Brian Mullen, Chief Marketing Officer at InfluxData, joins Corey on Screaming in the Cloud to discuss the complexity of time-series data and how InfluxDB is providing behind-the-scenes support in the world of IoT. Brian reveals some of the companies using InfluxDB so listeners can get an understanding of how InfluxDB is making everyday experiences possible, as well as InfluxDB’s new storage engine which allows for previously impossible query speed. Listen in to find out why Brian feels data is best understood through the lens of time, and how InfluxDB users approach their open-source offering.
Brian is an accomplished dealmaker with experience ranging from developer platforms to mobile services. Before InfluxData, Brian led business development at Twilio. Joining at just thirty-five employees, he built over 150 partnerships globally from the company’s infancy through its IPO in 2016. He led the company’s international expansion, hiring its first teams in Europe, Asia, and Latin America. Prior to Twilio Brian was VP of Business Development at Clearwire and held management roles at Amp’d Mobile, Kivera, and PlaceWare.
Announcer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.
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to understand more.
Corey: Welcome to Screaming in the Cloud
. I’m Corey Quinn. It’s been a year, which means it’s once again time to have a promoted guest episode brought to us by our friends at InfluxData
. Joining me for a second time is Brian Mullen, CMO over at InfluxData. Brian, thank you for agreeing to do this a second time. You’re braver than most.
Brian: Thanks, Corey. I’m happy to be here. Second time is the charm.
Corey: So, it’s been an interesting year to put it mildly and I tend to have the attention span of a goldfish of most days, so for those who are similarly flighty, let’s start at the very top. What is an InfluxDB slash InfluxData slash Influx—when you’re not sure which one to use, just shorten it and call it good—and why might someone need it?
Brian: Sure. So, InfluxDB is what most people understand our product as, a pretty popular open-source product, been out for quite a while. And then our company, InfluxData is the company behind InfluxDB. And InfluxDB is where developers build IoT real-time analytics and cloud applications, typically all based on time series. It’s a time-series data platform specifically built to handle time-series data, which we think about is any type of data that is stamped in time in some way.
It could be metrics, like, taken every one second, every two seconds, every three seconds, or some kind of event that occurs and is stamped in time in some way. So, our product and platform is really specialized to handle that technical problem.
Corey: When last we spoke, I contextualized that in the realm of an IoT sensor that winds up reporting its device ID and its temperature at a given timestamp. That is sort of baseline stuff that I think aligns with what we’re talking about. But over the past year, I started to see it in a bit of a different light, specifically viewing logs as time-series data, which hadn’t occurred to me until relatively recently. And it makes perfect sense, on some level. It’s weird to contextualize what Influx does as being a logging database, but there’s absolutely no reason it couldn’t be.
Brian: Yeah, it certainly could. So typically, we see the world of time-series data in kind of two big realms. One is, as you mentioned the, you know, think of it as the hardware or, you know, physical realm: devices and sensors, these are things that are going to show up in a connected car, in a factory deployment, in renewable energy, you know, wind farm. And those are real devices and pieces of hardware that are out in the physical world, collecting data and emitting, you know, time-series every one second, or five seconds, or ten minutes, or whatever it might be.
But it also, as you mentioned, applies to, call it the virtual world, which is really all of the software and infrastructure that is being stood up to run applications and services. And so, in that world, it could be the same—it’s just a different type of source, but is really kind of the same technical problem. It’s still time-series data being stamped, you know, data being stamped every, you know, one second, every five seconds, in some cases, every millisecond, but it is coming from a source that is actually in the infrastructure. Could be, you know, virtual machines, it could be containers, it could be microservices running within those containers. And so, all of those things together, both in the physical world and this infrastructure world are all emitting time-series data.
Corey: When you take a look at the broader ecosystem, what is it that you see that has been the most misunderstood about time-series data as a whole? For example, when I saw AWS talking about a lot of things that they did in the realm of for your data lake, I talked to clients of mine about this and their response is, “Well, that’d be great genius, if we had a data lake.” It’s, “What do you think those petabytes of nonsense in S3 are?” “Oh, those are the logs and the assets and a bunch of other nonsense.” “Yeah, that’s what other people are calling a data lake.” “Oh.” Do you see similar lights-go-on moment when you talk to clients and prospective clients about what it is that they’re doing that they just hadn’t considered to be time-series data previously?
