Snowflake 2023-03-06 JMP Securities Technology Conference
发布时间 2023-05-01 08:07:31 来源
中英文字稿 
And to my right, of course, is Mr. Scarpelli, the CFO. And we'll start at the top, which is, well, first of all, how are you? I'm good. Can I say something before you do this? I have to tell you. I can't wait. I've been dealing with, this is actually the fifth public company I've been dealing with, analysts with callbacks for many, many years. And I have to tell you, Pat did something the other day that was the first. Oh, I can't wait. I can't wait. He was unbelievable.
在我的右边当然是我们的首席财务官Scarpelli先生。我们从最高层开始，首先，你好吗？我很好。在你开始之前，我能说一些话吗？我必须告诉你，我迫不及待地想说。我已经和五家上市公司打过交道了，多年来一直与分析师有回电讨论。我不得不告诉你，帕特最近做了一件前所未有的事情，他太不可思议了。
He actually had his son, who is a data scientist, who works at actually a customer of ours. Come on the call and ask all the questions of Christian at a technical level, which was, I've never, ever seen an analyst have their son do that. That tells me two things. A, you're old. Really old. I've known Pat for many years. And B, the value you get out of, really, if you're a data scientist, and you appreciate that. And hopefully you got something out of that that your son learned and you could appreciate the difference between snowflake and what others are doing out there. So I really did appreciate you doing that.
实际上，他让他的儿子，一个数据科学家，在我们的客户公司工作。他让儿子参加电话会议，并以技术层面提出所有的问题来询问Christian，这是我从未见过的分析师让自己的儿子这样做。这告诉我两件事情。首先，你真的很老，我认识Pat已经多年了。其次，如果你是一名数据科学家，并且你欣赏这个领域，你真正得到的价值很大。希望你的儿子能从中学到一些东西，并且你能欣赏到雪花和其他公司之间的差别。所以我真的很感激你这样做。
What is back then? Well, I also realized Pat's cheap because his house was so cold that his son has a jacket on and he has a jacket on too. So he wrote it to me. I wrote it down. I wrote it down. I'm not going to find it. I wrote down Zachary's reaction. I put it as a, as a, as a positive data point. So yeah, so I have a, we have four children, but three of them are hopefully no one's in there, son, they're around here somewhere. But three of them are normal, but one of them is just really, really off the charts. And he's a data scientist and he talks slow. Did you notice that? Yeah, he's very methodical. He's very, very, very, very slow, very slow. And so he would be pausing before asking the next question. I was like, oh my god, is there another question coming? Yeah, no. And then it was just like great. He has good questions. It was great. And he, so when I emailed you guys, I said I'm going to get my data science expert on. I didn't share it. It was. Yeah, so snowpark is a big deal. That was what I took away from the conversation was Zach. We'll get to that. So let's start with house business and then we'll go straight to snowpark after that.
那么，什么是“back then”呢？嗯，我也意识到Pat很抠门，因为他的房子很冷，他的儿子和他自己都穿着夹克衫。所以他给我写了一封信，我把它写了下来。我不会找到它了，我写下了Zachary的反应，将其作为一个积极的数据点。所以我们有四个孩子，但其中三个正常，但有一个非常非常出类拔萃。他是一位数据科学家，说话很慢。你注意到了吗？是的，他非常有条理，非常非常非常慢，他在问下一个问题之前会停顿。我很担心，会不会有另一个问题出现？但他的问题很好。当我给你们发电子邮件时，我说我会请我那位数据科学专家来的。我没有透露这一点。雪公园是一个很大的问题，这是我从这次会议中得到的收获。那么，让我们从房子的生意开始，然后直接进入雪公园。
Well, we just gave an update on the business last week and nothing's changed since then. Give me your characterization. You know, Q4, from a revenue standpoint, we pretty much hit our internal guidance from the beginning. And I say internal what we present to our board. And so it's where we expected it to be. What we did notice was different was the, our older customers seem to have contributed to more of the growth and the newer cohort are growing much slower. And we think that is a function of newer customers today as they're ramping on snowflake.
