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Data as a Service

Your company’s data should be its greatest asset. It ought to be easy to develop new applications based on your data and to generate essential business insights—but for too many, legacy systems and databases make this difficult or impossible.

What is data as a service (DaaS)?

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.

Challenges of Legacy Systems vs DaaS

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 and rigid relational data models, waterfall development, and wariness of altering existing systems.

Data Locked in Silos

No complete view of your data? That means poor customer experience, missing insights, and slower app development.

Poor Data Accessibility

Existing systems aren’t built for the modern access patterns of 24/7 customer experiences on web, mobile, and social—and they’re single points of failure.

Limited Data Support

New classes of web, mobile, social, IoT, and AI applications produce data in a volume and variety that legacy systems just can’t handle.

Cloud Blockers

Brittle legacy systems prevent the shift to cloud computing, holding developers back from on-demand access to elastically scalable compute and storage infrastructure.

High Cost

Expensive hardware, huge jumps in costs as workloads scale, and punitive licensing impose barriers to innovation.

Solution: Get started with DaaS

Deliver Data as a Service within your organization to speed up development, integrate data, and improve accessibility and performance.

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.

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 to build a successful DaaS?

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 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.

TECHNOLOGY

Why use MongoDB Atlas as a data service?

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.

Learn more about MongoDB Atlas

MongoDB Atlas 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.

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.

Using multi-cloud and multi-region Atlas features, place the data where you need it.

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 Atlas 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

Distribute your data globally via Atlas multi-cloud and multi-region features to serve worldwide audiences and meet new regulatory compliance mandates.

MongoDB gives you the freedom to run anywhere.

Portability

MongoDB runs the same everywhere—commodity hardware on-premises, on the mainframe, in the cloud, or as an on-demand, fully managed Database as a Service.

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 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.

Data as a Service 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

  • 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

Use cases of DaaS

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

Mainframes and other legacy systems aren’t suited for modern applications. Rigidity, downtime requirements, and high costs mean that you’re held back from innovating for the business. By implementing an Operational Data Layer in front of your legacy systems, you can build new apps faster, deliver great performance with high availability, meet new regulatory demands, and make it drastically easier to serve mainframe data to new digital channels – all while reducing MIPS and hardware upgrade costs.

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?

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.

Mainframes and other legacy systems aren’t suited for modern applications. Rigidity, downtime requirements, and high costs mean that you’re held back from innovating for the business. By implementing an Operational Data Layer in front of your legacy systems, you can build new apps faster, deliver great performance with high availability, meet new regulatory demands, and make it drastically easier to serve mainframe data to new digital channels – all while reducing MIPS and hardware upgrade costs.

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.

Successful Data as a Service adoption

Get in touch to learn more about how to implement Data as a Service at your organization, review reference architectures, and more!