Jul
12
2018
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Datadog launches Watchdog to help you monitor your cloud apps

Your typical cloud monitoring service integrates with dozens of service and provides you a pretty dashboard and some automation to help you keep tabs on how your applications are doing. Datadog has long done that but today, it is adding a new service called Watchdog, which uses machine learning to automatically detect anomalies for you.

The company notes that a traditional monitoring setup involves defining your parameters based on how you expect the application to behave and then set up dashboards and alerts to monitor them. Given the complexity of modern cloud applications, that approach has its limits, so an additional layer of automation becomes necessary.

That’s where Watchdog comes in. The service observes all of the performance data it can get its paws on, learns what’s normal, and then provides alerts when something unusual happens and — ideally — provides insights into where exactly the issue started.

“Watchdog builds upon our years of research and training of algorithms on our customers data sets. This technology is unique in that it not only identifies an issue programmatically, but also points users to probable root causes to kick off an investigation,” Datadog’s head of data science Homin Lee notes in today’s announcement.

The service is now available to all Datadog customers in its Enterprise APM plan.

Jun
27
2018
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Intermix.io looks to help data engineers find their worst bottlenecks

For any company built on top of machine learning operations, the more data it has, the better it is off — as long as it can keep it all under control. But as more and more information pours in from disparate sources, gets logged in obscure databases and is generally hard (or slow) to query, the process of getting that all into one neat place where a data scientist can actually start running the statistics is quickly running into one of machine learning’s biggest bottlenecks.

That’s a problem Intermix.io and its founders, Paul Lappas and Lars Kamp, hope to solve. Engineers get a granular look at all of the different nuances behind what’s happening with some specific function, from the query all the way through all of the paths it’s taking to get to its end result. The end product is one that helps data engineers monitor the flow of information going through their systems, regardless of the source, to isolate bottlenecks early and see where processes are breaking down. The company also said it has raised seed funding from Uncork Capital, S28 Capital, PAUA Ventures along with Bastian Lehman, CEO of Postmates and Hasso Plattner, founder of SAP.

“Companies realize being data driven is a key to success,” Kamp said. “The cloud makes it cheap and easy to store your data forever, machine learning libraries are making things easy to digest. But a company that wants to be data driven wants to hire a data scientist. This is the wrong first hire. To do that they need access to all the relevant data, and have it be complete and clean. That falls to data engineers who need to build data assembly lines where they are creating meaningful types to get data usable to the data scientist. That’s who we serve.”

Intermix.io works in a couple of ways: First, it tags all of that data, giving the service a meta-layer of understanding what does what, and where it goes; second, it taps every input in order to gather metrics on performance and help identify those potential bottlenecks; and lastly, it’s able to track that performance all the way from the query to the thing that ends up on a dashboard somewhere. The idea here is that if, say, some server is about to run out of space somewhere or is showing some performance degradation, that’s going to start showing up in the performance of the actual operations pretty quickly — and needs to be addressed.

All of this is an efficiency play that might not seem to make sense at a smaller scale. The waterfall of new devices that come online every day, as well as more and more ways of understanding how people use tools online, even the smallest companies can quickly start building massive data sets. And if that company’s business depends on some machine learning happening in the background, that means it’s dependent on all that training and tracking happening as quickly and smoothly as possible, with any hiccups leading to real-term repercussions for its own business.

Intermix.io isn’t the first company to try to create some application performance management software. There are others like Data Dog and New Relic, though Lappas says that the primary competition from them comes in the form of traditional APM software with some additional scripts tacked on. However, data flows are a different layer altogether, which means they require a more unique and custom approach to addressing that problem.

May
03
2018
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Datadog provides visibility into Kubernetes apps with new container map

As companies turn increasingly to containerization, it creates challenges in terms of monitoring each individual container and the impact on the underlying application. This is particularly difficult because of the ephemeral nature of containers, which can exist for a very short time. Datadog introduced a container map product today that could help by bringing visualization to bear on the problem.

“With his announcement, what we are doing is introducing a container map to show you all of the containers across your system,” Ilan Rabinovitch, VP of Product Management at Datadog told TechCrunch. This could enable customers to see every container at any given time, organize them into groups based on tags, then drill-down to see what’s happening within each one.

The company makes use of tags and metadata to identify the different parts of the containers and their relationship to one another and the underlying infrastructure. The tool monitors containers much like any other entity in Datadog.

“Just as the host map does with individual instances, the container map enables you to easily group, filter, and inspect your containers using metadata such as services, availability zones, roles, partitions, or any other dimension you like,” the company wrote in a blog post introducing the new feature.

While Datadog won’t help a company directly remediate a problem as it avoids having write access to a company’s systems, the customer can use Web hooks or a serverless trigger like an Amazon Lambda function to invoke some sort of action should certain conditions be met that could compromise or break the application.

The company is simply acting as a third party watching to make sure the containers all behave properly. “We trust Kubernetes to do what it should do. But when something breaks, you need to be able to understand what happened, and Kubernetes is not designed to do this,” Rabinovitch said. The new map features provides that missing visibility into the container system and lets users drill down inside individual containers to pinpoint the source of a problem.

Apr
21
2018
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Through luck and grit, Datadog is fusing the culture of developers and operations

There used to be two cultures in the enterprise around technology. On one side were software engineers, who built out the applications needed by employees to conduct the business of their companies. On the other side were sysadmins, who were territorially protective of their hardware domain — the servers, switches, and storage boxes needed to power all of that software. Many a great comedy routine has been made at the interface of those two cultures, but they remained divergent.

