MACH Aligned for Retail: Cloud-Native SaaS

Silvio Sola and Genevieve Broadhead
September 22, 2022 | Updated: June 27, 2023
#Retail

MongoDB is an active member of the MACH Alliance, a non-profit cooperation of technology companies fostering the adoption of composable architecture principles promoting agility and innovation.

Each letter in the MACH acronym corresponds to a different concept that should be leveraged when modernizing heritage solutions and creating brand-new experiences. MACH stands for Microservices, API-first, Cloud-native SaaS, and Headless. In previous articles in this series, we explored the importance of Microservices and the API-first approach.

Here, we will focus on the third principle championed by the alliance: Cloud-native SaaS. Let’s dive in.

What is cloud-native SaaS?

Cloud-native SaaS solutions are vendor-managed applications developed in and for the cloud, and leveraging all the capabilities the cloud has to offer, such as fully managed hosting, built-in security, auto-scaling, cross-regional deployment, automatic updates, built-in analytics, and more.

Why is cloud-native SaaS important for retail?

Retailers are pressed to transform their digital offerings to meet rapidly shifting consumer needs and remain competitive. Traditionally, this means establishing areas of improvement for your systems and instructing your development teams to refactor components to introduce new capabilities (e.g., analytics engines for personalization or mobile app support) or to streamline architectures to make them easier to maintain (e.g., moving from monolith to microservices). These approaches can yield good results but require a substantial investment in time, budget, and internal technical knowledge to implement.

Now, retailers have an alternative tool at their disposal: Cloud-native SaaS applications. These solutions are readily available off-the-shelf and require minimal configuration and development effort. Adopting them as part of your technology stack can accelerate the transformation and time to market of new features, while not requiring specific in-house technical expertise.

Many cloud-native SaaS solutions focused on retail use cases are available (see Figure 1), including Vue Storefront, which provides a front-end presentation layer for ecommerce, and Amplience, which enables retailers to customize their digital experiences.

Collection of company logos of MACH Alliance members who provide retail solutions
Figure 1: Some MACH Alliance members providing retail solutions.

At the same time, in-house development should not be totally discarded, and you should aim to strike the right balance between the two options based on your objectives. Figure 2 shows pros and cons of the two approaches:

Pros and cons list for cloud-native SaaS and in-house approaches
Figure 2: Pros and cons of cloud-native SaaS and in-house approaches.

MongoDB is a great fit for cloud-native SaaS applications

MongoDB’s product suite is cloud-native by design and is a great fit if your organization is adopting this principle, whether you prefer to run your database on-premises, leveraging MongoDB Community and Enterprise Advanced, or as SaaS with MongoDB Atlas.

MongoDB Atlas, our developer data platform, is particularly suitable in this context. It supports the three major cloud providers (AWS, GCP, Azure) and leverages the cloud platforms’ features to achieve cloud-native principles and design:

  • Auto-deployment & auto-healing: DB clusters are provisioned, set up, and healed automatically, reducing operational and DBA efforts.

  • Automatically scalable: Built-in auto-scaling capabilities enable the database RAM, CPU, and storage to scale up or down depending on traffic and data volume. A MongoDB Serverless instance allows abstracting the infrastructure even further, by paying only for the resources you need.

  • Globally distributed: The global nature of the retail industry requires data to be efficiently distributed to ensure high availability and compliance with data privacy regulations, such as GDPR, while implementing strict privacy controls. MongoDB Atlas leverages the flexibility of the cloud with its replica set architecture and multi-cloud support, meaning that data can be easily distributed to meet complex requirements

  • Secure from the start: Network isolation, encryption, and granular auditing capabilities ensure data is only accessible to authorized individuals, thereby maintaining confidentiality.

  • Always up to date: Security patches and minor upgrades are performed automatically with no intervention required from your team. Major releases can be integrated effortlessly, without modifying the underlying OS or working with package files.

  • Monitorable and reliable: MongoDB Atlas distributes a set of utilities that provides real-time reporting of database activities to monitor and improve slow queries, visualize data traffic, and more. Backups are also fully managed, ensuring data integrity.

