Powering your Modernization Journey Using MongoDB Suite of Products.

The explosion of data generated from mobile apps and machines at the edge requires a data management solution that can manage those systems as well as capture and sync data securely. However, solutions that rely on connected architectures (such as RESTful Web Services) do not meet the availability, performance, and security needs of today’s applications. 

MongoDB Realm provides a full-featured solution enabling seamless sync from the edge to the cloud, and guaranteed data availability, irrespective of network connectivity.

Powering your Modernization
mangodb

Powering your Enterprise Mobility Solutions with MongoDB Realm & Atlas Data Lake

Enterprises are laying more focus on mobility, either from an employee perspective or from an organizational perspective. This entails integrating mobility into business operations to serve specific business processes or to drastically redefine operational paradigms.

From this standpoint, organizations are getting into the task of developing viable mobile strategies. One of the key aspects of successful implementation of an enterprise mobile strategy is to get into the basics and explore the right mobility solutions that perfectly align with your organizational goals.

Tasked with a growing mobile need within Enterprises, engineering teams face a multitude of challenges when they build mobile applications and without the right solution to solve these, their mobile strategies fall apart.

What problems do engineering teams face today in building out mobile applications?
  • Unpredictable environment of mobile apps in which network connections can be lost, devices can shut down any time due to battery or system resources being overutilized.
  • Coordinating between multiple backend API’s and local databases to maintain a common schema of objects with which to work with.
  • Building offline first capabilities
  • Reacting to data changes between backend systems and mobile interfaces leads to the implementation of extra layers of code.
  • Maintaining secure app layers, with data at rest, in transit and being stored on the backend cloud.
  • Consistently serializing objects between networks, database storage and application memory.
  • Simplified cross platform support across iOS and Android platforms with developer friendly SDK’s to work with.
How does MongoDB Realm help solve your challenges?
  • Cross Platform - Use a single database to build all your apps - iOS / Android.
  • Offline First - Designed for 100% offline use, allows you to build as good an experience as online apps.
  • Object Oriented - Code written with Realm is laconic in the sense, it can save you thousands of lines of code.
  • Reactive Architecture - Realm’s unique live objects ensure that data updated anywhere is automatically updated everywhere.
  • Simplified Data Sync - Automatically sync data from Realm Mobile Database to MongoDB Atlas. The sync protocol resolves conflicts consistently on each client and in the linked MongoDB Atlas cluster.
  • Performance - Fast querying due to a zero copy architecture and due to its internal MVCC architecture & Safe threading - Read and Writes can happen in parallel without slowing down your experience.
  • Optimized for Mobile - speed, battery life and data usage are used efficiently to create a fast and optimal mobile experience.
  • Secure - Fully secure your data in flight or at rest with transparent encryption and decryption.

Powering your Internet of Things(IoT) Solutions with MongoDB Realm & Atlas Data Lake

Today the IoT is enabling Enterprises to blend the physical and digital worlds. Realizing the business value of connecting all of these "things" enables creation of new revenue models, improves productivity, and generates new insights that drive operational efficiencies. 

What challenges do engineering teams face today in building out IoT applications?
  • Volume and Velocity - Ability to handle data at a very large volume and fast arriving sources of data from the edge all the way to the cloud.
  • Real-time processing - IoT data is massive in size and arriving at a high ingestion rate. IoT solutions that require the data to be moved (via ETL or similar processes) to a different system for reporting or analytics take up critical time and resources that may be unnecessary, while producing a more complex overall architecture. 
  • Schema Rigidity - SQL, the language used in relational database querying, isn’t a great fit for working with time series data. The fixed schema requirement does not allow for the flexibility and agility that modern systems require. For example, adding a new sensor type to an existing application may require table schema changes that result in application downtime or complex multi-resource coordination.
  • Inflexible Data models - the ability to ingest new data models and the fact there’s a lot of unstructured data, leads to a lot of frequent engineering overhead.
  • Inability to scale as data and processing needs evolve.
How does MongoDB Realm and MongoDB Atlas help solve your challenges?
  • Offline first approach model works very well to be able to aggregate information in a complete lockdown and sync back as needed even when there is low network bandwidth.
  • Simplified Data Sync - Automatically sync data from Realm Mobile Database to MongoDB Atlas without worrying about conflicts and data consistency.
  • Well suited for frequent or irregular batch writes for ingress of data.
  • Support for flexible and dynamic data models which allows for consumption of different data formats from sensors.
  • Low footprint that can run on suitable terminals or even a Raspberry Pi within the IoT network
  • Allows for transformation and processing of data within the OT network on the edge device.
  • With a central data layer powered by Atlas MongoDB, being able to store data and process IoT data of any structure: events, time series data, geospatial coordinates, text, and binary data such as images from connected devices.
  • Multi-Cloud support for Atlas MongoDB
  • Built for Optimal performance with on-demand scaling, resource optimization tools and real-time insights on your database performance.
  • Fully automated infrastructure provisioning, Database setup, maintenance and version upgrades.
  • Sophisticated security controls to satisfy new and existing data privacy and compliance measures.
  • Support for out of the box services that speed up development efforts such as GraphQL API’s, functions and triggers.

Powering your Data Ecosystem MongoDB Atlas Data lake

With the advent of big data technologies, many organizations are adopting a new information storage model called data lake to solve data management challenges. The data lake model is being adopted as a single data layer for relational and non-relational data originating from cloud native apps, mobile apps, IoT device for diverse use cases such as business intelligence, analytics and regulatory compliance.

This helps to identify, and act upon opportunities for business growth faster by attracting and retaining customers, boosting productivity, proactively maintaining devices, and making informed decisions.

What challenges do engineering teams face today in building out data ecosystems?
  • Data Movement - to import any amount of data that can come in real-time
  • Existing data warehouses are optimized to analyze relational data coming from transactional systems and line of business applications and unable to accommodate non-relational unstructured data coming from sources such as mobile applications and IoT devices. 
  • Fragmented data silos make it harder to derive insights using machine learning and predictive analytics.
  • With such fragmentation prevalent, compute and storage costs are increasing constantly.
  • Ability to secure and catalog all of the data.
How does MongoDB Atlas and Atlas Data Lake help solve your challenges?
  • With a central data layer powered by Atlas MongoDB and Atlas Data Lake, being able to store data and process relational and non-relational data of any structure: events, time series data, geospatial coordinates, text, and binary data such as images from connected devices.
  • Tier your data across fully managed databases and cloud object storage with Atlas Online Archive to support real time and historical data access.
  • Reduction in compute and storage costs leveraging Atlas Data Lake and Online Archive.
  • Analyze rich data easily and intuitively using powerful, easy to understand aggregations with MQL for consistent experience across data types.
  • Work with data at any scale as Atlas Data Lake is serverless without any worries of predicting capacity
  • Multi-Cloud support for Atlas MongoDB
  • Built for Optimal performance with on-demand scaling, resource optimization tools and real-time insights on your database performance.
  • Fully automated infrastructure provisioning, Database setup, maintenance and version upgrades.
  • Sophisticated security controls to satisfy new and existing data privacy and compliance measures.