Brian: Yeah. In fact, that’s exactly what we see with many of our customers is they didn’t realize that all of a sudden, they are now handling a pretty sizable time-series workload. And if you kind of take a step back and look at a couple of pretty obvious but sometimes unrecognized trends in technology, the first is cloud applications in general are expanding, they’re both—horizontally and vertically. So, that means, like, the workloads that are being run in the Netflix’s of the world, or all the different infrastructure that’s being spun up in the cloud to run these various, you know, applications and services, those workloads are getting bigger and bigger, those companies and their subscriber bases, and the amount of data they’re generating is getting bigger and bigger. They’re also expanding horizontally by region and geography.
So Netflix, for example, running not just in the US, but in every continent and probably every cloud region around the world. So, that’s happening in the cloud world, and then also, in the IoT world, there’s this massive growth of connected devices, both net-new devices that are being developed kind of, you know, the next Peloton or the next climate control unit that goes in an apartment or house, and also these longtime legacy devices that are been on the factory floor for a couple of decades, but now are being kind of modernized and coming online. So, if you look at all of that growth of the data sources now being built up in the cloud and you look at all that growth of these connected devices, both new and existing, that are kind of coming online, there’s a huge now exponential growth in the sources of data. And all of these sources are emitting time-series data. You can just think about a connected car—not even a self-driving car, just a connected car, your everyday, kind of, 2022 model, and nearly every element of the car is emitting time-series data: its engine components, you know, your tires, like, what the climate inside of the car is, statuses of the engine itself, and it’s all doing that in real-time, so every one second, every five seconds, whatever.
So, I think in general, people just don’t realize they’re already dealing with a substantial workload of time series. And in most cases, unless they’re using something like Influx, they’re probably not, you know, especially tuned to handle it from a technology perspective.
Corey: So, it’s been a year. What has changed over on your side of the world since the last time we spoke? It seems that well, things continue and they’re up and to the right. Well, sure, generally speaking, you’re clearly still in business. Good job, always appreciative of your custom, as well as the fact that oh, good, even in a world where it seems like there’s a macro recession in progress, that there are still companies out there that continue to persist and in some cases, dare I say, even thrive? What have you folks been up to?
Brian: Yeah, it’s been a big year. So first, we’ve seen quite a bit of expansion across the use cases. So, we’ve seen even further expansion in IoT, kind of expanding into consumer, industrial, and now sustainability and clean energy, and that pairs with what we’ve seen on FinTech and cryptocurrency, gaming and entertainment applications, network telemetry, including some of the biggest names in telecom, and then a little bit more on the cloud side with cloud services, infrastructure, and dev tools and APIs. So, quite a bit more broad set of use cases we’re now seeing across the platform. And the second thing is—you might have seen it in the last month or so—is a pretty big announcement we had of our new storage engine.
So, this was just announced earlier this month in November and was previously introduced to our community as what we call an IOx, which is how it was known in the open-source. And think of this really as a rebuilt and reimagined storage engine which is built on that open-source project, InfluxDB IOx that allows us to deliver faster queries, and now—pretty exciting for the first time—unlimited time-series, or cardinality as it’s known in the space. And then also we introduced SQL for writing queries and BI tool support. And this is, for the first time we’re introducing SQL, which is world’s most popular data programming language to our platform, enabling developers to query via the API our language Flux, and InfluxQL in addition.
Corey: A long time ago, it really seems that the cloud took a vote, for lack of a better term, and decided that when it comes to storage, object store is the way forward. It was a bit of a reimagining from how we all considered using storage previously, but the economics are at minimum of ten to one in favor of objects store, the latency is far better, the durability is off the charts better, you don’t have to deal—at least in AWS-land—with the concept of availability zones and the rest, just from an economic and performance perspective, provided the use case embraces it, there’s really no substitute.
Brian: Yeah, I mean, the way we think about storage is, you know, obviously, it varies quite a bit from customer to customer with our use cases. Especially in IoT, we see some use cases where customers want to have data around for months and in some cases, years. So, it’s a pretty substantial data set you’re often looking at. And sometimes those customers want to downsample those, they don’t necessarily need every single piece of minutia that they may need in real-time, but not in summary, looking backward. So, you really—we’re in this kind of world where we’re dealing with both hive fidelity—usually in the moment—data and lower fidelity, when people can downsample and have a little bit more of a summarized view of what happened.
