MongoDB Atlas, the fully managed cloud database, provides customers with pre-built and customizable alerts that can easily be configured for different channels, including Slack, Hipchat, PagerDuty, Flowdock, and more.
Due to popular demand, we’ve recently added Datadog as an optional endpoint for Atlas alerts. An increasing number of companies are using Datadog to monitor their entire application estate; this new integration will allow them to quickly get a sense of any database alerts from a dashboard they regularly view.
Setup is simple. Select a MongoDB Atlas Project, and click on “Settings” in the left-hand menu. Scroll down to “Datadog Settings” and paste in your Datadog API key.
Next, click on “Alerts” in the left-hand menu. You will see a screen that shows all alerting activity. Click on the green “Add” button in the upper right corner of your screen to create a new alert. You can now customize a new alert and specify “Datadog” as the endpoint.
To send an existing alert to Datadog, simply click on “Alert Settings” in the top navigation of your main Alerts screen. This will show you all of your existing alerts, and allow you to edit them using the same UI you use to create new alerts.
And that’s it. You should now start seeing MongoDB Atlas alerts in Datadog.
Not yet a MongoDB Atlas user? Create an account and get a free 512 MB database.
The Best Solutions Architects Work At MongoDB
Despite the bravado in the title, the purpose of this article is not to say that MongoDB Solutions Architects (SAs) are better than those working at other organizations. Rather, this article argues that the unique challenges encountered by SAs at MongoDB imply that successful MongoDB SAs are some of the best in the business. This assertion is derived from the unique challenges encountered by both supporting MongoDB customers and the MongoDB sales organization and breadth and depth of skills and knowledge required to be successful. To see why this is the case, let’s explore the role of an SA at MongoDB and the wide range of skills a Solutions Architect must master. A MongoDB SA (sometimes called a Sales Engineer in other organizations) is an engineer that supports the sales organization. The role is multi-faceted. A solutions architect must have: In-depth technical knowledge to both understand a customer’s technical challenges and to articulate how MongoDB addresses them Communication skills to present technical concepts in a clear and concise manner while tactfully dealing with skeptics and those more familiar with other technologies Sales skills to engage a prospect to learn their business challenges and the technical capabilities required to address those challenges Design and troubleshooting skills to assist prospects with designing solutions to complex problems and getting them back on track when things go wrong. The description above may make the MongoDB Solutions Architect role sound like other similar roles, but there are unique features of MongoDB (the product) and its competitive situation that make this role extremely challenging. We will explore this in the sections below. Technology While the strength of MongoDB and a major factor in its success has been the ease with which it can be adopted by developers, MongoDB is a complex product. Presenting MongoDB, answering questions, brainstorming designs, and helping resolve problems requires a wide range of knowledge including: The MongoDB query language Application development with MongoDB’s drivers in 10+ different programming languages Single and multi-data center architectures for high availability Tuning MongoDB to achieve the required level of performance, read consistency, and write durability Scaling MongoDB to manage TBs of data and thousands of queries per second Estimating the size of a cluster (or the cloud deployment costs) required to meet application requirements Best practices for MongoDB schema design and how to design the best MongoDB schema for a given application MongoDB Enterprise operations tools: Ops Manager , Compass , etc. Atlas : MongoDB’s Database as a Service Offering MongoDB’s various connectors: BI , Spark , and Hadoop Migration strategies from RDBMS (and other databases) to MongoDB This is a lot to know and there is a lot of complexity. In addition to the core knowledge listed above, knowledge of the internal workings of MongoDB is essential when working on designs for applications with high performance and scalability requirements. Therefore, most Solutions Architects understand MongoDB’s internal architecture, such as how the WiredTiger storage engine works or how a MongoDB cluster manages connections. To make the SA role even more challenging, organizations often choose MongoDB after failing with some other technology. (Maybe their RDBMS didn’t scale or it was too difficult to expand to handle new sources of data, or Hadoop processing did not meet real-time requirements, or some other NoSQL solution did not provide the required query expressibility and secondary indexes.) This means that MongoDB is often used for bleeding-edge applications that have never been built before. One of the roles of an SA is to understand the application requirements and help the application team come up with an initial design that will ensure their success 1 . It is probably obvious to experienced SAs, but SAs need to understand the