Independent Software Vendors (ISVs) increasingly rely on capabilities like these to build cloud-native SaaS applications addressing retail use cases. For example, Commercetools offers a fully managed ecommerce platform underpinned by MongoDB Atlas (see Figure 3). Their end-to-end solution provides retailers with the tools to transform their ecommerce capabilities in a matter of days, instead of building a solution in-house. Commercetools is also a MACH Alliance member, fully embracing composable architecture paradigms explored in this series.

Adopting Commercetools as your ecommerce platform of choice lets you automatically scale your ecommerce as traffic increases, and it integrates with many third-party systems, ranging from payment platforms to front-end solutions. Additionally, its headless nature and strong API layer allow your front-end to be adapted based on your brands, currencies, and geographies.

Commercetools runs on and natively ingests data from MongoDB. Leveraging MongoDB for your other home-grown applications means that you can standardize your data estate, while taking advantage of the many capabilities that the MongoDB data platform has to offer. The same principles can be applied to other SaaS solutions running on MongoDB.

Figure 3: MongoDB Atlas and Commercetools capabilities.

Find out more about the MongoDB partnership with Commercetools.

Learn how Commercetools enabled Audi to integrate its in-car commerce solution and adapt it to 26 countries.

MongoDB supports your home-grown applications

MongoDB offers a powerful developer data platform, providing the tools to leverage composable architecture patterns and build differentiating experiences in-house. The same benefits of MongoDB’s cloud-native architecture explored earlier are also applicable in this context and are leveraged by many retailers globally, such as Conrad Electronics, running their B2B ecommerce platform on MongoDB Atlas.

Summary

Cloud-native principles are an essential component of modern systems and applications. They support ISVs in developing powerful SaaS applications and can be leveraged to build proprietary systems in-house. In both scenarios, MongoDB is strongly positioned to deliver on the cloud-native capabilities that should be expected from a modern data platform.

Thank you to Ainhoa Múgica and Karolina Ruiz Rogelj for their contributions to this post.

Stay tuned for our final blog of this series on Headless and check out our previous blogs on Microservices and API-first.

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How a Data Mesh Facilitates Open Banking

