Developer Data Platform: A New Alternative To Working With Data

These days, data makes the world go around. On average, businesses collect and generate huge amounts of complex data. Big data and analytics have grown rapidly, and will only continue to explode as tech innovations come out of the woodwork. There is no sign of slowing down, so there is a need for resources to catch up.

Around 97.2% of businesses are investing in big data, AI, and business analytics. In the race to increase business value, IDC Worldwide Big Data and Analytics Spending Guide projects that worldwide spending will hit a compound annual growth rate (CAGR) of 12.8% from 2021 to 2025. Organizations are willing to invest, so it’s all about what funnels the best ROI.

MongoDB states that developers need a new way to work with data, which is where the developer data platform (DDP) comes in. With what is described as “a tightly integrated collection of data and application infrastructure building blocks”, a DDP revolutionizes the way developers and even managers can interact with data. Flexibility, simplicity, and cost-efficiency are all rolled into one thanks to a few core functions that should be of interest to anyone dealing with data.

Easing Data Integration Across The Board

The double-edged sword of today’s data landscape is that there are many data technologies available to use. More options ease the barrier of entry for many but also means having to spend a lot of time and money to ensure that applications and data management function correctly when used in various settings. Whether it’s a different data language or infrastructure, data integration can get hampered with all the testing and troubleshooting that has essentially become the norm.

With DDPs, this process no longer has to be a painful part of the development pipeline. The infrastructure inherently carries different APIs and support for different data formats. So, regardless of where the data is sourced from, you should be able to connect to and manage your data with relative simplicity. In the case of DDPs like Atlas, this translates well with organizations that have adopted Cloud Databases (DBaaS) as data comes from different existing clusters on demand. Whether the goal is to scale or deploy, multi-cloud and multi-model systems are the future of agile data development.

Unifying The Developer’s Experience

With the rapid pace of development, data produced and collected, and analytics faced by businesses at any given moment, developers are in need of ways to simultaneously work on projects with minimized risk. The margin of error may not be something that is top of mind for many leaders, but it’s worth noting that the majority of data loss happens due to system failures more than cybercrime.

A DDP solves this issue by defragmenting the developer journey. Different developers often must work with different data technologies and lakes but end up with contradictory results that hamper collaboration. How this works with a DDP is that the interface remains consistent and unified regardless of who or where the access point may be (with the proper access for security, naturally.) This can help ease the workload for teams that must work with varied query languages and systems while still sticking to production deadlines and compliance requirements.

Database, search engine, and sync mechanisms all work within the same environment. At the same time, developers can visualize, store, query, and visualize as needed in real-time.

Addressing Growing Data Models And Workloads

Developers and organizations alike seek ways to interact with data in a scalable and broad manner. This might feel contradictory given the specificity of data, but a DDP actually enables that. By addressing growing demands for data models, deployments, and different types of workloads, the biggest advantage to DDP is how it allows developers to use a flexible schema and scale horizontally.

With that, the need to reallocate resources and modify existing database values becomes a thing of the past. Developers can continue to build on their data and analyze it all as they go along while preserving the framework that has already been established. The best part about this is that it doesn’t come with added complications in architecture, so it’s not like you’re replacing one problem with another. It’s basically more freedom without the added risk that such freedom usually comes with.

As Data Science Central finds that big data analytics are increasingly used for innovation, risk identification, improving efficiency, and cost optimization, it makes sense that DDPs would rise above as the best way to maximize these inherent benefits.

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