A Quick Guide on How to Build a Data Platform in MongoDB

How to Build a Data Platform in MongoDB

In today's business landscape, one key to success is proper data management. Achieving this comes down to how a data platform is built, along with its performance. 

As the developer, it's up to you to ensure that the system always runs as error-free as possible.  In simple terms, a data platform is a group of programs and processes operating cohesively to address the data needs of an organization. This is where everything about handling data is defined, from how it's gathered up until delivery to end-users. 

This article looks into building a data platform in MongoDB, a widely-used database ecosystem. As with all other development projects, start by checking business requirements. There are different kinds of data platforms and you must ensure the one you'll build matches the project specs. 

Customer data platforms, for instance, are specially developed for managing customer-related info like business transaction details. There are also analytics data platforms which are geared towards research. Any type can be developed in MongoDB, as it uses non-relational databases that can support structured, semi-structured, and unstructured data. 

Building a data platform in MongoDB follows the framework of modern data architecture, which makes use of 'layers' or 'tiers'. Each of these corresponds to different aspects of data management. 

Layer 1: Source

This is where you set which sources of data will be used. Will the system pull information from third-party feeds or use real- time data? Will it rely on event-based information? Or is it a combination of everything? These are questions you must answer in this layer. 

In many cases, there's already a data warehouse and building a data platform is requested to improve workflow. Using existing data is a good starting point, and you may build on this by adding other sources as needed. 

Layer 2: Acquisition 

Next comes the process of how data will be acquired from sources. One option is through an API, like our app project for checking the weather for example, which uses the OpenWeatherMap API. 

The data platform may also integrate a stream processor, such as when it's configured to ingest files from live data streams. This part is for setting your extract, transform, and load (ETL) logic as well. You can leverage MongoDB's powerful feature called query table encryption for added ETL flexibility.

This enables client- side data encryption, allowing objects to be stored in the database as randomized encrypted data. What makes it unique is that expressive queries can still be run on the stored data even while at this state. This feature is the first of its kind in database software. 

Layer 3: Virtualized Storage

The third layer is for establishing the type of storage. Hybrid cloud is also used in MongoDB data platforms, which is a combination of on-premise and cloud storage. This is to provide support for legacy applications and at the same time have the advantages of cloud, like 24/7 availability and scalability. 

Layer 4: Data Governance, Application, and Analytics

Data governance entails setting protocols for data catalog and classification. In a customer data platform for example, one collection can be for customer details like emails and mobile numbers, while another is for invoices. 

Metadata management must also be covered including data models to be used as well as data sharing rules. Application is for integrating existing tools along with considering what applications need to be created. Addressing the need of MongoDB users, especially enterprise level organizations, might involve handling dozens of applications. 

Some even have over a hundred separate systems, many of which may be in silos or incompatible with others. Pro tip: If the project requires a Herculean effort, break down integration into chunks and apply priority labels depending on the importance of applications – e.g. critical, major, medium, low. 

The same applies with analytics, as the organization may have existing analytics tools, reports, and dashboards. Developers can also configure in-app analytics in MongoDB data platforms using features such as column store indexing that lets users handle purpose-built data indexes. Analytical queries may be performed without moving data or changing the document structure. 

Layer 5: Security 

The final layer is security, which covers log-in processes and authentication. Will the system follow conventional username/password protocol? Will biometrics software be integrated in the data platform? This is also where you set up alert systems in place, along with protection protocols for various threats like DDoS attacks. 

Data laws belong in this layer as well. As the data platform architect, you have to work with the organization's legal team to draft internal data policies. Ensure compliance with regulations like the US CLOUD Act or GDPA if your client or employer also operates in Europe. 

These are the guidelines for you to follow when building a data platform in MongoDB. If you have related knowledge or other ideas about similar topics, you may reach out to write for us about coding and help more developers in the process.