Open banking shows signs of revolutionizing the financial world. In response to pressure from regulators, consumers, or both, banks around the world continue to adopt the central tenet of open banking: Make it easy for consumers to share their financial data with third-party service providers and allow those third parties to initiate transactions. To meet this challenge, banks need to transition from sole owners of financial data and the customer relationship to partners in a new, distributed network of services. Instead of competing with other established banks, they now compete with fintech startups and other non-bank entities for consumer attention and the supply of key services. Despite fundamental shifts in both the competition and the customer relationship, however, open banking offers a huge commercial opportunity, which we’ll look at more closely in this article. After all, banks still hold the most important currency in this changing landscape: trust. Balancing data protection with data sharing Established banks hold a special position in the financial system. Because they are long-standing, heavily regulated, and backed by government agencies that guarantee deposits (e.g., the FDIC in the United States), established banks are trusted by consumers over fintech startups when it comes to making their first forays into open banking. A study by Mastercard of 4,000 U.S. and Canadian consumers found that the majority (55% and 53%, respectively) strongly trusted banks with their financial data. Only 32% of U.S. respondents and 19% of Canadians felt the same way about fintech startups. This position of trust extends to the defensive and risk-averse stance of established banks when it comes to sharing customer data. Even when sharing data internally, these banks have strict, permission-based data access controls and risk-management practices. They also maintain extensive digital audit trails. Open banking challenges these traditional data access practices, however, causing banks to move to a model where end customers are empowered to share their sensitive financial data with a growing number of third parties. Some open banking standards, such as Europe’s Payment Services Directive (PSD2), specifically promote informed consent data sharing, further underlining the shift to consumers as the ultimate stewards of their data. At the same time, banks must comply with evolving global privacy laws, such as Europe’s General Data Protection Regulation (GDPR). These laws add another layer of risk and complexity to data sharing, granting consumers (or “data subjects” in GDPR terms) the right to explicit consent before data is shared, the right to withdraw that consent, data portability rights, and the right to erasure of that data — the famed “right to be forgotten.” In summary, banks are under pressure from regulators and consumers to make data more available, and customers now make the final decision about which third parties will receive that data. Banks are also responsible for managing: Different levels of consent for different types of data The ability to redact certain sensitive fields in a data file, while still sharing the file Compliance with data privacy laws, including "the right to be forgotten" The open opportunity for banks In spite of the competition and added risks for established banks, open banking greatly expands the global market of customers, opens up new business models and services, and creates new ways to grow customer relationships. In an open banking environment, banks can leverage best-of-breed services from third parties to bolster their core banking services and augment their online and mobile banking experiences. Established banks can also create their own branded or “white label” services, like payment platforms, and offer them as services for others to use within the open banking ecosystem. For customers, the ability of third parties to get access to a true 360-degree view of their banking and payment relationships creates new insights that banks would not have been able to generate with just their own data. Given the risks, and the huge potential rewards, how do banks satisfy the push and pull of data sharing and data protection? How do they systematically collect, organize, and publish the most relevant data from across the organization for third parties to consume? Banks need a flexible data architecture that enables the deliberate collection and sharing of customer data both internally and externally, coupled with fine-grained access, traceability, and data privacy controls down to the individual field level. At the same time, this new approach must also provide a speed of development and flexibility that limits the cost of compliance with these new regulations and evolving open banking standards. Rise of the data mesh Open banking requires a fundamental change in a bank’s data infrastructure and its relationship with data. The technology underlying the relational databases and mainframes in use at many established banks was first developed in the 1970s. Conceived long before the cloud computing era, these technologies were never intended to support the demands of open banking, nor the volume, variety, and velocity of data that banks must deal with today. Banks are overcoming these limitations and embracing open banking by remodeling their approach to data and by building a data mesh using a modern developer data platform. What is a data mesh? A data mesh is an architectural framework that helps banks decentralize their approach to sharing and governing data, while also enabling self-service consumption of that data. It achieves this by grouping a bank’s data into domains. Each domain in a data mesh contains related data from across the bank. For example, a "consumer" domain may contain data about accounts, addresses, and relationship managers from across every department of the bank. Each data domain is owned by a different internal stakeholder group or department within the bank, and these owners are responsible for collecting, cleansing, and distributing the data in their domain across the enterprise and to consumers. With open banking, domain owners are also responsible for sharing data to third parties. This decentralized, end-to-end approach to data ownership encourages departments within the bank to adopt a “product-like” mentality toward the data within their domain, ensuring that it is maintained and made available like any other service or product they deliver. For this reason, the term data-as-a-product is synonymous with data mesh. Data domain owners are also expected to: Create and maintain relevant reshaped copies of data, rather than pursue a single-source-of-truth or canonical model. Serve data by exposing data product APIs. This means doing the cleansing and curation of data as close as possible to the source, rather than moving data through complex data pipelines to multiple environments. The successful implementation of a data mesh, and the adoption of a data-as-a-product culture, requires a fundamental understanding of localized data. It also requires proper documentation, design, management, and, most important, flexibility, as in the ability to extend the internal data model. The flexibility of the document model is, therefore, critical for success. Conclusion Open banking holds great potential for the future of the customer experience, and will help established financial institutions meet the ever-evolving customer expectations. Facilitated by a data mesh, you can open new doors for responsible, efficient data sharing across your financial institution, and this increase in data transparency leads to better outcomes for your customers—and your bottom line. Want to learn more about the benefits of open banking? Watch the panel discussion Open Banking: Future-Proof Your Bank in a World of Changing Data and API Standards .

