Startup Spotlight: Qubitro - Simplifying IoT

Michael Lynn
July 21, 2020 | Updated: July 24, 2020
#startups-old#IoT

Welcome to yet another in our Startup Spotlight series where we talk with founders, developers and participants of The MongoDB for Startups program to find out what they’re building, how they’re building it and how they’re achieving success.

Qubitro Logo

Today the Internet of Things is enabling companies to realize the business value of a connected world. Every day, the IoT connects billions of devices worldwide and that number is growing exponentially. To fully realize the value of IoT, you need to incorporate systems that support rapid change, flexibility and immense scale and performance. MongoDB is a platform that helps many companies in these specific areas. One of those companies building on the power and flexibility offered by MongoDB Atlas is Qubitro.

Qubitro has built a platform that enables developers and businesses to develop connected solutions faster and easier with its SaaS IoT platform and services.

Qubitro customers connect, monitor, and design automated pipelines with device data through an easy to use interface with zero setup and coding. Hardware designers and producers can develop their own client applications with Qubitro's REST APIs without worrying about scalability, maintenance, and performance.

Beray Bentesen is CEO, and Founder of Qubitro. I’ve had the privilege of working with Beray since they entered the MongoDB for Startups program in December of 2019. Since then, they’ve launched their platform in Beta so we thought it would be interesting to have a chat with Beray and find out how they’re doing.

Tell us a little bit about Qubitro

Qubitro Dashboard

There are two major problems with the current methods of designing and developing connected IoT solutions. Put simply, these problems are cost and technical difficulties.

Although there are IoT services available, most of these still require you to manage your data, build a user-interface, manually create and configure integrations in addition to the other common requirements for the most IoT scenarios.

Our goal is to offer a fully managed solution that continuously evolves by collecting and leveraging user feedback so that developers and IoT businesses don’t need to worry about the human resource and infrastructure costs.

Our goal is to offer the best possible user experience in addition to offering the best possible technology solutions. This is why we spend so much time on what might seem like minor details such as colors, user interaction and even documentation.

We want to get the benefit of existing IoT devices and their data, and combine this with our experience. We are taking advantage of the latest software and hardware solutions to utilize the best performance possible.

Qubitro will be available for any kind of user from students to large enterprises by offering a ‘pay as you connect’ pricing model instead of fixed or complex pricing methods.

What difference has MongoDB made in your journey as a founder, and for your startup?

Being able to prototype fast is a well-known demand of startups but in our case, being able to develop continuously without breaking the entire system while gaining the experience and receiving feedback from the users was the key.

MongoDB made us able to prototype fast, implement and update features quickly. We still use the almost same core architectural design for the entire platform. MongoDB made a difference by having well-documented examples and libraries. We even made a major migration from Python to Go without worrying about the syntax.

In addition to that, as a founder, you must rely on the experience of your team, and being able to expand, finding experienced people, or even making necessary replacements in a team is dependent on the technology that is used. Not-thinking about the what-if scenarios enabled me to focus on the development and the business thanks to MongoDB.

How did you choose MongoDB for your platform?

We had to choose a solution by considering the following conditions:

  • We couldn’t go with vendor-locked services/products since the entire platform should work on any platform
  • Managed service options to be able to develop without worrying about the backup, maintenance etc
  • It had to have on-premise support (Kubernetes operator was a bonus for our case)
  • Documentation & language support
  • Additional tools (MongoDB Compass etc.)

How does MongoDB work for you and what specific products and services are you relying on from the MongoDB platform?

We chose Atlas so that we can get benefits like:

  • Global clusters to have globally distributed, location-aware operations
  • Auto-scale and managed maintenance
  • Security must be built-in
  • Being able to choose cloud provider We have independent and separate clusters globally distributed for both device data and the core collections.

We currently have a monolith REST API project as well as microservices that are both written in Go and run on Kubernetes clusters that are also distributed globally on multiple cloud platforms.

We have an MQTT Brokers that are bridged to MongoDB with distributed pub-sub messaging systems written in Erlang. We also use MongoDB for device authentication, ACLs (Access control lists ) for devices, and information like device status.

We are also using additional solutions for things like automated backups, synchronization of the device data etc. that are not directly connected with MongoDB.

Our challenge is to work with dynamic data. In addition to dynamic IoT device data, we store information like coordinates, chart/widget colors, SQL syntax for the rule engine, credentials for integrations, user and project-related data, and so on. Offering all this stuff through the user interface requires flexibility which MongoDB is pretty good at.

We also use MongoDB Compass for the quick visualization of data, metrics on Atlas to monitor clusters as well as alerts that are connected to Slack and other custom webhook notifications. What’s the biggest impact that using MongoDB is having on your business?

MongoDB Atlas is working pretty solid and allows us to cut costs, but more importantly save time. Everything is working smoothly and we can get the benefits of built-in security, monitoring tools, and other services like The Performance Advisor.

How do you work with MongoDB, Inc.? We are part of the MongoDB for Startups program which includes benefits like 1:1 mentoring and technical support. It’s a great opportunity for us to be part of this program and we are looking forward to achieving more.