capabilities, strengths, and weakness of all competing and tangential solutions as well. MongoDB’s biggest competitors are Oracle, Amazon, and Microsoft – all of whom are constantly evolving their product offerings and marketing strategies. An SA must always keep their knowledge up to date as the market evolves. Communication Being a great technologist is not enough. An SA spends at least as much time communicating with customers as they do working with technology. Communication is sometimes in the form of a standard presentation or demo, but it most often entails detailed technical conversations about how MongoDB works or how MongoDB can be used to address a particular problem. Concise technical explanations that address customer questions using language tailored to their particular situation and frame of reference are the hallmark of an SA. MongoDB SAs have to be comfortable communicating with a wide range of people, not just development teams. They must engage operations, line of business stakeholders, architects, and technology executives in sales discovery conversations and present the technical aspects of MongoDB of most concern at the appropriate level of detail. For example, an SA must be able to provide technology executives with an intuitive feel for why their development teams will be significantly more productive with MongoDB or will be able to deploy a solution that can meet scalability and performance requirements unattainable with previous technology approaches. Similarly, an SA must learn an operations team’s unique challenges related to managing MongoDB and describe how tools like Ops Manager and Atlas address these requirements. Public speaking skills are also essential. Solutions Architects deliver webinars, speak at conferences, write blog posts, and lead discussions and MongoDB User Groups (MUGs). Sales An SA is a member of the Sales organization and “selling” is a big part of the role. Selling involves many aspects. First, SAs assist the MongoDB Account Executives with discovery and qualification. They engage the customer in conversations to understand what their current problems are, their desired solution, the business benefits of the solution, the technical capabilities required to implement this solution, and how they'll measure success. After every customer conversation, SAs work with their Account Executives to refine their understanding of the customer’s situation and identify information that they want to gather at future meetings. Once the required technical capabilities are understood, it is the SA’s role to lead the sales activities that prove to the customer that (1) MongoDB meets all their required capabilities and (2) MongoDB meets these capabilities better than competing solutions. Most of the time this is accomplished via customer conversations, presentations, demonstrations, and design brainstorming meetings. Finally, customers sometimes want to test or validate that MongoDB will meet their technical required capabilities. This is often in the form of a proof of concept (POC) that might test MongoDB performance or scalability, the ease of managing MongoDB clusters with its operations tools, or that MongoDB’s BI Connector provides seamless connectivity with industry standard BI Tools, such as Tableau , Qlik , etc. SAs lead these POC efforts. They work with prospects to define and document the scope and success criteria and work with the prospect during the course of a POC to ensure success. Design and Troubleshooting I alluded to this in the “Technology” section: helping prospects with creative problem solving distinguishes SAs at MongoDB. Organizations will choose MongoDB if they believe and understand how they will be successful with it. Imparting this understanding (a big part of the Solutions Architect’s role) is typically done by helping an organization through some of the more thorny design challenges and implementation decisions. Organizations will choose MongoDB when they understand the framework of a good MongoDB design for their use case and believe all their design requirements will be met. Designing a solution is not a yes or no question that can be researched in the documentation, but is found through deep technical knowledge, careful analysis, and tradeoffs among many competing requirements. The best answer is often found through a collaborative process with the customer. SAs often lead these customer discussions, research solutions to the most challenging technical problems, and help craft the resulting design. Solutions Architects are also a source of internal innovation at MongoDB. Since Solutions Architects spend a significant amount of time speaking with customers, they are the first to realize when marketing or technical material is not resonating with customers or is simply difficult to understand. The pressure of short timelines and desire to be successful often results in innovative messaging and slides that are often adopted by MongoDB’s Product Marketing organization. Similar innovation often occurs with respect to MongoDB feature requests and enhancements. SAs are continually working with customers to help them solve problems and they quickly identify areas where MongoDB’s enhancements would provide significant value. The identification of these areas and specific recommendations from SAs on what product enhancements are required have played a big