without giving the CFO headaches.

Now if only I can get my time machine to show what the future holds…. 我想十大电子游艺平台首选得一起去找答案.


自1987年以来, Rob has contributed to virtually every aspect of the data warehouse and analytical arenas. Rob’s work has been dedicated to helping companies become data-driven and turn business insights into action.  目前, Rob works to help companies not only create the foundation but also incorporate the principles of a modern data architecture into their overall analytical processes.

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