Leaf in the Wild: GeistM Uses Atlas to Thrive in MarTech

Can you start by telling us a little bit about your company?

GeistM is a hyper growth marketing technology (“MarTech”) business that provides a full, turnkey and innovative solution for digital marketing. We are the world’s fastest growing MarTech platform, listed in The Inc. 500 and Crain’s Fast 50, with clients including LG, Procter & Gamble, and HelloFresh. We have amassed 400B impressions resulting in 1.4BN for our clients.

We deliver performance driven marketing at scale because of our deep and wide use of technology, embodied in our integrated MarTech business operations platform—Blackfire. Blackfire is a proprietary technology that has distinct modules: Campaign Creation, Data Aggregator, Creative Asset Manager, & Attribution Analytics. All of these modules allow Geist to provide our clients with best in class marketing data, using these insights to further drive performance of our marketing campaigns.

How are you using MongoDB?

MongoDB is the database on which Blackfire is built, and as we extend to related applications, we continue to use it. MongoDB allows us to easily manage very large amounts of data that must be captured and enhanced within single collections, using other collections in the enhancement process.

By using MongoDB, we manage data without an object-relational impedance mismatch between our database data structures and object-oriented data structures in our Javascript code base. MongoDB not only improves the dissonance between formal specifications of data structures, but it also does the same for relationships. Our programmers are also able to treat the database as an integral part of their own work, rather than as a separate animal, eliminating the need for a separate DBA role. As our requirements are constantly evolving (sometimes we need to deliver changes in a single day), another critical feature of MongoDB is that we can alter the data model without going through a separate database delivery lifecycle. There is only one delivery lifecycle for both code and data changes.

Live Dashboard of Events

What were you using before MongoDB?

Blackfire was first prototyped with MySQL and Keen.io. Keen provided a hosted platform tailor-made for event capture, however, we quickly outgrew it and MySQL became a nuisance with our fluid data needs. At that point, we considered Cassandra, Redshift, and RDS, but the utter simplicity and independence of MongoDB had us hooked—within a day the best choice was obvious.

We migrated to MongoDB and managed it ourselves for one year with a self hosted MongoDB installation, but turned to MongoDB Atlas as our rapid growth became more daunting than ever.

Can you describe your MongoDB deployment and application stack?

We use MongoDB Atlas which comes with a three node replica set. Our applications are entirely Node and Javascript. We also use a good amount of AWS resources including Lambda, SQS, S3, API gateway and many more.

Can you share any best practices on scaling MongoDB or operations?

MongoDB’s ability to support multiple databases within a single cluster has allowed us to partition our large volume collections by client, keeping our environment simpler than it would be if we sharded, and offering increased client privacy at the same time. Using a recent subset of very large historical collections has also improved performance.

We found that by casting our data in multiple ways, for different consumption requirements, we can vastly improve availability and performance. Data storage is cheap, but search is expensive. We move our data through multiple states, effectively answering questions before they are asked at the expense of duplicative storage.

How is MongoDB performing for you?

It meets most of our needs so we are happy with it. Naturally, there are performance and scaling challenges. However, we have always been able to figure out a way to address these challenges in a timely fashion.

Can you elaborate on how you use the MongoDB query language and aggregation framework?

We consistently need to search through hundreds of millions, if not billions, of records spread across multiple collections in almost real time. This would be impossible without the MongoDB query language and aggregation framework.

The many high level aggregation operations that mirror typical data analysis requirements greatly reduces the amount of data manipulation code we need to write. This results in enhanced programmer efficiency, fewer programming errors, and fewer optimization concerns than if these operations had to be created by combining primitive operations.

What’s the biggest impact MongoDB is having on your business?

MongoDB facilitates running our business. We can rely on it and it delivers. We feel that very few options would be as effective as MongoDB and the investment in a category leader certainly minimizes risk. MongoDB’s accommodation of structural database changes during runtime has greatly decreased time to deliver the necessary changes. They also offer us advice, services, as well as excellent online courses through MongoDB University and frequent informational updates.

Do you have plans to use MongoDB for other applications?

Blackfire includes a data fabric subsystem for collecting data. The data collection process begins with data being collected from both internal and external sources. After this, data is normalized around a canonical internal form. Finally, the needed data is distributed to other systems for their use.

The fabric is unique in that the mappings, views and procedures, as well as the business collections, are defined within the system itself, and can be extended or modified at runtime. We are beginning the process of making this data fabric available to GiestM sister companies as a separate system.

What advice would you give someone who is considering using MongoDB for their next project?

Don’t write off Atlas because it is a bit cheaper to host it yourself. Atlas provides a great deal of support as well as backups, redundancy etc. My second piece of advice would be to make sure your data model reflects the business you are supporting. There are many reasons for not enforcing a schema on a collection as data requirements evolve, but it’s still important to have a structure that drives your data. If you create ad-hoc documents and collections, your application will get messy very quickly and you’ll miss out on optimization opportunities like the ability to organize your system into microservices.