role in focusing the feature set of future MongoDB releases. Project Management Lastly, SAs often support a number of Account Executives and work on several dozen sales opportunities per quarter. This means that SAs are working a large number of opportunities simultaneously and must be highly organized to ensure that they are prepared for each activity and complete every follow-up item in a timely manner. It is not possible for an SA manager to track or completely understand every sales opportunity so SAs must be self-motivated and manage all their own activities. Summary Solutions Architecture at MongoDB is a challenging and rewarding role. The wide range of technical knowledge plus sales and communication skills required to be successful is common to SA roles. When you combine this with the need for SAs to design innovative solutions to complex (often previously unsolvable problems), the SAs have the set of skills and the track record of success that makes them the “best” in the business. If you want to join the best, check out the MongoDB Careers page . About the Author - Jay Runkel Jay Runkel is a principal solutions architect at MongoDB. For over 5 years, Jay has worked with Fortune 500 companies to architect enterprise solutions using non-relational document databases. Before MongoDB, Jay was a key team member at MarkLogic and Venafi, where he worked with financial services, medical, and media organizations to develop operational systems for analytics and custom publishing. He also has experience guiding large financial institutions, retailers, health care and insurance organizations to secure, protect, and manage their encryption assets. Jay has a BS in Applied Mathematics from Carnegie Mellon and a Masters in Computer Science from the University of Michigan. 1. My favorite part of the job is to get locked in a conference room and whiteboard for 4 hours with a development team to brainstorm the MongoDB solution/design for a particular use case. The most valuable end product of this session is not the design, but the development’s belief that they will be successful with MongoDB and that the development process will be easier than they expected. ↩
Engineering, Done DIRT Cheap: How an Outdated Data Architecture Becomes a Tax on Innovation
In March 2021, I wrote about The Innovation Tax : the idea that clunky processes and outdated technologies make it harder for engineering teams to produce excellent tech that delights customers. In the months since then, my thinking has evolved even further. I couldn’t have guessed how many technology leaders would immediately recognize these problems in their own organizations and share their own deep frustrations with me. This article puts that evolved thought together with the massive feedback that piece received. It will give you actionable ways to decrease your tax burden — and who wouldn’t want that? The innovation tax, like income tax, is real. Of course, it saps morale (with resulting attrition and churn), but it also has other financial and opportunity costs. Taxed organizations see their pace of innovation suffer as people and resources are locked into maintaining rather than innovating. We named this tax DIRT . Why? Well, it’s rooted in data (D), because it so often springs from the difficulty of using legacy databases to support modern applications that require access to real-time data to create rich user experiences. It affects innovation (I), because your teams have little time to innovate if they’re constantly trying to figure out how to support a complex and rickety architecture. It’s recurring (R), because it’s not as if you pay the tax (T) once and get it over with. Quite the opposite. DIRT makes each new project ever more difficult because it introduces so many components, frameworks, and protocols that need to be managed by different teams of people. In retrospect, it’s clear that technology leaders would recognize this tax and immediately grasp the degree to which it’s caused -- or cured -- by their data architecture. Data is sticky, strategic, heavy, intricate -- and the core of the modern digital company. Modern applications have much more sophisticated data requirements than the applications we were building only 10 years ago. Obviously, there is more data, but it’s more complicated than that: Companies are expected to react more quickly and more cleverly to all of the signals in that data. Legacy technologies, including single-model rigid, inefficient, and hard-to-program relational databases, just don’t cut it. In over 300 CxO conversations I've had since joining MongoDB in 2020, fewer than a handful of CTOs disputed this statement. When your tech stack can’t handle the demands of new applications, engineering teams will often bolt on single-purpose niche databases to do the job (think time series, text, graph, etc.). Then they’ll build a series of pipelines to move data back and forth. And everything will get slow and complicated — and even political. Time to polish up that LinkedIn profile. If this were rare, it wouldn’t be such a big deal. But large enterprises can have hundreds or thousands of applications, each with their own sources of data and their own pipelines. Over time, as data stores and pipelines multiply, an organization’s data architecture starts to look like a plate of spaghetti. Soon you’re operating and maintaining