September 22, 2022
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MongoDB: Powering Digital Natives

Today's rapidly evolving digital landscape is dominated by digital native companies, driving innovation . These are companies born in the digital age and who operate through digital channels with a business model enabled by technology and data. They are not only adept at using technology but are also reshaping the way software is developed and deployed. This article delves into the challenges and opportunities facing digital natives in modern application development, with a particular focus on the complexities of managing data. We’ll explore how the right data platform can empower your digital native organization to build high-quality software faster, adapt to changing market demands, and unlock the full potential of your business. Strong foundations: The four pillars of tech-fueled growth for digital natives Achieving explosive growth requires a strong foundation built on specific principles, which empower rapid scaling and success. Here, we explore the four key pillars that fuel tech-driven growth for digital natives: Product-market fit, fast: As a digital native, you must continuously ship and iterate products to achieve a quick product-market fit. This builds customer trust and captures opportunities before competitors can in an evolving market. Data and AI-driven decisions: You must leverage data to personalize experiences, automate processes, and guide product decisions. A robust data architecture feeds real-time data into AI models, enabling data-driven decisions organization-wide. Balance of freedom and control: Your developers must have the freedom to choose technologies, even as your organization maintains control over the infrastructure to manage risks and costs at scale. Selected technologies must integrate within your overall technology estate. Extensible and open technologies: You must explore disruptive technologies while maintaining existing systems. Freedom from platform and vendor lock-in enables quick adoption of innovations, from current generative AI capabilities to future technological advances. Data: The unsolved challenge in modern application development From cloud platforms and managed services to gen AI code assistants, advancements have transformed how engineering teams build, ship, and run applications: Agile methods and programmatic APIs streamline development, while CI/CD and infrastructure as code automate processes. Containerization, microservices, and serverless architectures enable modularity, while new languages and frameworks boost capabilities. Enhanced logging and monitoring tools provide deep application health insights. Figure 1: Tools and processes to maximize velocity. But none of these advancements address where developers spend most of their time— data . In fact, 73% of developers share time and again that working with data is the hardest part of building an application or feature. So why is data the problem? Traditionally, selecting a database, often an open-source relational one, is the first step in development. However, these databases can struggle with the characteristics of modern data: it’s high volume, unstructured, and constantly evolving. As applications mature and their data demands grow, development teams may encounter challenges with achieving scalability and maintaining service resilience. Some teams turn to NoSQL databases, but even then they find there are limited capabilities, pushing them back to relational databases. As the application gains traction, the business’s appetite for innovation grows, compelling development teams to incorporate an expanding array of database technologies. This results in an architectural sprawl, imposing on teams the challenges of mastering, sustaining, and harmonizing new technologies. Concurrently, the dynamic technology landscape undergoes constant evolution, demanding teams to swiftly adjust. As a result, self-contained, autonomous teams encounter these hurdles recurrently, highlighting the pressing need for streamlined solutions to mitigate complexity and enhance agility. Figure 2: The evolving tech landscape. Data sprawl: A major threat to developer productivity and business agility Data sprawl is slowing everyone down. The more systems we add, the harder it is for developers to keep up. Each new database brings its own unique language, format, and way of working. This creates a huge headache for managing everything—from buying new systems to making sure they all work together securely. It’s a constant battle to keep data accessible, consistent, and backed up across all these different platforms. Figure 3: Teams building on separate stacks leads to data sprawl and manageability issues across the organization It compromises every single one of the four outcomes your technology foundation should be providing, yielding the opposite results: Missed opportunities, lost customers: Fragmented development experiences consume time as engineers struggle with multiple technologies, frameworks, and extract, transform, and load mechanisms for duplicating data between systems. This slows down releases, degrades digital product quality, and impedes engineers from achieving product-market fit and effective competition. Flying blind: With your operational data siloed across multiple systems, you lack the data foundations necessary to use live data in shaping customer experiences or reacting to market changes. This is because you are unable to feed reliable, consistent, real-time data into your AI models to take action within the flow of the application or to provide the