So, pretty unique for us and we have to kind of design the product in a way that is able to balance both of those because that’s what, you know, the customer use cases demand. It’s a super hard problem to solve. One of the reasons that you have a product like InfluxDB, which is specialized to handle this kind of thing, is so that you can actually manage that balance in your application service and setting your retention policy, et cetera.
Corey: That’s always been something that seemed a little on the odd side to me when I’m looking at a variety of different observability tools, where it seems that one of the key dimensions that they all tend to, I guess, operate on and price on is retention period. And I get it; you might not necessarily want to have your load balancer logs from 2012 readily available and paying for the privilege, but it does seem that given the dramatic fall of archival storage pricing, on some level, people do want to be able to retain that data just on the off chance that will be useful. Maybe that’s my internal digital packrat chiming in at this point, but I do believe strongly that there is a correlation between how recent the data is and how useful it is, for a variety of different use cases. But that’s also not a global truth. How do you view the divide? And what do you actually see people saying they want versus what they’re actually using?
Brian: It’s a really good question and not a simple problem to solve. So, first of all, I would say it probably really depends on the use case and the extent to which that use case is touching real world applications and services. So, in a pure observability setting where you’re looking at, perhaps more of a, kind of, operational view of infrastructure monitoring, you want to understand kind of what happened and when those tend to be a little bit more focused on real-time and recent. So, for example, you of course, want to know exactly what’s happening in the moment, zero in on whatever anomaly and kind of surrounding data there is, perhaps that means you’re digging into something that happened in you know, fairly recent time. So, those do tend to be, not all of them, but they do tend to be a little bit more real-time and recent-oriented.
I think it’s a little bit different when we look at IoT. Those generally tend to be longer timeframes that people are dealing with. Their physical out-in-the-field devices, you know, many times those devices are kind of coming online and offline, depending on the connectivity, depending on the environment, you can imagine a connected smart agriculture setup, I mean, those are a pretty wide array of devices out and in, you know, who knows what kind of climate and environment, so they tend to be a little bit longer in retention policy, kind of, being able to dig into the data, what’s happening. The time frame that people are dealing with is just, in general, much longer in some of those situations.
Corey: One story that I’ve heard a fair bit about observability data and event data is that they inevitably compose down into metrics rather than events or traces or logs, and I have a hard time getting there because I can definitely see a bunch of log entries showing the web servers return codes, okay, here’s the number of 500 errors and number of different types of successes that we wind up seeing in the app. Yeah, all right, how many per minute, per second, per hour, whatever it is that makes sense that you can look at aberrations there. But in the development process at least, I find that having detailed log messages tell me about things I didn’t see and need to understand or to continue building the dumb thing that I’m in the process of putting out. It feels like once something is productionalized and running, that its behavior is a lot more well understood, and at that point, metrics really seem to take over. How do you see it, given that you fundamentally live at that intersection where one can become the other?
Brian: Yeah, we are right at that intersection and our answer probably would be both. Metrics are super important to understand and have that regular cadence and be kind of measuring that state over time, but you can miss things depending on how frequent those metrics are coming in. And increasingly, when you have the amount of data that you’re dealing with coming from these various sources, the measurement is getting smaller and smaller. So, unless you have, you know, perfect metrics coming in every half-second, or you know, in some sub-partition of that, in milliseconds, you’re likely to miss something. And so, events are really key to understand those things that pop up and then maybe come back down and in a pure metric setting, in your regular interval, you would have just completely missed. So, we see most of our use cases that are showing a balance of the two is kind of the most effective. And from a product perspective, that’s how we think about solving the problem, addressing both.
Corey: One of the things that I struggled with is it seems that—again, my approach to this is relatively outmoded. I was a systems administrator back when that title was not considered disparaging by a good portion of the technical community the way that it is today. Even though the job is the same, we call them something different now. Great. Okay, whatever smile, nod, and accept the larger paycheck.
