MACH Aligned for Retail: API-First

Silvio Sola
June 24, 2022 | Updated: June 28, 2022
#Retail

Retailers must constantly evolve to meet growing customer expectations and remain competitive. Both their internal- and external-facing applications must be developed using principles that promote agility and innovation, moving away from siloed architectures.

As discussed in the first article of this series, the MACH Alliance promotes the development of modern applications through open tech ecosystems. MACH is an acronym that represents Microservices, API-first, Cloud-native SaaS, and Headless. MongoDB is a proud member of the Alliance, providing retailers with the tools to build highly flexible and scalable applications.

This is the second in a series of blog posts focused on MACH and how retail organizations can leverage this framework to gain a competitive advantage. In this article, we’ll discuss concepts relating to the second letter of MACH: API-first.

Read the first post in this series, "MACH Aligned for Retail: Microservices."

What is an API-first approach and why is it important?

An application programming interface (API) is a set of routines, protocols, and tools that allow applications, or services within a microservices architecture, to talk to each other. APIs can be seen as messengers that deliver requests and responses.

Applications built around APIs are said to be API-first. With this approach, the design and development of APIs come before the software implementation. Typically, an interface is created that is used to host and develop the API. The development team will then leverage the interface to build the rest of the application.

This methodology enables developers to have access to specific functionalities of external applications or other microservices within the same application, depending on their needs. It promotes reusability because functionalities are interoperable with mobile and other client applications.

In addition, applications developed with an API layer in mind can adapt to new requirements more easily because additional services and automation can be integrated into production when new requirements arise, therefore remaining competitive for longer.

*An API-first approach to developing applications*

The role of API-first in retail

APIs play a crucial role in deeply interconnected systems that need to interface with other internal applications, third-party partners, and customers — all key areas when it comes to developing powerful retail applications. Think about how an e-commerce platform connects to the different systems making up the purchase process, such as inventory management, checkout, payment processing, shipping, and loyalty programs.

The use of APIs is deeply interlinked with the concept of microservices. Software and data need to be decoupled to enable retailers to meet ever-increasing requirements, including omnichannel and cross-platform integration, seamless experiences across physical and online stores, and the ability to leverage real-time capabilities that enable differentiating features, such as live inventory updates and real-time analytics. APIs can be seen as a bridge for loosely coupled microservices to communicate with each other.

Besides enabling a microservices architecture, an API-first approach offers the following additional benefits:

Avoid duplication of efforts and accelerate time to market. Developers can work on multiple frontends at the same time, being confident that functionalities can be integrated by embedding the same APIs once ready. Think of multiple development teams working on an e-commerce web application, mobile portal, and internal inventory management system all at the same time. An API enabling the placement of a new order can be seamlessly leveraged by the web and mobile application and fed into the inventory management system to aid warehouse workers. Bug-fixing and feature enhancements can happen simultaneously, avoiding duplication of efforts and allowing new capabilities to be released to market more quickly.
Reduce risks and operating costs. An API-first approach enables system stability and interoperability from the beginning because API efficiency is placed at the center of the development lifecycle and is no longer an afterthought once the application or functionality has been developed. This approach reduces the risk for retailers and saves money and effort in troubleshooting unstable systems.
Enable new opportunities and scale faster. A flexible approach revolving around APIs provides more opportunities when it comes to integrating and refactoring the way different client applications and microservices communicate with each other, allowing retailers to improve and scale their IT offering in a fraction of the time. This approach also changes the way retailers can interact with external partners and do business with them since they can be provided with the tools to easily integrate with the retailer’s offering.
Achieve language flexibility. Effective retailers need to have the capability to adapt their digital offering to different regions and languages. The plug-in capabilities of API-first allow developers to offer language-agnostic solutions that different microservices can integrate with, leveraging region-specific frontends.

What is the alternative?

The four MACH Alliance principles combined (Microservices, API-first, Cloud-native SaaS, Headless) act as a disrupting force compared to the way applications were built until recently. Adapting to a new technology paradigm requires effort and a different developer mindset. But what was there before?

From an API-first perspective, it can be said that the opposite is code-first. With this approach, application development starts in the integrated development environment (IDE), in which code is written and the software takes shape. Development teams know that they will need to build an interface to be able to interact with each function of the code, but it is seldom a priority; developing core functionalities takes precedence over the interface where those functionalities will be hosted and accessed.

When the time comes for the interface to be developed, the code has already been defined. This means the API is developed around existing code rather than vice versa, which poses limitations. For example, developers might not be able to return data the way they want because of the underlying data schema.

Bottlenecks can also occur as other teams requiring the API will need to wait until the code is finalized to be able to embed it in their underlying applications. Any delays in the software development lifecycle will hold them up and delay progress. Although a code-first approach might have worked in the past, it is no longer suitable for dealing with highly interconnected applications.

Learn more about how MongoDB and MACH are changing the game for ecommerce.

How MongoDB helps achieve an API-first approach

Simply lifting and shifting monolithic applications to a microservice and API-first architecture will only provide minimal benefits if they are still supported by a relational data layer. This is where most of the bottlenecks occur. Changes to application functionalities will require constant refactoring of the database schemas, object-relational mapping (ORM), and refining at the microservice level.

Moving to a modern MACH architecture requires a modern data platform that removes data silos.

