Data has become the lifeblood of business ecosystems, regardless of what industry you peer into. From gaming to healthcare, we see the use of developer data platforms rising as a means to manage vast amounts of data.
On average, 120 zettabytes of data are generated every day in 2023, and this is only expected to grow more in the coming decade. With data center capacity hitting scale limits and growing generative AI efforts expected to account for 10% of all data produced, it is more important than ever to have platforms that not only store data but also analyze, deploy, and reduce their complexity.
What is a Developer Data Platform?
A developer data platform is an in-depth system that facilitates the deployment of bespoke architectures to address application needs. With a unified environment, developers are able to manage data-centric workflows and infrastructure building blocks.
Basically, it is meant to streamline the development process by eliminating integration burdens and getting rid of fragmented development structures. Everything is simple to access, analyze, and distribute within the developer data platform. On top of that, it answers the problems met by infrastructure limitations because it reduces the sprawl and complexity of data infrastructure.
With the continued use of cloud storage and computing, newer developer data platforms also allow integration with the cloud. This allows developers to run the platform in automated and multi-cloud environments.
You’ll most often see use cases of developer data platforms in mobile development, e-commerce, financial services, and healthcare. This is because of the need to optimize processes, securely store and aggregate data, and manage vast amounts of data at any given moment. The reason that is so significant is that it can answer a lot of root issues that cause some of the biggest challenges faced by big data in 2023.
How to Use a Developer Data Platform
There are a number of providers that have launched developer data platforms like Tigris, Microsoft, and MongoDB. Although there are general aspects that will carry over across platforms, a good point to start with is MongoDB Atlas as it can be hosted on a cloud platform of your choosing. It also offers a lot of flexibility and has recently introduced five new capabilities to build new classes of applications using one DDP.
- The first thing to do is decide whether you want to go with a command line interface (CLI) or a user interface (UI). For the sake of simplicity, you may want to start out with the Atlas UI first. More experienced developers may want to go for CLI as it is generally faster and easier to scale as long as you take the time to learn all the commands. That said, Atlas UI also has a flexible UI that works well with automation and mass queries, so you wouldn’t necessarily be choosing a “lesser” choice if that’s what you go with.
- From there, you simply make an account and choose between organizations or projects. Although choosing between those is skippable, it’s worth making your choice ahead of time so you can group and define any users, resources, triggers, and clusters.
- Assuming you’ve already created your account, you can now get started with creating an Atlas cluster within a Project environment. You can choose between shared, dedicated, or multi-cloud clusters. The shared cluster is the most affordable choice, but this is because it makes use of shared network and hardware resources. The common choice is to go for a dedicated cluster unless you plan to scale and replicate your data across different regions across the globe. Going international would require a multi-cloud or multi-region cluster.
- With the UI, you just need to click “Create Cluster” and define any settings you want to apply. Now, you will have to enable network access so that you can actually connect to the cluster. The specifics will depend on your application, but you will have to generate a database connection string. You will have to check what supported drivers are necessary unless you’re already using Compass (which already has all drivers integrated).
Once you’ve got that up and running, it’s much easier to scale as needed. Data integration, processing, optimization, and management are simplified as long as you already have the environment set up. With experts predicting the future of data science to be largely baked in automation, operationalized models, and cloud computing, it’s a great time to get the basic know-how in DDP usage.