嗯，上周我们刚刚对业务进行了更新，自那以后没有任何变化。请给我你的评价。你知道的，从收入的角度来看，我们在第四季度基本上达到了我们内部的指引。我所说的内部指引是我们向董事会呈现的指引。我们注意到的不同之处在于，我们的老客户似乎为更多的增长做出了贡献，而新的用户群体增长速度更慢。我们认为这是新客户今天在雪花上的逐渐增长所致。
There's a lot more knowledge out there around people who have been working on snowflake for a period of time have learned the pitfalls of not deploying snowflake properly. The pitfalls have not put in place the proper governance. We have a lot more partners who are trained on how to implement snowflake properly. We are doing a lot more training with customers on how to use snowflake out of the gate. We have built a lot of capabilities within snowflake to ensure that people are using it properly.
有很多知识已经涉及到雪花，那些长时间从事雪花工作的人已经了解了不正确部署雪花的风险。这些风险是由于没有进行正确的管理而存在的。我们现在有更多的合作伙伴受过培训，知道如何正确地实施雪花。我们也向客户提供更多关于如何正确使用雪花的培训。我们在雪花内部已经建立了很多功能，以确保人们正确地使用它。
Like auto suspend, it was always easy for people to, you spin up a warehouse. There's two things you want to do. You want to be able to select the right size warehouse. And it was easy for people to select a really large warehouse. It was easy for people to disable the auto suspend function. So A, you could be running a warehouse bigger than what you want. Now it's much harder to do that. We do that for you. We have all kinds of alerting when someone disables an auto suspend function so that people know that and there's questions, do you really want to do that? And B, people are just using snowflake more efficiently.
和自动暂停一样，人们很容易启动一个仓库。你想做的事情有两件。第一是选择合适大小的仓库。而人们往往会选择一个非常大的仓库。另外，人们也很容易禁用自动暂停功能。因此，A)你可能会运行比你需要更大的仓库。而现在这样做就更难了。我们帮你选择。当有人禁用自动暂停功能时，我们会发出各种警报，以便人们知道并有疑问，你真的想这样做吗? B)人们现在正在更高效地使用snowflake。
But I think two is the earlier customers were those digitally native customers that think of the instacarts in others of the world that were just born in the cloud that really it was growth at all costs. The cohort of customers we have now are the more established mature companies. I don't want to say they're the laggards or the late adopters, but they're not those necessarily the fastest moving companies. They've always had cost controls in place. And so we're seeing the newer cohorts just ramping more slowly.
我认为，我们之前的客户主要是那些数字原生客户，他们认为像世界上其他刚刚出现在云端的Instacarts一样成长必须要不惜一切代价。而现在我们拥有的客户群体是更成熟更稳定的公司。我不想说他们是滞后者或者迟钝的采用者，但他们并不一定是发展最快的公司。他们一直都有成本控制措施。因此，我们看到新的客户群体增长速度较慢。
They still all have the same end state they want to get to. They're just going to go at their pace in a very controlled fashion on controlling costs. Okay. So you predict the future, you were predicting the future based on cohorts and the new customers are actually not matching the same behavior as the older cohorts. Correct. That fair assessment.
他们仍然都有着想要达到的相同的最终状态。只是他们会以一种非常有控制的方式按照自己的节奏控制成本。好的。所以你预测未来，你是基于队列预测未来的，而新客户实际上与老队列的行为不匹配。是的，这是一个公平的评估。
And we were going through and spending a lot of time in the second half of January and February as we're rolling quota. You remember at the beginning of the year is when we have to roll quotas out by rep for the full year and we roll out consumption quota. There's a lot of discussion that happens between my finance team and the sales people on accounts. And that's where it became evident. A lot of those newer cohort of customers are just not growing at the same pace.
我们在一月和二月的后半段花费了很多时间，因为我们正在推行销售定额。你记得在一年开始时，我们需要按代表的全年销售定额滚存，并推出消费定额。我的财务团队和销售人员之间会有很多讨论。而这就清楚地显示出来了。许多新客户组的增长速度并不那么快。
And I don't mean customers we landed three quarters ago. I'm talking the customers we've landed over the last one to three years are just growing slower. Over the last one to three years. How much slower are we talking about? Well, enough that I lowered the guidance to 40% growth next year. Which by the way is still good growth at the scale we're at. I'm not going to apologize for that type of growth.