The MongoDB developer data platform provides a flexible data model, along with automation and scalability features to adapt to even the most challenging retail use cases and to multiple platforms (e.g., on-premises, cloud, mobile, and web applications).

MongoDB Atlas, MongoDB’s fully managed cloud database, also provides capabilities to manage the data layer end to end via APIs, such as the MongoDB Atlas Data API. This is a REST-like, resilient API for accessing all Atlas data that enables CRUD operations and aggregations with instantly generated endpoints. This is a perfect answer to an API-first approach, since developers can access their data using the same principles leveraged to connect to other applications and services.

MongoDB’s Atlas Data API provides several other benefits, allowing developers to:

Build faster with developer-friendly data access. Developers work with a familiar, REST-like query and response format, no client-side drivers are necessary.
Scale confidently with a resilient, fully managed API that reduces the operational complexity needed to start reading and writing your data.
Integrate your MongoDB Atlas data seamlessly into any part of your stack — from microservices to analytics workloads.

This article has provided only a sample of what can be leveraged via MongoDB’s APIs. The MongoDB Query API provides a comprehensive set of features to seamlessly work with data in a native, familiar way. It supports multiple index types, geospatial data, materialized views, full-text search, and much more. In the next part in this MongoDB and MACH Alliance series, we will discuss how a cloud-native SaaS architecture can enable full application flexibility and scalability.

Read the first post in this series, "MACH Aligned for Retail: Microservices."

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Launching Your Tech Career at MongoDB: 2 Interns Share Their Stories

Finding the right job in the tech industry isn’t easy. It’s even more challenging when you’re a new graduate or soon-to-finish college student trying to understand the opportunities for your career in tech. MongoDB aims to make that transition easier with its summer internship program and a new grad program that are designed to provide students and recent grads an opportunity to get their foot in the door of a growing tech company. Betsy Button, a MongoDB software engineer, participated in the MongoDB internship program in 2020. Betsy Button is a former intern from the class of 2020, and she now works full-time at MongoDB as a software engineer. Due to the COVID pandemic, Button’s internship was fully remote — a much different experience than past MongoDB internships. “While a remote internship would never have been my first choice experience, the MongoDB Campus Team’s dedication to making the most out of the summer shined through all of their virtual intern events, network programming, and frequent check-ins,” Button said. She said her challenging work as an intern prepared her for her full-time role today. “I really enjoyed the high-impact project that my intern team worked on throughout the summer,” she said. “Product managers estimated that our work saved the company a significant amount of money every month, which is an awesome outcome for ‘just an internship.’” Becoming a MongoDB Intern MongoDB’s summer internship program launched in 2011 with four interns and has grown to include more than 150 interns around the world working in over a dozen roles, from engineering to product design to marketing. Mentorship is the cornerstone of the MongoDB internship experience. Each intern is paired with a mentor on their team; a Campus Team mentor; and an optional Affinity Group mentor. Interns also participate in sessions that promote professional growth, including learning and development sessions, social events, a guest speaker series, and a roundtable discussion that allows them to meet with company executives and employees across different business units. MongoDB’s New Grad Program provides a seamless transition for interns to continue their career journeys with the company. “We view the 11-week internship program as an extension of the interview process,” said Natalie Chwalk, the program manager for early talent at MongoDB. “Our interns are our pipeline of future leaders at MongoDB, so we are evaluating them on performance as much as they are evaluating us as a potential employer.” Chwalk added: “The end goal is that we convert our interns to entry-level employees. This is the ultimate win-win because we are retaining strong talent, and the new grads are able to begin their careers with a company culture they know and love.” After graduating, software engineer enrollees have three options to choose from if they return to MongoDB to work full-time: Full rotation: The new graduate will rotate on three different engineering teams every six weeks, for 18 weeks total. At the end, the employee will be permanently placed with a team based on feedback, evaluations, and business needs. Department rotation: The new graduate will rotate on three teams within their department on different sub-teams for 18 weeks. At the end, they will be permanently placed with a team, based on the same criteria above. Direct to intern team: The new graduate returns directly back to their intern team. Tristan Wedderburn, a software engineer at MongoDB, found a permanent role after his MongoDB internship. Tristan Wedderburn is a former MongoDB intern and now a software engineer on the Atlas Serverless team at MongoDB. A highly complex project he worked on as an intern led him to realize how valuable the program was. “The project was technically challenging, and it was exciting to see the project shipped into production shortly after the internship, highlighting the impact that interns have the ability to make,” Wedderburn said. Wedderburn found the Affinity Group mentorship as a particularly valuable part of the intern program. “In our sessions, we would discuss technical concepts that I hadn’t been exposed to in my engineering classes, which was cool,” he said. As for what advice he would give to prospective interns and recent graduates? It’s on you to make the most of it, he said. “Your experience is directly related to how much you want to get out of it,” Wedderburn said. “Adopt a learning mindset and ask questions when you don’t understand things. Your team is there to support you.” Button agrees with how to make the most of your time as a MongoDB intern. “Take advantage of the networking opportunities available to interns,” she said. “The people at MongoDB are some of the brightest and kindest that I’ve ever met. It’s worth spending time getting to know others outside your team.” Interested in learning more about MongoDB’s opportunities for students and recent graduates? Check out our intern video or visit our Careers page .

June 23, 2022

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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024