That is, until the cloud changed everything. Suddenly, there was increasing overlap in the skills required for software engineering and operations, as well as a greater need for collaboration between the two sides to effectively deploy applications. Yet, while these two halves eventually became one whole, the software monitoring tools used by them were often entirely separate.

New York City-based Datadog was designed to bring these two cultures together to create a more nimble and collaborative software and operations culture. Founded in 2010 by Olivier Pomel and Alexis Lê-Quôc, the product offers monitoring and analytics for cloud-based workflows, allowing ops team to track and analyze deployments and developers to instrument their applications. Pomel said that “the root of all of this collaboration is to make sure that everyone has the same understanding of the problem.”

The company has had dizzying success. Pomel declined to disclose precise numbers, but says the company had “north of $100 million” of recurring revenue in the past twelve months, and “we have been doubling that every year so far.” The company, headquartered in the New York Times Building in Times Square, employs more than 600 people across its various worldwide offices. The company has raised nearly $150 million of venture capital according to Crunchbase, and is perennially on banker’s short lists for strong IPO prospects.

The real story though is just how much luck and happenstance can help put wind in the sails of a company.

Pomel first met Lê-Quôc while an undergraduate in France. He was working on running the campus network, and helped to discover that Lê-Quôc had hacked the network. Lê-Quôc was eventually disconnected, and Pomel would migrate to IBM’s upstate New York offices after graduation. After IBM, he led technology at Wireless Generation, a K-12 startup, where he ran into Lê-Quôc again, who was heading up ops for the company. The two cultures of develops and ops was glaring at the startup, where “we had developers who hated operations” and there was much “finger-pointing.”

Putting aside any lingering grievances from their undergrad days, the two began to explore how they could ameliorate the cultural differences they witnessed between their respective teams. “Bringing dev and ops together is not a feature, it is core,” Pomel explained. At the same time, they noticed that companies were increasingly talking about building on Amazon Web Services, which in 2009, was still a relatively new concept. They incorporated Datadog in 2010 as a cloud-first monitoring solution, and launched general availability for the product in 2012.

Luck didn’t just bring the founders together twice, it also defined the currents of their market. Datadog was among the first cloud-native monitoring solutions, and the superlative success of cloud infrastructure in penetrating the enterprise the past few years has benefitted the company enormously. We had “exactly the right product at the right time,” Pomel said, and “a lot of it was luck.” He continued, “It’s healthy to recognize that not everything comes from your genius, because what works once doesn’t always work a second time.”

While startups have been a feature in New York for decades, enterprise infrastructure was in many ways in a dark age when the company launched, which made early fundraising difficult. “None of the West Coast investors were listening,” Pomel said, and “East Coast investors didn’t understand the infrastructure space well enough to take risks.” Even when he could get a West Coast VC to chat with him, they “thought it was a form of mental impairment to start an infrastructure startup in New York.”

Those fundraising difficulties ended up proving a boon for Datadog, because it forced the company to connect with customers much earlier and more often than it might have otherwise. Pomel said, “it forced us to spend all of our time with customers and people who were related to the problem” and ultimately, “it grounded us in the customer problem.” Pomel believes that the company’s early DNA of deeply listening to customers has allowed it to continue to outcompete its rivals on the West Coast.

More success is likely to come as companies continue to move their infrastructure onto the cloud. Datadog used to have a roughly even mix of private and public cloud business, and now the balance is moving increasingly toward the public side. Even large financial institutions, which have been reticent in transitioning their infrastructures, have now started to aggressively embrace cloud as the future of computing in the industry, according to Pomel.

Datadog intends to continue to add new modules to its core monitoring toolkit and expand its team. As the company has grown, so has the need to put in place more processes as parts of the company break. Quoting his co-founder, Pomel said the message to employees is “don’t mind the rattling sound — it is a spaceship, not an airliner” and “things are going to break and change, and it is normal.”

Much as Datadog has bridged the gap between developers and ops, Pomel hopes to continue to give back to the New York startup ecosystem by bridging the gap between technical startups and venture capital. He has made a series of angel investments into local emerging enterprise and data startups, including Generable, Seva, and Windmill. Hard work and a lot of luck is propelling Datadog into the top echelon of enterprise startups, pulling New York along with it.

Jan
12
2016
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Investors Feed Datadog A Hefty $94.5 Million Round

Silly Morkie puppy sticking out his tongue while laying in a pile of one hundred dollar bills. Every startup needs a steady diet of funding to keep it strong and growing. Datadog, a monitoring service that helps customers bring together data from across a variety of infrastructure and software is no exception.
Today it announced a massive $94.5 million Series D Round. The company would not discuss valuation.
The round was led by ICONIQ Capital. Existing investors Index Ventures… Read More

Jan
28
2015
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Investors Throw Datadog A $31M Bone

Dog holding two one hundred dollar bills in its mouth like a bone. Datadog, a cloud service that helps customers monitor infrastructure and software, whether all in the cloud or a hybrid on-premises-cloud environment, announced $31M in Series C funding today. The round was led by Index Ventures with help from RTP Ventures, OpenView Venture Partners and what they referred to as “other equity holders.” Index and OpenView helped fund the Series… Read More

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