我所指的不是我们三个季度前获得的客户。我指的是在过去一到三年里我们新增的客户成长速度较慢。我们说的“较慢”是多慢呢？我已经将下一年度的预期增长率下调至40%。虽然在我们当前的规模下仍算不错的增长率，但我并不为这种增长率感到歉意。
What was it before? In the end of November, we were in our preliminary planning. We were thinking 47%. Yeah. Growth. Okay. So is an order of magnitude of what the cohorts are seeing? Is that a good indicator or is there something else in there? No. That's the layer next to the level of concern. Is there something on top of it? That's the biggest thing there.
这之前是怎么样的？在十一月底，我们进行了初步的规划。我们考虑了47％的增长。好吧，那么这是否是群体所看到的程度，或者还有其他因素在其中？不，那只是关注程度的下一层。还有更重要的吗？那是最大的事情。
Okay. And by the way, we're generally pretty good at forecasting consumption on an annual basis and I'll tell you, we had set our 2022 plan in February of 2022. We went to the board. We did miss that plan by 2%. I mean, full transparency. About 42 million dollars or whatever it was. And that was before you had Ukraine. That was before you had interest rates, that was before you had the crypto implosion. That was before at about 4%. About 4%. 2%. 2%. So I think we do pretty good. My point is I think we do pretty good job at forecasting.
好的，顺便说一下，我们通常很擅长预测年度消费，并且我告诉你，我们在2022年2月制定了我们的计划。我们向董事会提交了计划，但我们错过了这个计划2%。我是说，完全透明。大约4200万美元左右。那是在乌克兰问题出现之前，也是在利率问题、加密货币崩溃问题之前。这是在约4%左右。2%。2%。所以我认为我们做得相当好。我的观点是我们在预测方面做得相当好。
Okay. So you peaked my interest when you started talking about the quotas. So how do the quotas work for your salespeople? Salespeople get two quotas. They get a growth quota and they get a consumption quota. And it could be anywhere from 50-50 to 50% of their pay. Their variable pay is based on growth, 50% on revenue. It could be as high as 90% on growth, 10% on revenue if you're truly a hunter going after new logos. Or it could be 10, 90, 10 on growth, 90% on consumption if you're just managing one of our top 10 accounts.
好的，当你开始谈论销售配额时，我就对此产生了兴趣。那么你们的销售人员如何执行销售配额呢？销售人员有两个配额。他们有一个增长配额和一个消费配额。他们的可变工资基于增长，其中50%基于收入。如果您是真正的猎人开发新客户，增长配额可能高达90％，收入配额为10％。或者，如果您只是管理我们前十大客户之一，增长配额和消费配额可能分别为10％和90％。
Do they complain that it's not fair to expect them as a, hey, just Mr. Salesperson, to be able to influence the consumption at some major bank, for example? They some complain, some don't. Some other really. They don't complain directly to me because they know what happens when they complain to me. But at the end of the day, though, they can influence it. You're absolutely wrong. What are the prime examples?
他们抱怨作为销售人员，仅仅看作为一个“Mr. Salesperson”，就期待他们能够影响一些大型银行的消费，这不公平。有些人抱怨，有些人不抱怨，还有其他的。他们不会直接向我抱怨，因为他们知道向我抱怨会发生什么。但到最后，他们仍然可以影响消费。你绝对是错的。有哪些主要的例子呢？
他们认为这不公平，因为他们只是销售人员，而这些大型银行却有许多资源和权力。虽然有些人会抱怨，但他们知道向管理层抱怨会产生负面影响。然而，销售人员仍然可以通过提供良好的客户服务和推荐适当的产品来影响消费。所以，这不是不可能的。
Here's a prime example. If they're out there and they're educating customers on actually how to use the product, how to use Snowpark, we're really spent, or last sales kickoff, on educating salespeople on how to go in and ask the right questions to customers to identify whether there's a Snowpark opportunity within that account, a Spark replacement, an ER, AMR, Cloudera, whatever. And they don't actually have to sell anything. They just need to educate the customer, get a customer using, and that will lead to a customer having to buy more capacity sooner. That's what we need our sales reps to do.
