New Data Modeling Learning Path and Certification

Thalita Carrico
March 20, 2024 | Updated: April 24, 2024
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Data modeling is a crucial part of software development. It's what gives structure to the data so that it can be analyzed, used to make decisions, and built into useful applications. It can be complex and challenging, especially when you're juggling performance and maintainability. But here's the good news: a well-designed data model can boost your app's performance.

That's why we've put together a new learning path and certification to help you level up your data modeling skills. Whether you're a seasoned developer looking to deepen your understanding or a newcomer eager to learn, this content is designed to give you the tools you need to create efficient and effective data models. Let's dive in and start building better apps together.

Deep dive into data modeling with MongoDB

The new free, online MongoDB Data Modeling Path will help you build your knowledge from the ground up and prepare you for the certification exam. The curated sequence of videos, hands-on labs, and quizzes provides you with a guided journey to learning data modeling at your own pace.

The learning path will show you a step-by-step method for creating effective data models. You'll learn how to identify entities and workloads, map relationships, and when to embed or reference them. In addition, you'll learn different schema design patterns, how to recognize and address MongoDB anti-patterns, and the basics of schema lifecycle management.

And, here's a little something extra: Once you complete the learning path, you'll automatically score a 50% discount on the new certification!

Learn fast, certify faster

With an extensive array of new educational resources on data modeling for MongoDB, we are thrilled to introduce our new credential, the MongoDB Associate Data Modeler certification, which will allow you to validate and showcase your expertise in the field.

Our certifications are officially recognized by professional institutions, validating and acknowledging your MongoDB expertise. They're a valuable asset for advancing in your role and enhancing your marketability for future positions. Certified individuals earn bragging rights, inclusion in the Credly Talent Directory, and a distinguished Credly badge, making it easy to share your achievements.

The MongoDB Associate Data Modeler certification is designed for experienced users. Candidates should have some familiarity with JSON, and the MongoDB Query API, including aggregations and data modeling on MongoDB. You should also understand the tradeoffs of simplicity versus performance of data modeling techniques. This certification attests to your competency in designing, building, and evolving effective data models in MongoDB, and incorporating data governance into your work.

Explore MongoDB’s new learning path and certification designed to elevate and validate your data modeling skills.

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Nouveau parcours d’apprentissage et certification en modélisation des données

La modélisation des données est un élément crucial du développement logiciel. C’est ce qui structure les données afin qu’elles puissent être analysées, utilisées pour prendre des décisions et intégrées dans des applications utiles. Le processus peut être complexe et difficile, surtout lorsque vous jonglez avec les performances et la maintenabilité. Cependant, un modèle de données bien conçu peut améliorer les performances de votre application. C’est pourquoi nous avons élaboré un nouveau parcours de formation et une nouvelle certification pour vous aider à améliorer vos compétences en modélisation des données. Que vous soyez un développeur expérimenté souhaitant approfondir ses connaissances ou un novice désireux d’apprendre, ce contenu vous fournira les outils dont vous avez besoin pour créer des modèles de données efficaces et performants. Allons-y et commençons à créer ensemble de meilleures applications. Plongez au cœur de la modélisation des données avec MongoDB Le nouveau parcours de modélisation des données MongoDB en ligne et gratuit vous aidera à développer vos connaissances à partir de zéro et à vous préparer à l’examen de certification. La séquence organisée de vidéos, d’ateliers pratiques et de questionnaires vous propose un parcours guidé pour apprendre à modéliser les données à votre propre rythme. Le parcours d’apprentissage vous montrera une méthode étape par étape pour créer des modèles de données efficaces. Vous apprendrez à identifier les entités et les charges de travail, à cartographier les relations et à déterminer quand les intégrer ou les référencer. De plus, vous apprendrez différents modèles de conception de schéma, comment reconnaître et traiter les anti-modèles MongoDB, ainsi que les bases de la gestion du cycle de vie des schémas. Et voici un petit plus : une fois que vous aurez terminé le parcours d’apprentissage, vous bénéficierez automatiquement d’une réduction de 50 % sur la nouvelle certification  ! Apprenez vite, qualifiez-vous plus vite Avec une vaste gamme de nouvelles ressources pédagogiques sur la modélisation des données pour MongoDB, nous sommes ravis de vous présenter notre nouvelle certification, la certification MongoDB Associate Data Modeler , qui vous permettra de valider et de mettre en valeur votre expertise du domaine. Nos certifications valident et reconnaissent votre expertise MongoDB et sont officiellement reconnues par les institutions professionnelles. Elles sont un atout précieux pour progresser dans vos fonctions et améliorer votre employabilité pour de futurs postes. Pour mettre en avant leurs compétences et faire connaître leurs réalisations, les personnes certifiées sont incluses dans le répertoire des talents Credly et obtiennent un badge Credly distingué. La certification MongoDB Associate Data Modeler est conçue pour les utilisateurs expérimentés. Les candidats doivent avoir une certaine maîtrise de JSON et de MongoDB Query API , y compris des agrégations et de la modélisation des données sur MongoDB. Vous devez également comprendre les compromis entre la simplicité et la performance des techniques de modélisation des données. Cette certification atteste de vos compétences dans les domaines de la conception, du développement et de l’évolution de modèles de données efficaces dans MongoDB, ainsi que de l’intégration de la gouvernance des données dans votre travail. Découvrez le nouveau parcours d’apprentissage et la nouvelle certification de MongoDB conçus pour améliorer et valider vos compétences de modélisation des données.

March 20, 2024
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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