Is there anything else you’d like to share about GeistM?

In addition to the Bionic nature of Blackfire, the secret to Geist’s success is that we are a teaching organization. Our people spend a significant amount of their time helping others learn new things, and whenever a new technique is discovered, it is shared with all concerned parties. This applies equally to our technology team, so our use of MongoDB continues to evolve through our interactions with each other.

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Performance Best Practices: Query Patterns and Profiling

Best practices for delivering performance at scale with MongoDB. Learn about optimal query patterns and tools to help you profile and tune your queries.

February 4, 2020
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Using MongoDB Skill Scanner to Build Better Training Programs

Technology leaders know that transformation is about more than just adopting modern technologies like MongoDB. The entire organization has to rally behind change — which is no easy task. The skills that modern development teams need are evolving faster than ever, and hiring to fill skills gaps can be too time-consuming and expensive of a process for many organizations. So it’s imperative that we plan for how we want to bring our people with us on our modernization journey, and proactively upskill them on the technologies we’re betting on. Because what happens if you choose MongoDB, but your developers don’t know how to use it? CIOs know that training programs are easier said than done. EY reported that 30% of CIOs acknowledge that their training programs are ineffective, and that they’re struggling to retain talent because of it. These leaders come to us to help them build and execute their MongoDB training programs , and seek advice on two extremely common yet critical challenges: How do we get away from the less effective one-size-fits-all approach? How do we measure the ROI of our training program and connect it to business impact? How we use MongoDB Skill Scanner to overcome training challenges Our Professional Services team uses a tool called MongoDB Skill Scanner to address both of these challenges. This tool helps us provide these three benefits to our customers looking to build a training program: Improve MongoDB proficiency: Teams can use Skill Scanner to quickly and easily assess the MongoDB skill gaps of their team members and gain a comprehensive understanding of their team’s MongoDB skills baseline. Increased productivity and accuracy: When team members have a comprehensive understanding of MongoDB, they are able to work more quickly and accurately on projects, leading to increased productivity and a higher quality of work. Save time and money with targeted Training: Using Skill Scanner, customers can avoid wasting time and money on trial-and-error learning. Instead, they can focus on improving their skills in a more targeted and efficient way with right-sized training plans. By leveraging this data, our customers’ engineers can engage in the right training at the right time, targeted for their job role and specific skill shortages. When a training program is built this way, engineers maximize their knowledge retention and minimize time away from their projects. Skill Scanner includes three role-based assessments, one for developers, database administrators, and DevOps respectively. Through a series of multiple choice questions, Skill Scanner provides customers with a clear understanding of their level of expertise across a set of technical skills that are critical for success in their role. After submitting the assessment, engineers will get results in each skill area outlining if they are beginner, intermediate, or advanced. Why data-driven training programs matter We’ve learned that it’s not enough to just tell teams to go watch training videos or webinars on their own, or to place everyone in the same one-size-fits-all program. Skills gaps vary from team to team, and individual to individual. The one-size-fits-all approach of some programs may not address individual learners' needs, wasting time and making it difficult for them to acquire new skills. By using Skill Scanner, we’re able to interpret this data to help determine which training courses your team should take. But we don’t only capture this data before doing training; we use Skill Scanner again after training programs are completed to see where immediate improvements have been made. This helps technology leaders prove the impact and ROI of their training, and gives them the confidence that their teams are ready to be successful with MongoDB. Developing a Precision Learning Program To go even further, our team can work with you to build a Precision Learning Program, where we use Skill Scanner data to build learning schedules that are unique to each individual. These schedules include a variety of short, blended, learning events such as classes, technical workshops, self-paced exercises, and project coaching. We’ve seen PLP lead to higher knowledge retention and of course, measurable project results. A customer who recently concluded their PLP saw a 43% increase in knowledge retention. Getting started building a personalized training program Skill gaps aren’t a novel problem IT leaders are facing. But with new digital courses, training, and technologies, the resources to close these gaps are at your fingertips. Skill Scanner and Precision Learning Program have been specifically designed to empower teams by offering targeted training that enhances their understanding of MongoDB. These short training events are carefully crafted to close skill gaps without compromising developer productivity. We’ve seen a variety of customers use this tool to help train their team’s individual needs, from needing to upskill new hires on their teams, projects with new MongoDB products, migrating to MongoDB Atlas, and more. It also saves your business the hours developers would've wasted searching for answers (and developers don’t want to spend their time that way, either). “We need help getting from point A to point B and feel MongoDB is uniquely positioned to help” — CTO at large insurance firm If you're interested in trying out MongoDB Skill Scanner or want to explore the MongoDB Precision Learning Program further, you can reach out to your account representative or contact us directly .

June 7, 2023