But my way of thinking about things are okay, you have the logs, they live on the server itself. And maybe if you want to be fancy, you wind up putting them to a centralized rsyslog cluster or whatnot. Yes, you might send them as well to some other processing system for visibility or a third-party monitoring system, but the canonical truth slash source of logs tends to live locally. That said, I got out of running production infrastructure before this idea of ephemeral containers or serverless functions really became a thing. Do you find that these days you are the source of truth slash custodian of record for these log entries, or do you find that you are more of a secondary source for better visibility and analysis, but not what they’re going to bust out when the auditor comes calling in three years?
Brian: I think, again, it—[laugh] I feel like I’m answering the same way [crosstalk 00:15:53]
Corey: Yeah, oh, and of course, let’s be clear, use cases are going to vary wildly. This is not advice on anyone’s approach to compliance and the rest [laugh]. I don’t want to get myself in trouble here.
Brian: Exactly. Well, you know, we kind of think about it in terms of profiles. And we see a couple of different profiles of customers using InfluxDB. So, the first is, and this was kind of what we saw most often early on, still see quite a bit of them is kind of more of that operator profile. And these are folks who are going to—they’re building some sort of monitor, kind of, source of truth for—that’s internally facing to monitor applications or services, perhaps that other teams within their company built.
And so that’s, kind of like, a little bit more of your kind of pure operator. Yes, they’re building up in the stack themselves, but it’s to pay attention to essentially something that another team built. And then what we’ve seen more recently, especially as we’ve moved more prominently into the cloud and offered a usage-based service with a, you know, APIs and endpoint people can hit, as we see more people come into it from a builder’s perspective. And similar in some ways, except that they’re still building kind of a, you know, a source of truth for handling this kind of data. But they’re also building the applications and services themselves are taken out to market that are in the hands of customers.
And so, it’s a little bit different mindset. Typically, there’s, you know, a little bit more comfort with using one of many services to kind of, you know, be part of the thing that they’re building. And so, we’ve seen a little bit more comfort from that type of profile, using our service running in the cloud, using the API, and not worrying too much about the kind of, you know, underlying setup of the implementation.
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Corey: So, I’ve been on record a lot saying that the best database is TXT records stuffed into Route 53, which works super well as a gag, let's be clear, don’t actually build something on top of this, that’s a disaster waiting to happen. I don’t want to destroy anyone’s career as I do this. But you do have a much more viable competitive threat on the landscape. And that is quite simply using the open-source version of InfluxDB. What is the tipping point where, “Huh, I can run this myself,” turns into, “But I shouldn’t. I should instead give money to other people to run it for me.”
Because having been an engineer, where I believe I’m the world’s greatest everything, when it comes to my environment—a fact provably untrue, but that hubris never quite goes away entirely—at what point am I basically being negligent not to start dealing with you in a more formalized business context?
Brian: First of all, let me say that we have many customers, many developers out there who are running open-source and it works perfectly for them. The workload is just right, the deployment makes sense. And so, there are many production workloads we’re using open-source. But typically, the kind of big turning point for people is on scale, scale, and overall performance related to that. And so, that’s typically when they come and look at one of the two commercial offers.
So, to start, open-source is a great place to, you know, kind of begin the journey, check it out, do that level of experimentation and kind of proof of concept. We also have 60,000-plus developers using our introductory cloud service, which is a free service. You simply sign up and can begin immediately putting data into the platform and building queries, and you don’t have to worry about any of the setup and running servers to deploy software. So, both of those, the open-source and our cloud product are excellent ways to get started. And then when it comes time to really think about building in production and moving up in scale, we have our two commercial offers.
And the first of those is InfluxDB Cloud, which is our cloud-native fully managed by InfluxData offering. We run this not only in AWS but also in Google Cloud and Microsoft Azure. It’s a usage-based service, which means you pay exactly for what you use, and the three components that people pay for our data in, number of queries, and the amount of data you store in storage. We also for those who are interested in actually managing it themselves, we have InfluxDB Enterprise, which is a software subscription-base model, and it is self-managed by the customer in their environment. Now, that environment could be their own private cloud, it also could be on-premises in their own data center.
And so, lots of fun people who are a little bit more oriented to kind of manage software themselves rather than using a service gear toward that. But both those commercial offers InfluxDB Cloud and InfluxDB Enterprise are really designed for, you know, massive scale. In the case of Cloud, I mentioned earlier with the new storage engine, you can hit unlimited cardinality, which means you have no limit on the number of time series you can put into the platform, which is a pretty big game-changing concept. And so, that means however many time-series sources you have and however many series they’re emitting, you can run that without a problem without any sort of upper limit in our cloud product. Over on the enterprise side with our self-managed product, that means you can deploy a cluster of whatever size you want. It could be a two-by-four, it could be a four-by-eight, or something even larger. And so, it gives people that are managing in their own private cloud or in a data center environment, really their own options to kind of construct exactly what they need for their particular use case.