这是一个很好的例子。如果他们在教育客户如何使用产品、如何使用Snowpark的话，我们就真的需要在销售启动会上教育销售人员如何向顾客提出正确的问题，以确定该帐户中是否有Snowpark机会，是一个Spark替代品，还是ER、AMR、Cloudera等。他们实际上不需要销售任何东西，只需要教育客户，让客户使用产品，这将导致客户更快地购买更多的容量。这就是我们需要我们的销售代表去做的事情。
And by the way, AWS, Azure, and Google pay all the reps on revenue, not on bookings. I miss the significance of that. What we're doing is what is in the industry you're saying reps can't influence a customer on the channel. They all do that. That's how they're paying all the reps. Yeah, that's how they all did.
顺便提一下，AWS、Azure和Google向销售代表支付的报酬基于营收而非订阅业务，我认为这点很重要。我们正在行业上推广的做法是，你在说销售代表无法影响客户的购买渠道，但实际上他们都会这么做，这也是为什么所有的销售代表都按此方式获得报酬。是的，他们都这么做。
Okay, so let's shift to Snowpark, which honestly, I don't get nearly as well as I should. So this will be the opportunity. Well, if you don't get it, I'm probably not. I'm not as technical as your son. That's for sure.
好的，让我们转向Snowpark，说实话，我对它的理解远不如我应该了解的那么深入。所以这将是个机会。如果你不理解，那么我也可能理解不了。我肯定比你的儿子要不那么专业。
Well, yeah. If only you were here. But so what was the opportunity with Snowpark that you felt like you were not capturing before? Spark workloads within data engineering. And what was happening is before we had Snowpark customers would take their data out of Snowflake, move it into another system. Run the Spark workloads and then move that data back into Snowflake. What's very expensive? Because you're paying to move that data by running it right within Snowflake.
是的，如果你在这里就好了。但是，你觉得Snowpark提供了什么样的机会，之前你没有掌握呢？那就是在数据工程中使用Spark负载。在没有Snowpark之前，客户需要将数据从Snowflake中取出，移动到另一个系统中运行Spark负载，然后将数据移回Snowflake。这非常昂贵，因为你需要支付将数据转移的费用。现在有了Snowpark，你可以直接在Snowflake中运行Spark负载，这样更加便捷和经济实惠。
You know exactly, A, you're not having to pay for moving the data, but B, you have this security and governance of Snowflake. Once you move your data outside of Snowflake, unless it's in a system that's highly secured and governed, you don't know what may happen to that data and where it could go. That's the big benefit to customers. And what are the very, what are the kind of workloads that you would take out of what's the kind of analysis that you would do in Spark that you wouldn't just be able to do in Snowflake?
你明白，A，你不必支付数据搬迁费用，但是B，你可以享有Snowflake的安全和管控。一旦你将数据移出Snowflake，除非它在一个高度安全和管控的系统中，否则你无法知道数据会发生什么和它可能去哪里。这是对客户的巨大利益。那么，有哪些工作量是你会从Spark中取出的，又有哪些分析是你无法使用Snowflake完成的呢？
You know, as I said, I'm not the technical person. I'm the technical person. I'm going to look at so hey, so hey, what are you doing in Spark that you would do in Snowflake? Analytics. Analytics. Analytics. Analytics of Snowflake all the time. That's what we do. Machine learning. Machine learning. Machine learning stuff. Exactly. Is that, is that, were they often doing it in Databricks? Databricks, open source Spark, Cloud era, EMR. Yeah. Okay.
你知道，我不是技术专家。我是技术人员。我会关注在Spark里你能做哪些和Snowflake一样的事情？分析。一直都是Snowflake的分析。我们做机器学习等相关工作。没错。那这些工作通常是在Databricks里完成的吗？Databricks、开源Spark、Cloud era、EMR都可以做。好的。
So basically you had sort of the, it also enables you to write applications directly in Snowflake by having that. They tend to be heavily analytics applications. Right. Right. So basically we're talking about there's this machine learning opportunity, right? That you guys were not capturing as much of as you could. And in fact, it was worse, right? Because your customers would take the data out of Snowflake, put it somewhere else, and then there'd be all these inefficiencies in doing that.
