Supercharging Edge-to-Cloud Strategy

Pablo Jimenez

The emergence of Big Data and the proliferation of AI/ML, is today more than ever, pushing enterprises' digital strategies to adopt more sophisticated systems that help them become data-driven organizations. This said the constant dependency on legacy systems makes it difficult for many enterprises to even access their edge data and make use of it in time to make operational/business decisions.

From healthcare to retail, manufacturing to telecom, companies that can successfully adopt IoT into their operations have proven to grow significantly faster than laggards within their respective industries. Modern IoT solutions enable businesses to capture and visualize edge data in real time, resulting in rich insights into their operations. By bringing computing to the edge, they can also deploy a wide variety of applications that help them take action on critical data right then and there, delivering significant efficiency into operations.

The emergence of generative AI tools is the disruptive force that has revolutionized business operations from a strategic and operational level. It has supercharged corporate data strategies by taking on the heavy lifting of processing/analysis and automating business activities by triggering reactions.

This streamlining of operations has not only increased productivity but has also enabled faster and more efficient decision-making. By relieving technical and analytics teams of arduous tasks, these tools free up resources to focus on important creative aspects of business, unlocking meaningful business value with relatively low effort.

Data from the edge plays a key role in improving a company's AI/ML strategies as it helps enrich their corporate models and improve associated outcomes. For this reason, it is imperative that enterprises modernize their edge-to-cloud stack with solutions that can be easily implemented and adaptable to their growing data needs.

Check out our AI resource page to learn more about building AI-powered apps with MongoDB.

Modernizing applications with MongoDB

Successful modernization requires the right service provider with the expertise and right tools that can adapt to an organization's unique needs and business goals. MongoDB Atlas, AppServices, and Device Sync provide the infrastructure needed for enterprises to implement these modern solutions and start reaping their benefits. Below is a reference architecture for IoT solutions developed by WeKan that can be implemented across industries.

Highlights of the architecture

  • The solution is cloud-agnostic and compatible with services from any of the cloud providers (AWS, GCP, AZURE)

  • Atlas Device SDK’s Data Ingest provides performant behavior for heavy client-side insert-only workloads of structured & unstructured data that is then streamed to Atlas with automatic clean-up

  • Out-of-the-box synchronization allows seamless and secure transport of data from the device to the cloud using Atlas Device Sync

  • Built-in conflict resolution with document and field-level permissions offers reliable bi-directional sync capabilities and ensures data consistency at all times

  • Atlas Device SDK offers computing at the edge, allowing businesses to take action on field/telemetry data without the need for connectivity to the cloud

  • MongoDB’s native time series collections, with hands-free schema optimization, support high-efficiency storage and low-latency querying

  • Change streams allow applications to access real time data changes in the database without any complexity or risk, allowing IoT applications to subscribe to all data changes in real-time and action on them as needed

  • MongoDB’s Schema Flexibility delivers agility to the business as engineers can seamlessly make changes and additions to the schema without downtime

  • MongoDB Atlas, combined with industry-leading data warehousing solutions, enables businesses with first-in-class and real-time business intelligence capabilities

MongoDB and WeKan

Together, MongoDB and WeKan offer a powerhouse solution that combines the technical capabilities and the right expertise. Their solution streamlines the adoption process, making it easier and safer for customers to modernize their edge-to-cloud stack.

  • Providing the right expertise and support with the Jumpstart Program: From architecture design to migration strategy definition and implementation support. A mixed team of specialists from MongoDB and WeKan works hand in hand with customers to ensure quick and correct implementation of the MongoDB technology.

  • Offering the right tooling to accelerate time to market: WeKan's Migration Acceleration suite offers full DataBase & Application code analysis to best prepare for the migration and MongoDB's relational Migrator helps accelerate the transformation and transport of data from RDBMS to MongoDB. Together, these tools help reduce overall migration efforts/costs by 70%.

  • A highly specialized service at the best price-point: WeKan’s Global Delivery Model offers architecture design and implementation support at a competitive price-point, making it easier for enterprises to access the expertise needed to migrate away from legacy and safely implement modern solutions around MongoDB

Here are a couple of examples of how these IOT solutions can be applied across multiple industries:

Automotive Industry: Predictive maintenance with MongoDB & GCP

Leveraging Atlas Device SDK and MongoDB Atlas helps automakers deploy applications that use real-time data to proactively detect failures and efficiently schedule maintenance events.

  • Vehicle telemetry data is stored in Atlas Device SDK (onboard) and synced using Atlas Device sync to MongoDB Atlas

  • Smart factory telemetry data about their production lines are synced via MQTT to GCP Cloud Iot core and pub-sub back to MongoDB Atlas

  • Data is transferred from MongoDB Atlas to a data warehouse/data lake house, such as Bigquery or Databricks, for analysis

  • Predictive maintenance ML Models are executed on the data from the data warehouse to infer the assets that require maintenance in the near term

  • These are then processed and stored back in MongoDB Atlas as tickets for further action

  • These tickets are then assigned to users and synced to their mobile application using Atlas Device Sync

Manufacturing - Industry 4.0

Leveraging Atlas Device SDK at the Edge and MongoDB Atlas, Manufacturers can seamlessly transport their factory data to the cloud, gain business insights, and action on it as needed.

  • Sensors from the production lines in the manufacturing plant transmit telemetry data over MQTT to the local Atlas Device SDK Gateway about the customer orders being built

  • The Atlas Device SDK Gateway sends the data in real-time via Atlas Device Sync back to MongoDB Atlas.

  • With centralized information, customers, factory managers, and warehouse operators can all see real-time data about orders, inventory, and manufacturing timelines.


As enterprises grapple with the complexities of data management, real-time synchronization, scalability, and edge computing, the MongoDB and WeKan partnership offers powerful solutions to tackle these challenges head-on. Together, they help customers move away from legacy systems and implement complete edge-to-cloud solutions that harness the full potential of IoT for better data access, improved insights, and, ultimately, enhanced business outcomes.