Anything else you’d like to share about Qubitro?

The current stage is built on top of the long-time experience on IoT solutions and community feedback. We are still scratching the surface while adopting the upcoming technologies like EDGE/Fog computing without increasing the complexity of the user experience.

Registration for the Beta is free and we are looking forward to reaching as many people as possible around the globe to fix existing bugs and make the product great.

← Previous

Forbes: MongoDB Cloud Migration Helps World's Biggest Media Brand Continue To Set Standard For Digital Innovation

Forbes's migration to MongoDB Cloud

July 21, 2020
Next →

Empower Modern App Developers with Document Databases

Across industries, business success depends on a company’s ability to deliver new digital experiences through software. The speed at which a company can develop and deploy a new application with innovative features is a direct lever on business outcomes. Given the vital role developers play in the success of your business, it stands to reason that equipping them with the tools to maximize their productivity is in your best interest. Unfortunately, many organizations are unaware of the tax they’re placing on their development teams by using a relational database. While the relational database has been a bedrock for data-driven applications for 50 years, it was developed in an era before the internet and is a poor fit as the foundation of today’s web and mobile applications. Document databases, which have emerged over the past decade, have cemented themselves as the most popular and widely used alternative to the tabular model found in traditional relational databases. Document databases have become so powerful that even relational databases are trying to emulate them. Built around JavaScript Object Notation (JSON)–like documents, document databases are intuitive for developers to use. Instead of the rigid row-and-column structure of the relational model, document databases map documents directly to objects in code, which is how coders naturally think of and work with data. Let’s break down the key advantages to document databases in building modern applications. We’ll see why the document model’s flexibility eliminates the complex intergroup dependencies that have traditionally slowed developers down. The limitations of the relational database model Relational databases add complexity to a developer’s workload, severely hampering the velocity of work. The rigid row-and-column structure creates a mismatch between the way developers think of code and data, and how they need to store it. Additionally, while the relational model was fine in an age when most applications used a small pre-set of attributes such as last names, ZIP codes, and state abbreviations, the majority of data collected by organizations today is rich in structure. We have given names and sometimes preferred names. We have unique attributes that are relevant to only some of us: For example, people with PhDs have dissertation topics, sports lovers have favorite sports, and our families come in every conceivable shape and size. This richly structured data reflects how we actually think about the real world, and it’s very difficult to flatten, store, analyze, or query using rows and columns. With relational databases, developers can feel stuck in quicksand with changes to their applications requiring them to carefully collaborate with experts like database administrators (DBAs) who help them translate their schemas and queries to underlying relational data models to ensure that indexing strategies are appropriately employed. The layer of indirection increases cognitive load, is hard to reason about, and slows everything down. More than half of application changes require database schema modifications. Those database modifications take longer to complete than the application changes they are designed to support. You can quickly see why these complicated efforts severely slow the delivery of new software features into production. Enabling development of modern apps with document databases With the birth of the internet and the proliferation of mobile and web apps, developers’ roles evolved. The emergence of robust development frameworks, which abstracted away underlying complexity, and the rise of DevOps led organizations to consolidate developer functions. The new generation of full-stack developers wanted databases that better addressed their applications’ requirements and their ways of working with data. The founders of MongoDB recognized a need for a modern database solution while at the adtech giant DoubleClick in 2007. They still were unable to scale to the 400,000 transactions per second the business required due to the constraints of the relational model. These challenges inspired them to create a new, modern, general-purpose database. This database could address the shortcomings of the relational data model and offer a solution that developers actually wanted to use. The result was a horizontally scalable, document-based NoSQL database called MongoDB. The document database model in general, and MongoDB more specifically, addresses the limitations of relational databases in several notable ways: Intuitive data model: The documents at the center of document databases have a universal data format. JSON is a lightweight, language-independent, and human-readable format that has become a widely used standard for storing and exchanging data. These documents map directly to data structures in popular programming languages so there is no need for the additional mapping layer often used with relational databases. Because data that is accessed together is stored together, there is less code to write; developers don’t need to decompose data across tables or run joins. Flexible schema: These JSON-like documents are flexible. Each document can have its own fields, and there’s no need to pre-define the schema in the database. It can be modified at any time. That flexibility enhances developer agility. Meeting user expectations while simplifying application architectures The most innovative applications we use in our daily lives — think Netflix and Instagram — have raised user expectations for what every application should be. Today we expect applications to be: Highly responsive Able to deliver relevant information Optimized for mobile devices Secure Powered by real-time insights Continuously improved Meeting those expectations can be extremely challenging, especially for developers using relational databases. A typical data infrastructure built around a legacy relational database can trap your development team in overly complex and siloed architectures. Document databases, on the other hand, can simplify application architectures. Documents are a superset of all other data models, so developers can store and work with a variety of data types. Development teams can accommodate most of their use cases in a single data model and database The document data model can help your developers overcome the limitations of the relational model while improving their productivity and velocity. Allowing them to minimize the undifferentiated work of maintaining their infrastructure and to focus on meeting demanding user expectations. As a result, they can deliver better, more innovative applications faster than before. Click here to read the original article published on The New Stack.

June 5, 2023