基本上，你们拥有一种功能，使你们能够直接在Snowflake中编写应用程序。这些应用程序往往是重度分析应用程序。对的，我们讨论的基本上是有这个机会进行机器学习，对吧？而你们没有尽可能充分地利用它。事实上，更糟糕的是，你们的客户会把数据从Snowflake中提取出来，放在其他地方，然后进行操作会非常低效。
When did Snowpark come out? For GA for Python just came out last quarter. And you really just pushed with the sales force at our sales kickoff in February? Yeah. So we're right there. I guess it's sort of, we've had it for Java and Scala earlier. But Python is the most common programming language that the data engineers want to use today.
Snowpark是什么时候推出的？针对Python的GA仅在上个季度推出。你们在二月份的销售启动会上才真正开始推广销售？是的。我们确实在那时开始了推广。我想我们之前已经推出了Java和Scala版本。但是现在，数据工程师最常用的编程语言是Python。
Yeah. By the way, a little aside, so that, that, that kid that you're talking about, I'm like, exactly what's everyone using ChatGPT for? He goes, well, that, I wrote a program in Scala and I needed it to be, I think, at Python. And so I asked ChatGPT to rewrite it for me in Python. And I'm like, in that work, he goes, oh yeah, he submitted the code yesterday, he got accepted. Crazy. It is.
哦，顺便提一下，那个你提到的小孩，我想知道大家都在用ChatGPT做什么？他说，他用Scala写了一个程序，但是需要用Python实现。所以他向ChatGPT请求帮忙将程序转为Python。我问他这个方法有效吗，他说，是的，昨天他提交了代码，通过了审核，这太不可思议了。
That's actually what we see as one of the biggest use cases for AI is really helping develop code. I'm still trying to figure out how ChatGTP itself is going to make money. And do you listen to this week's all-in podcast? No. No. I can't believe that I, that I recommend it. Honestly, it's so freaking good, right? And it's so good because that one guy is, is Dave Sacks is the head of venture firm. So that, however, you know, whatever, 40, 50 portfolio companies, and he just kind of tells you what he's seeing, right? Which is very rare to get a VC to do that, right? They're the whole things closer to the vest.
实际上，我们认为AI最大的用例之一是帮助开发代码。我仍在努力弄清楚ChatGTP本身如何盈利。你听了本周的"all-in"播客吗？没有。没有。我真的不能相信我会推荐它。老实说，它非常好听，对吧？而且之所以这么好，是因为其中一位是风险投资公司的负责人Dave Sacks。他有大约40到50个投资组合公司，他告诉你他看到了什么，这非常少见，VC们通常不会这样做，他们把所有事情放得很靠近胸口。
But anyways, yeah, this week's, this week's session is all about they're not sure either is the bottom line. They see where there's a lot of benefits coming to consumers, right? They're not so sure that that's not just going to get absorbed by, you know, the big tech players. They're not so sure either. It was interesting. There was a, it was well publicized. There was a Princeton grad student over a weekend. He wrote an application in streamlit using chat GTP to tell whether a, and a paper was machine generated or written by humans. You know, we've had over 8 million views. Yeah.
无论如何，这周的课程是关于他们也不确定底线的。他们看到消费者获得了很多好处，但他们不确定这是否会被大型科技公司所吸收。这也让人不确定。有趣的是，有一个普林斯顿研究生在一个周末使用 streamlit 和 chat GTP 来编写了一个应用程序，以判断一篇论文是机器生成的还是由人编写的。我们已经获得了超过800万次的阅读量。
So we had this conversation in our 14 year old is like, yeah, why are all the teachers so freaked out at Redwood High School about this chat GPPT thing? What is it anyways? Right? And so we have the data scientists in the 14 year old at the table, right? And so Zachary goes, GGS chat GPPT, so she's in ninth grade. He says, well, he goes, what are you studying in school right now? And she goes, global warming. And he goes, ask chat GPPT to write a five paragraph, so chat GPP, write a five paragraph effort essay about global warming that's appropriate for a 10th grader. Yeah. Right? That's the beauty, right? That's appropriate for a 10th grader. And then the next example is even crazier.
