Data as a Service, DaaS, is considered one of the emerging cloud types. DaaS is hosted in the cloud and provides its data services as Software as a Service to the consumers. Consuming DaaS is a strategic investment for consolidating and organizing your enterprise data in one place, then making it available to serve new and existing digital initiatives.
MongoDB Atlas, the Data as a Service platform, is the only cross cloud, cross region platform built on top of MongoDB’s famous database software. Over 25k customers use this platform to innovate their application, leaving all the tedious work of managing the database to MongoDB.
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.
Expensive hardware, huge jumps in costs as workloads scale, and punitive licensing impose barriers to innovation.
The path to Data as a Service is to implement an Operational Data Layer (ODL). This data layer sits in front of legacy systems, enabling you to meet challenges that the existing architecture can’t handle—without the difficulty and risk of a full rip and replace. Depending on your requirements, an ODL can draw data from one or many source systems and power one or many consuming applications. An ODL can be used to serve only reads, accept writes that are then written back to source systems, or evolve into a system of record and eventually replace legacy systems entirely and simplify the enterprise architecture.
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 ensuring 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 enriching 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 should definitely use MongoDB Atlas, the Data as a Service platform. This will allow you to boost your productivity and let MongoDB experts take care of all the heavy lifting for you.
Unifying data in rich MongoDB documents means your developers write less code and your users get better performance when accessing data.
Process data in any way your applications require, from simple queries to complex aggregations, analytics, faceted search, geospatial processing, and graph traversals.
Using multi-cloud and multi-region Atlas features, place the data where you need it.
Built-in redundancy and self-healing recovery ensure resilience of your modernized apps, without expensive and complex clustering add-ons.
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 API for applications; this layer can be custom built, using Atlas Data API, 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. Finally, multiple data sources can be consumed via the Atlas Data Lake. Atlas data lets you directly query Atlas databases and AWS S3 together using a single API. Furthermore, data lakes can be linked to Charts or MongoDB Realm Services.
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
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
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
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
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.
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.
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?
The future of Data as a Service
Companies understand that data and data services are the heart of the organization and key to its success. Together with the cloud-first adoption, Data as a Service has a bright future. Building and managing data systems in a legacy fashion adds a tremendous overhead to any organization’s IT efforts. Unlocking the innovation of your products with a platform like MongoDB Atlas, leaving behind all the heavy lifting of managing, upgrading and maintaining those stack components, allows companies to deliver faster and better.
Data as a Service will surely evolve in the next few years to open the potential of the digital worlds that surround us and will continue to improve the user experience and help you surpass your customers' wildest expectations.