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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Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana

For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies, the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.

November 21, 2024