business with up-to-the-second visibility into operations. High attrition, high costs: Complex data architecture impacts development team culture, leading to siloed knowledge, inefficient collaboration, and decreased developer satisfaction. This complexity also consumes substantial resources in maintaining existing systems by diverting resources from new projects that are vital for business competition in new markets. Disruption from new technologies: Dependence on any one cloud provider can stifle innovation for development teams by restricting access to the latest technologies. Developers are confined to the tools and services offered by a single provider, hindering their ability to explore and integrate new, potentially more efficient, or advanced technologies. Speed: A unified developer experience for building high-quality software faster In today’s digital world, speed is king. Your customers expect seamless experiences, but clunky applications leave them frustrated. But traditional databases can be a bottleneck, struggling to keep pace with your ever-evolving data and slowing down development. The future of data is here, and it’s flexible: a data platform built for digital natives . It leverages a flexible document model, letting you store and work with your data exactly how you need it. This eliminates rigid structures and complex migrations, freeing your developers to focus on what matters—building amazing applications faster. Flexible document data models empower developers to handle today’s rapidly evolving application data ( 80%+ unstructured) that relational databases struggle with. MongoDB documents are richly typed, boosting developer productivity by eliminating the need for lengthy schema migrations when implementing new features. Developers get to use their preferred tools and languages. Through its drivers and integrations, MongoDB supports all of the most popular programming languages, frameworks, integrated development environments, and AI-code assistance tools. MongoDB scales! It starts small and scales globally. Built for elasticity and horizontal scaling, it handles massive workloads without app changes. Figure 4: A unified developer experience, integrating all necessary data services for building sophisticated modern applications Introducing MongoDB Atlas : a fully-managed cloud database built for the modern developer. It enables the integration of real-time data from devices with AI capabilities (through vector embeddings and large language models ) to personalize user experiences. Stream processing empowers constant data analysis, while in-app analytics provides real-time insights without needing separate data warehouses, all while automatically managing data movement and storage for cost-effectiveness. MongoDB Atlas simplifies database management with the following: Easy deployment via UI, API, CLI, Kubernetes, and infrastructure as code tools. Automated operations for cost-effective performance and real-time monitoring. MongoDB Atlas customer success stories: Development with speed, scale, and efficiency Delivery Hero Delivery Hero, a global leader in online food delivery, leverages MongoDB Atlas to power its rapid service. Founded in 2011, Delivery Hero now serves millions of customers in over 70 countries through brands like PedidosYa, foodpanda, and Glovo. Having replaced its legacy SQL database, Delivery Hero optimized operations and bolstered performance by using MongoDB Atlas. By leveraging MongoDB Atlas Search, Delivery Hero revolutionized its search functionality, ensuring a seamless user experience for its extensive customer base through simplified indexing and real-time data accuracy. MongoDB’s scalability has empowered Delivery Hero to manage over 100 million products in its catalog without encountering latency issues, enabling the company to expand its services while maintaining peak performance. This agility, coupled with MongoDB’s cost-effectiveness, has enabled Delivery Hero to swiftly adapt to evolving customer demands, solidifying its position in the fiercely competitive delivery market. MongoDB Atlas Search was a game changer. We ran a proof of concept and discovered how easy it is to use. We can index in one click, and because it’s a feature of MongoDB, we know data is always up-to-date and accurate. Andrii Hrachov, Principal Software Engineer, Delivery Hero Read the full customer story to learn more. Coinbase Coinbase, a prominent cryptocurrency exchange boasting 245,000 ecosystem partners and managing assets worth $273 billion , trusts MongoDB to handle its extensive data workload. As the company grew, MongoDB scaled seamlessly to accommodate the increased demand. To further improve performance in the fast-paced crypto world, Coinbase partnered with MongoDB to develop a system that significantly accelerated data transfer to reporting tools, reducing processing time from days to a mere 5-6 hours. This near real-time data access enables Coinbase to rapidly analyze trends and make informed decisions, maintaining a competitive edge in the ever-evolving crypto landscape. Watch Coinbase's full session at MongoDB.local Austin, 2024 to learn more. MongoDB: Your flexible platform for digital growth With MongoDB, you can freely explore, experiment, develop, and deploy according to your digital-native business needs. If you would like to learn more about how MongoDB can empower your digital-native business to conquer market trends, visit: Innovate With AI: The Future Enterprise Application-Driven Intelligence: Defining the Next Wave of Modern Apps AI-Driven Real-Time Pricing with MongoDB and Vertex AI

November 7, 2024