我们在和我们14岁的孩子谈话时，她说：“为什么Redwood高中的所有老师都对这个叫做GPPT的聊天东西感到那么惊慌？那是什么？”因此，我们有了数据科学家和14岁的孩子坐在一起的对话。然后Zachary说：“GGS聊天GPPT，所以她是九年级。他问她：“你现在在学校里学什么？”她回答：“全球变暖。”然后他说：“让GPPT写一篇适合十年级的关于全球变暖的五段论文章。”这就是美妙之处，它适合十年级的学生阅读。的确是个很疯狂的例子。
So my wife is an author and she's got a bunch of articles on the web. And she was about to interview someone for a class that she's doing who is the CEO and female founder of a video game company. And it was write the interview questions that she should ask the CEO founder of the video game company in the voice of Samantha Parent, my wife. And she gets a little bossy when she's on this topic. There's a lot of moths and, you know, and so literally the questions come out in her voice. But how is that all going to benefit snowflake? I don't know.
我的妻子是一位作家，在网络上有许多文章。她准备采访一个视频游戏公司的女性创始人兼CEO，为她的课程进行采访。她会以自己的声音，向这位CEO创始人提问。她在这个话题上有些自作主张，往往会表现得有些强势。但这一切与Snowflake有什么关系呢？我不知道。
So what we see happening, and I'm just going to, I'll call them large language models. Yeah. We're never going to be the one developing these large language models. Why to develop something like that is like it could be a hundred million in compute to develop these models.
所以，我们看到的就是，我简单地称之为大型语言模型。是的。我们永远不会开发这些大型语言模型。开发这样的东西就好像需要1亿计算才能开发出这些模型。
But these models need to be fine tuned on real data. We have the data. So the big thing that we think is enabling those models to run directly on the data in snowflake is how we think we will benefit. So companies will take those large language models, they license and run it against their snowflake data for their business to fine tune the models for them. Right. That's how we believe.
但是这些模型需要在真实数据上进行微调。我们拥有数据。因此，我们认为能够使这些模型直接在Snowflake数据上运行的重要因素是我们认为我们将从中受益。因此，公司将会拿取这些大型语言模型，针对其Snowflake数据进行许可和运行，以微调模型，以满足他们的业务需求。是的，这就是我们的信仰。
What's interesting to us though is people to be able to write their own queries in snowflake using chat GTP to develop the queries. That's where are you seeing people do that? There's all kinds of YouTube videos around that. I give you a simple example. You want a query certain data and you want to write the code and Python. Some are to what you're saying but not convert it. Actually write the queries themselves. You can do. Wow. By the way, they're not always 100% accurate. No. They're to.
我们感兴趣的是人们能够使用雪花数据库进行聊天式 GPT 编写自己的查询语句。你能看见人们在哪里这样做吗？有各种各样的 YouTube 视频讲述这个。这里给你一个简单的例子：你想查询某些数据，而且想用 Python 编写代码。有些人只是和你说一下，但不转换它。实际上，他们是自己编写查询语句。你可以这样做。哇，顺便说一下，它们并不总是 100% 准确。不是吗？
Okay. Cool. So let's go back to what you're seeing in the macro. So lengthening sales cycles. Sales cycles. All that stuff that everyone else is talking about. Sales cycles themselves. I've been saying since day one, these large global 2000 accounts are one to two to three year sales cycles. It really depends. But they all start small. We generally, when we land a global 2000, the average size is like a underground. It's not like they're big. It's their follow-on deals typically are bigger.
好的，很好。让我们回到你在宏观层面看到的问题。销售周期的延长，这是大家都在谈论的问题。我从一开始就一直在说，这些大规模的全球 2000 客户的销售周期可能会是一到两年，甚至三年。具体看情况。但是它们都从小开始。当我们成功开拓一个全球 2000 客户时，平均规模就像地下室一样小。不是很大。而通常后续的交易规模则更大。
You can see anything changing there. What I do see is customers wanting and it's more with our existing customers where they've consumed faster than their contract rate. They want additional discounts. They want an economic benefit to do a new deal. If not, they're just going to continue to buy under their existing contract and we're just saying fine by under your existing contract. It's just buying capacity and that's what we saw. My largest customer just bought enough capacity to birds them through to March rather than do an annual contract.