an entire middleware layer of ETL, ELT, and streaming. The variety of technologies, each with their own frameworks, protocols, and sometimes languages, makes it harder for developers to collaborate. It makes it extremely difficult to scale, because every architecture is bespoke and brittle. Developers spend their precious “flow” hours doing integration work instead of building new applications and features that the business needs and customers will love. Enterprise architects often end up spending their time on all the wrong things. It’s clear to me that most customers are ready for a new approach to data architecture. One of the best parts of my job is listening to and learning from other CxOs. Since the pandemic made it impossible to do that in person, MongoDB moved these discussions online, inviting technology leaders to hash out some of their biggest problems 1:1 and in groups with me. In one of those sessions, a CTO commented, “Technical debt should be carried on your CFO's balance sheet.” Even on Zoom, the power of that statement was clear. We also started looking at slide decks about data architecture from some of the best-known venture capital firms. Certainly VCs must position each of their portfolio companies as a critical player in the data architecture of the future. But the overall vision was not compelling. One technology leader said, “When I look at 20 net-new technologies I need to learn, it’s terrifying.” Others commented that just looking at these architecture diagrams was a little off-putting, because they knew their own organization’s data architecture was at least that complicated already. They knew they needed to simplify their data architecture, but more than one admitted to postponing this work -- indefinitely -- because it was just too daunting. I recently met with a major health care company whose executives think it’s just barely possible, but they are bravely diving in anyway, knowing that they must do it and that they’ll learn along the way as they tear down their monoliths. In many cases, the innovation tax manifests as the inability to even consider new technology because the underlying architecture is too complex and difficult to maintain, much less understand and transform. This is why a lot of senior people at enterprise companies are sitting with their fingers in the transformation dike, waiting for retirement -- they think they can’t modernize. It won’t surprise you that we also saw how MongoDB, as a general purpose database able to handle all types of data at speed and scale, could help solve this problem. Let me be clear. I’ve been working on or with databases for my entire 35-year career, and I joined MongoDB for a reason: I believe we can build the database and application-building environment that I’ve wanted to create and use for at least 30 of those years. Our vision of MongoDB goes beyond our namesake database to a broader, more versatile application data platform that allows you to accelerate and simplify how you build any type of application. It represents significant progress toward our larger goal, which remains the same as ever: to make data stunningly easy to work with. We want to see data become an enabler of innovation, not a blocker. And we want to finally allow technology teams to start to untangle their sprawl and get rid of their DIRT. Where to start? It’s good to have a better understanding of just how DIRT might be holding your teams back. Do your developers have trouble collaborating because the development environment is so fragmented? Do schema changes take longer to roll out than the application changes they’re designed to support? Do you have trouble building 360-degree views of your customers? And if so, why? These are all good places to start digging in the DIRT. You might also take a hard look at your applications and data sources, as well as what it would take to move your data onto an application data platform. That could mean identifying the objects in your applications and all the applications that interact with them. You could then assign a complexity score to each one based on attributes such as properties, methods, collections, and attributes. Now take a step back and identify each application that connects to each of those objects and rank it based on how mission-critical it is, how many people rely on it, how many tasks it has to perform, and the complexity of those tasks. Once you have a better handle on all this complexity, you’ll be better positioned to create a plan to move off your legacy systems, perhaps starting with the least complex and least integrated data sources. Of course, your metrics and your mileage will vary, but the point is to start. I don’t pretend any of this is easy. Like many of you, I’ve spent most of my career working on problems just like these. But that also means I know progress when I see it, and the beginning of a way for organizations to start to clean up their DIRT. I’ll be continuing to write more about these challenges and hopefully continue to add some perspective. If you’re curious to learn more about DIRT, you can download our white paper . As always, I’m eager to have you tweet your alignment, lack thereof, or other thoughts at @MarkLovesTech . You can also reach out to me on marklovestech.com , where you will find a compilation of my latest musings related to MongoDB and otherwise.