Corey: Does your object storage layer make it easier to dynamically change clusters on the fly? I mean, historically, running things in a pre-provisioned cluster with EBS volumes or local disk was, “Oh, great. You want to resize something? Well, we’re going to be either taking an outage or we’re going to be building up something, migrating data live, and there’s going to be a knife-switch cutover at some point that makes things relatively unfortunate.” It seems that once you abstract the storage layer away from anything resembling an instance that you would be able to get away from some of those architectural constraints.
Brian: Yeah, that’s really the promise, and what is delivered in our cloud product is that you no longer, as a developer, have to think about that if you’re using that product. You don’t have to think about how big the cluster is going to be, you don’t have to think about these kind of disaster scenarios. It is all kind of pre-architected in the service. And so, the things that we really want to deliver to people, in addition to the elimination of that concern for what the underlying infrastructure looks like and how its operating. And so, with infrastructure concerns kind of out of the way, what we want to deliver on are kind of the things that people care most about: real-time query speed.
So, now with this new storage engine, you can query data across any time series within milliseconds, 100 times faster queries against high cardinality data that was previously impossible. And we also have unlimited time-series volume. Again, any total number of time series you have, which is known as cardinality, is now able to run without a problem in the platform. And then we also have kind of opening up, we’re opening up the aperture a bit for developers with SQL language support. And so, this is just a whole new world of flexibility for developers to begin building on the platform. And again, this is all in the way that people are using the product without having to worry about the underlying infrastructure.
Corey: For most companies—and this does not apply to you—their core competency is not running time-series databases and the infrastructure attendant thereof, so it seems like it is absolutely a great candidate for, “You know, we really could make this someone else’s problem and let us instead focus on the differentiated thing that we are doing or building or complaining about.”
Brian: Yeah, that’s a true statement. Typically what happens with time-series data is that people first of all, don’t realize they have it, and then when they realize they have time-series data, you know, the first thing they’ll do is look around and say, “Well, what do I have here?” You know, I have this relational database over here or this document database over here, maybe even this, kind of, search database over here, maybe that thing can handle time series. And in a light manner, it probably does the job. But like I said, the sources of data and just the volume of time series is expanding, really across all these different use cases, exponentially.
And so, pretty quickly, people realize that thing that may be able to handle time series in some minor manner, is quickly no longer able to do it. They’re just not purpose-built for it. And so, that’s where really they come to a product like Influx to really handle this specific problem. We’re built specifically for this purpose and so as the time-series workload expands when it kind of hits that tipping point, you really need a specialized tool.
Corey: Last question, before I turn you loose to prepare for re:Invent, of course—well, I guess we’ll ask you a little bit about that afterwards, but first, we can talk a lot theoretically about what your product could or might theoretically do. What are you actually seeing? What are the use cases that other than the stereotypical ones we’ve talked about, what have you seen people using it for that surprised you?
Brian: Yeah, some of it is—it’s just really interesting how it connects to, you know, things you see every day and/or use every day. I mean, chances are, many people listening have probably use InfluxDB and, you know, perhaps didn’t know it. You know, if anyone has been to a home that has Tesla Powerwalls—Tesla is a customer of ours—then they’ve seen InfluxDB in action. Tesla’s pulling time-series data from these connected Powerwalls that are in solar-powered homes, and they monitor things like health and availability and performance of those solar panels and the battery setup, et cetera. And they’re collecting this at the edge and then sending that back into the hub where InfluxDB is running on their back end.
So, if you’ve ever seen this deployed like that’s InfluxDB running behind the scenes. Same goes, I’m sure many people have a Nest thermostat in their house. Nest monitors the infrastructure, actually the powers that collection of IoT data collection. So, you think of this as InfluxDB running behind the scenes to monitor what infrastructure is standing up that back-end Nest service. And this includes their use of Kubernetes and other software infrastructure that’s run in their platform for collection, managing, transforming, and analyzing all of this aggregate device data that’s out there.