那里变化万千，你可以看到各种情况。我所观察到的是客户需求增加了，而且更多的是来自现有的客户，他们的消费速度比合同规定的速度更快，他们想要额外的折扣以获取经济利益，否则他们只会继续在现有的合同下购买，而我们只能说在现有的合同下为他们提供所需的容量，这就是我们看到的情况。我的最大客户刚刚购买了足够的容量，可以支撑他们一直使用到三月份，而不是签订年度合同。
Why? Because they can under their contract and they can continue to do that until July. But in July, they have to do something of equal to or greater than their old deal or they lose the discount they have. And so I'm hearing more and more customers wanting different payment terms. If you went back over a year ago, interest rates were virtually zero and holding cash wasn't a big deal today. You can earn 4.9% over an eight money.
为什么？因为根据他们的合约，他们可以继续这样做直到7月。但是在7月，他们必须做出等于或超过旧合约价值的交易，否则他们就会失去折扣。因此，我越来越多地听到客户想要不同的付款方式。如果你回溯到一年前，利率几乎为零，持有现金并不是什么大事。今天，你可以在八个月内获得4.9%的收益率。
Okay. We have five minutes. So let's open it up. I have a couple more. I want to ask you at the end. But let's open it up to any questions from our audience. There is no change in the competitive landscape. It continues to be Google BigQuery number one. It has been since the time we went public. Databricks is probably, I don't want to say number two because Microsoft I would say it would be number two. Databricks is the other one. And our whole snow park really goes directly at them. But the reality is they coexist in many of our accounts. Why we brought them into many of those accounts early on and we partnered with them. But it's definitely Google, I would say, is the most competitive.
好的。 我们有五分钟的时间。 那么让我们开始。 我还有几个问题想在最后问你。但是让我们向我们的观众开放任何问题。竞争环境没有变化。Google BigQuery仍然是第一名。自我们上市以来一直如此。 Databricks可能是第二名。我不想说是第二名，因为我认为Microsoft将是第二名。 Databricks是另一个。我们的整个snow park确实直接针对它们。但现实是，在我们很多账户中它们共存。这就是为什么我们早期将它们引入许多这些账户并与它们合作的原因。但是毫无疑问，我认为Google是最具竞争力的。
In Google and Microsoft have the best, they can do a lot of bundling, which they do. Databricks doesn't have that pricing pressure that a Google or Microsoft can put on us. And by the way, we've been dealing with that for years with those guys offering stuff for free. And free isn't free. You've got to look at the total cost of ownership.
Google和Microsoft拥有最好的绑定功能，他们可以做很多打包销售，而他们也确实有这么做。Databricks没有像Google和Microsoft那样对我们施加定价压力。顺便说一下，多年来我们一直在与这些公司打交道，他们会提供一些免费的东西，但这并不意味着它是真正免费的。你必须考虑到总拥有成本。
So the question was, what do I think about the public sector opportunity and when do we expect to have FedRAMP high certification? I expect to have FedRAMP high certification very soon. We've submitted everything and it's just waiting on the getting awarded that it could be, as far as I know it could be a week or it could be two or three months waiting on them. The public sector opportunity is only upside because they're pretty small. It's less than 1% of our business today.
问题是，我对公共部门机会有什么想法，我们预计何时获得FedRAMP高级认证？我预计很快就会获得FedRAMP高级认证。我们已经提交了所有文件，现在只是等待他们授予认证，根据我的了解可能需要一周或两到三个月时间。公共部门机会仅提供向上的发展，因为目前它们很少，仅占我们业务的不到1％。
I do think public sector in general, and I'm not just talking. The US is a big opportunity for us. I just came back from Korea and I was actually meeting with a consortium of companies that are advising the Korean government on their digital strategy and what they want to do. And this is going on around the world.
我认为公共部门一般来说是一个巨大的机遇，我不只是指美国。我刚刚从韩国回来，与一些公司的联合会见面，他们正在为韩国政府制定数字战略并指导他们做什么。这种情况在世界各地都在进行。
There are data sovereignty issues that we're working through, but UK is another good one there. But overall, public sector could be 10% of our business, but it's going to take some time to get there. We're doing very well within state and local section of government that doesn't require that FedRAMP high, but it almost seems like the goalpost to move where we thought FedRAM model was going to be enough and now they're saying no, they need FedRAMP high.
