The keys to success in the digital age are how quickly you can build innovative applications, scale them, and gain insights from the data they generate – but legacy systems hold you back.
Demands for faster time to market and higher productivity are held back by traditional rigid relational data models, waterfall development, and wariness of altering existing systems
Expensive hardware, huge jumps in costs as workloads scale, and punitive licensing impose barriers to innovation.
Deliver Data as a Service within your organization to speed development, integrate data, and improve accessibility and performance.
An ODL makes your enterprise data available as a service on demand, simplifying the process of building transformational new applications. It can reduce load on source systems, improve availability, unify data from multiple systems into a single real-time platform, serve as a foundation for re-architecting a monolith into microservices, and more. An Operational Data Layer becomes a system of innovation, allowing an evolutionary approach to legacy modernization.
Successfully building an ODL and delivering Data as a Service requires a combination of people, process, and technology. Here’s how MongoDB can help:
MongoDB has developed a tried and tested approach to constructing an Operational Data Layer. The Data Layer Realization methodology helps you unlock the value of data stored in silos and legacy systems, driving rapid, iterative integration of data sources for new and consuming applications. Data Layer Realization offers the expert skills of MongoDB’s consulting engineers, but also helps develop your own in-house capabilities, building deep technical expertise and best practices.
This process for constructing an Operational Data Layer has been successfully implemented with many customers. Starting with clear definitions of project scope and identifying required producing and consuming systems is the first step to ensure success. Based on these findings, we assign data stewards for clear chains of responsibility, then begin the process of developing and deploying the Operational Data Layer with loading and merging, data access API creation, validation, and optimization. This process is iterative, repeating in order to add new access patterns and consuming apps or enrich the ODL with new data sources.
A successfully implemented ODL is a springboard for agile implementation of new business requirements. MongoDB can help drive continued innovation through a structured program that facilitates prototyping and development of new features and applications.
When you choose MongoDB as the foundation for DaaS, you’re investing in the best technology for your system of innovation.
MongoDB’s document data model is much more natural to developers than the relational tabular model, and you maintain the same ACID data integrity guarantees you are used to
Unifying data in rich MongoDB documents means your developers write less code and your users get better performance when accessing data
A flexible data model is essential to accommodate agile development and continuous delivery of new features: adapt your schema as your apps evolve, without disruption
Process data in any way your applications require, from simple queries to complex aggregations, analytics, faceted search, geospatial processing, and graph traversals
Built-in redundancy and self-healing recovery ensure resilience of your modernized apps, without expensive and complex clustering add-ons
Ditch expensive scale-up systems and custom engineering. MongoDB automatically scales out your database to meet growing data volumes and user loads
Run operational apps while also serving analytics, machine learning, and BI to unlock critical insights in real time – all on a single data platform
Deploy a MongoDB cluster across the globe – or turn to MongoDB Atlas, our Database as a Service, for coverage in 50+ regions of all the major cloud providers
Get the benefits of a multi-cloud strategy and avoid vendor lock-in – or if you want, run MongoDB yourself on-prem
Consuming systems require powerful and secure access methods to the data in the ODL. MongoDB’s drivers provide access to a MongoDB-based ODL from the language of your choice. Data as a Service reaches its fullest potential when you present a common Data Access API for applications; this layer can be custom built, or MongoDB Stitch can be used to expose access methods with a built-in rules engine for fine-grained security policies.
Data as a Service should also be available for analytics. The Connector for Business Intelligence allows analysts to connect to a MongoDB ODL with their BI and visualization tools of choice, or MongoDB Charts can connect directly to the ODL for native visualization. The Connector for Apache Spark exposes MongoDB data for use by all of Spark’s libraries, enabling advanced analytics such as machine learning processes.