Another one, especially for those of us that streamed our minds out during the pandemic, Disney+ entertainment, streaming, and delivery of that to applications and to devices in the home. And so, you know, this hugely popular Disney+ streaming service is essentially a global content delivery network for distributing all these, you know, movies and video series to all the users worldwide. And they monitor the movement and performance of that video content through this global CDN using InfluxDB. So, those are a few where you probably walk by something like this multiple times a week, or in our case of Disney+ probably watching it once a day. And it’s great to see InfluxDB kind of working behind the scenes there.
Corey: It’s one of those things where it’s, I guess we’ll call it plumbing, for lack of a better term. It’s not the sort of thing that people are going to put front-and-center into any product or service that they wind up providing, you know, except for you folks. Instead, it’s the thing that empowers a capability behind that product or service that is often taken for granted, just because until you understand the dizzying complexity, particularly at scale, of what these things have to do under the hood, it just—well yeah, of course, it works that way. Why shouldn’t it? That’s an expectation I have of the product because it’s always had that. Yeah, but this is how it gets there.
Brian: Our thesis really is that data is best understood through the lens of time. And as this data is expanding exponentially, time becomes increasingly the, kind of, common element, the common component that you’re using to kind of view what happened. That could be what’s running through a telecom network, what’s happening with the devices that are connected that network, the movement of data through that network, and when, what’s happening with subscribers and content pushing through a CDN on a streaming service, what’s happening with climate and home data in hundreds of thousands, if not millions of homes through common device like a Nest thermostat. All of these things they attach to some real-world collection of data, and as long as that’s happening, there’s going to be a place for time-series data and tools that are optimized to handle it.
Corey: So, my last question—for real this time—we are recording this the week before re:Invent 2022. What do you hope to see, what do you expect to see, what do you fear to see?
Brian: No fears. Even though it’s Vegas, no fears.
Corey: I do have the super-spreader event fear, but that’s a separate—
Corey: That’s a separate issue. Neither one of us are deep into the epidemiology weeds, to my understanding. But yeah, let’s just bound this to tech, let’s be clear.
Brian: Yeah, so first of all, we’re really excited to go there. We’ll have a pretty big presence. We have a few different locations where you can meet us. We’ll have a booth on the main show floor, we’ll be in the marketplace pavilion, as I mentioned, InfluxDB Cloud is offered across the marketplaces of each of the clouds, AWS, obviously in this case, but also in Azure and Google. But we’ll be there in the AWS Marketplace pavilion, showcasing the new engine and a lot of the pretty exciting new use cases that we’ve been seeing.
And we’ll have our full team there, so if you’re looking to kind of learn more about InfluxDB, or you’ve checked it out recently and want to understand kind of what the new capability is, we’ll have many folks from our technical teams there, from our development team, some our field folks like the SEs and some of the product managers will be there as well. So, we’ll have a pretty great collection of experts on InfluxDB to answer any questions and walk people through, you know, demonstrations and use cases.
Corey: I look forward to it. I will be doing my traditional Wednesday afternoon tour through the expo halls and nature walk, so if you’re listening to this and it’s before Wednesday afternoon, come and find me. I am kicking off and ending at the [unintelligible 00:29:15] booth, but I will make it a point to come by the Influx booth and give you folks a hard time because that’s what I do.
Brian: We love it. Please. You know, being on the tour is—on the walking tour is excellent. We’ll be mentally prepared. We’ll have some comebacks ready for you.
Corey: Therapists are standing by on both sides.
Brian: Yes, exactly. Anyway, we’re really looking forward to it. This will be my third year on your walking tour. So, the nature walk is one of my favorite parts of AWS re:Invent.
Corey: Well, I appreciate that. Thank you. And thank you for your time today. I will let you get back to your no doubt frenzied preparations. At least they are on my side.
Brian: We will. Thanks so much for having me and really excited to do it.
Corey: Brian Mullen, CMO at InfluxData, I’m Cloud Economist Corey Quinn and this is Screaming in the Cloud. If you’ve enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you’ve hated this podcast, please leave a five-star review on your podcast platform of choice, along with an insulting comment that you naively believe will be stored as a TXT record in a DNS server somewhere rather than what is almost certainly a time-series database.
Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit duckbillgroup.com
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