我们正在研究数据主权问题，但英国在这方面也是一个好的选择。总的来说，公共部门可能占我们业务的10％，但到达这个水平需要一些时间。我们在不需要FedRAMP高标准的州和地方政府部门中表现非常出色，但现在似乎已经改变目标，过去我们认为FedRAMP模型已经足够，但现在他们说不行，需要FedRAMP高标准。
So hopefully it's very soon. And then IL-5 will be next. What's IL-5? It's another. I was sure it was. I was sure it was. Yes. IL-6 is, I don't think we'll ever get there, is probably the most rigorous.
希望很快就能有结果。接下来会是IL-5，IL-5是什么？它是另一种。我很确定它是。是的。IL-6可能是最严格的，我不认为我们能够研究它。
I'm curious though, so when you say we're waiting back to hear from them, who's them? Is it like a particular department or is it out of their circle? What happens is you have to do all this stuff and demonstrate that you have all the controls and everything in procedures in place to operate in a FedRAMP high and then you literally handle your files over to a third party to do an audit and you need a sponsor within the government and we have a sponsor within the government that has to pay for that third party to do the audit of everything for them to sign off on.
我很好奇，当你说我们在等待他们的回复时，他们是指谁？是特定部门还是超出他们的范围？情况是这样的，你必须完成所有这些事情，并证明你有所有的控制和程序来操作FedRAMP高级别，然后你实际上把你的文件交给第三方进行审计，你需要一个政府赞助商，我们有一个政府赞助商，必须支付第三方对所有内容进行审计的费用，以便他们签署批准。
Oh, is that how it works? Yes. They have to pay for it. It's a lot more than that. Yeah, but they have to pay for it. The sponsor has to pay for it. Yes. Interesting.
哦，是这样工作的吗？是的。他们必须付钱。这比那还多很多。是的，但是他们必须为此付钱。赞助商必须为此付钱。是的。有趣。
A minute and a half, what do you think investors don't get as well as they should about this story? You know, I can ask this question all the time. I thought I had the most original question. Is it not better or is it?
一分半钟的时间，你认为投资者对这个故事应该更好地理解的是什么？你知道，我经常问这个问题。我以为我问了最原创的问题。难道这不是更好，还是哪个更好？
You know, I think in general, investors get it. I'm not going to say they don't get anything. I think a lot of people don't appreciate that how long some of these migrations are going to take with customers and that we have some customers that it's going to take them 10 years before they have all their data move to Snowflake because their on-prem data state is so big and they move so slow.
你知道的，我认为一般来说，投资者都明白这个道理。我不会说他们一无所知。我觉得很多人不了解，某些用户的数据转移需要花费很长时间，甚至要花费10年左右，因为他们的本地数据太庞大，迁移速度非常缓慢，这一点很重要。
I think if you look at, and a lot of people think that we've been just a on-prem data warehouse migration, less than 20% of our businesses actually been these big on-prem data warehouses. We have a lot of new digitally native companies that were never a on-prem migration.
我认为，如果你看一下，很多人认为我们只是在进行本地数据仓库迁移，但实际上不到20%的业务是涉及这些大型本地数据仓库的。我们有很多新的数字原生公司，它们从未进行过本地迁移。
If you look at, we've signed up well over, I think it's over 1,500 on-prem data warehouse migrations of principally teradata and less than 100 of those customers have actually shut teradata down. Yeah. It's a long tail. Yeah. That is a durable opportunity.
如果你看看，我们已经签约了超过1500个本地数据仓库迁移项目，主要是Teradata，其中不到100个客户实际上关闭了Teradata。是的，这是一个长尾。这是一个持久的机会。
简单来说，这段话是在描述一个数据仓库服务的情况。他们已经成功签约了超过1500个客户，其中大部分都是Teradata的用户。但是只有不到100个客户实际上停止使用Teradata，这意味着这个服务仍然有持久的机会。
All right, Mike. It's always great to have you here. We appreciate it. Thank you. All right. All right. All right.
好的，迈克。你来这里总是很棒的，我们很感激。谢谢你。好的，好的，好的。