Data as a Service
Challenges
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.
Lack of Agility
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
Data Locked in Silos
Poor Data Accessibility
Limited Data Support
Cloud Blockers
High Cost
Expensive hardware, huge jumps in costs as workloads scale, and punitive licensing impose barriers to innovation.
Solution
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.
How?
Successfully building an ODL and delivering Data as a Service requires a combination of people, process, and technology. Here’s how MongoDB can help:
Data Layer Realization
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.
Why the MongoDB Intelligent Operational Data Platform?
When you choose MongoDB as the foundation for DaaS, you’re investing in the best technology for your system of innovation.
MongoDB is the best way to work with data
Ease
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
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Speed
Unifying data in rich MongoDB documents means your developers write less code and your users get better performance when accessing data
Flexibility
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
Versatility
Process data in any way your applications require, from simple queries to complex aggregations, analytics, faceted search, geospatial processing, and graph traversals
MongoDB lets you intelligently put data where you need it
Availability
Built-in redundancy and self-healing recovery ensure resilience of your modernized apps, without expensive and complex clustering add-ons
Scalability
Ditch expensive scale-up systems and custom engineering. MongoDB automatically scales out your database to meet growing data volumes and user loads
Workload Isolation
Run operational apps while also serving analytics, machine learning, and BI to unlock critical insights in real time – all on a single data platform
Data Locality
MongoDB gives you the freedom to run anywhere
Portability
Global Coverage
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
No Lock-In
Get the benefits of a multi-cloud strategy and avoid vendor lock-in – or if you want, run MongoDB yourself on-prem
MongoDB enables data access and APIs
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 Realm 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.
Benefits
Reduce Risk
Achieve always-on availability to eliminate downtime (and any associated penalties)
Avoid exposing source systems directly to new consuming applications
Implement a system of innovation without the danger of a full “rip and replace” of legacy systems
Improve Innovation
Build new applications and digital experiences that weren’t possible before
Make full use of your data to build unique differentiators vs. the competition
Improve customer experience
Move Faster
Develop new applications 3-5x faster
Iterate quickly on existing services, adding new features that would have been impossible with legacy systems
Deliver insights that improve your competitiveness and efficiency
Lower Costs
Reduce capacity on source systems, cutting costs for licensing, MIPS, and expensive hardware
Leverage cloud and/or commodity infrastructure for workloads
In the long term, decommission legacy systems
Use Cases
Single View
DaaS is perfectly suited to generating a Single View of your business. When you unify your enterprise data and make it available as Data as a Service, the next step is to build an application to expose a single view of that data to those who need it. Better real-time visibility across the business, improved customer service, and insight for more intelligent cross-sell and up-sell opportunities are all within reach.
Mainframe Offload
Analytics
Providing Data as a Service doesn’t just support operational applications. It can also power the the analytics that make sense of your data – faster than a traditional data warehouse. Whether you’re analyzing your unified enterprise data set for business insights, running real-time analytics to take action based on algorithms, or reviewing usage patterns to inform application roadmaps, an Operational Data Layer can serve analytical needs with the appropriate workload isolation to ensure that there is no performance impact on production workloads.
And More
Building a mobile application to reach your customers any place, any time? Putting machine learning to work on your enterprise data? Building recommendation engines, adding social components to your UI, or personalizing content in real time? These applications, and any others you need to build, benefit from being able to access Data as a Service. What innovation could you power with all of your enterprise data easily and securely available in one place?
Featured Resources