Unleashing the Power of MongoDB Atlas and Amazon Web Services (AWS) for Innovative Applications
When you use MongoDB Atlas on AWS, you can focus on driving innovation and business value, instead of managing infrastructure. The combination of MongoDB Atlas, the premier developer data platform, and AWS, the largest global public cloud provider empowers organizations to create scalable and intelligent applications while streamlining their data infrastructure management. With MongoDB Atlas and AWS, building GenAI-powered applications is far simpler. MongoDB Vector Search enables developers to build intelligent applications powered by semantic search and generative AI over any type of data. Organizations can use their proprietary application data and vector embeddings to enhance foundation models like large language models (LLMs) via retrieval-augmented generation (RAG). This approach reduces hallucinations and delivers personalized user experiences while scaling applications seamlessly to meet evolving demands and maintaining top-tier security standards. MongoDB real-world use cases MongoDB helped Forbes accelerate provisioning, maintenance, and disaster-recovery times. Plus, the flexible data structures of MongoDB's document data model allows for faster development and innovation. In another example , a popular convenience store chain reported 99.995% uptime, freeing up its engineers and allowing them to focus on building innovative solutions thanks to Atlas Device Sync . Working with MongoDB helped functional food company MuscleChef transition from a food and beverage business with a website to a data-driven company that leverages customer insights to continuously improve and scale user experience, new product development, operations and logistics, marketing, and communications. Since working with MongoDB, repeat customer orders have surged 49%, purchase frequency saw a double-digit increase, and average order value is 50% higher than its largest competitors. Thousands of customers have been successful running MongoDB Atlas on the robust infrastructure offered by AWS. No-code enterprise application development platform Unqork helps businesses build apps rapidly without writing a line of code. Using MongoDB Atlas, the platform ingests data from multiple sources at scale and pushes it to applications and third-party services. Volvo Connect enables drivers and fleet managers to track trucks, activities, and even insights using a single administrative portal. The versatility and performance of Atlas combined with the AWS global cloud infrastructure helps the business connect critical aspects of their business in completely new ways. Verizon also opted to run Atlas on AWS to unlock the full power of its 5G mobile technology by moving compute elements to the network edge, making the user experience faster. A unified approach to data handling The Atlas developer data platform integrates all of the data services you need to build modern applications that are highly available, performant at global scale, and compliant with the most demanding security and privacy standards within a unified developer experience. With MongoDB Atlas running on AWS Global Cloud Infrastructure, organizations can leverage a single platform to store, manage, and process data at scale, allowing them to concentrate on building intelligent applications and driving business value. Atlas handles transactional data, app-driven analytics, full-text search, generative AI and vector search workloads, stream data processing, and more, all while reducing data infrastructure sprawl and complexity. MongoDB Atlas is available in 27 AWS regions. This allows organizations to deliver fast and consistent user experiences in any region and replicate data across multiple regions to reach end-users globally with high performance and low latency. Additionally, the ability to store data in specific zones ensures compliance with data sovereignty requirements. Security is paramount for both MongoDB Atlas and AWS. MongoDB Atlas is secure by default. It leverages built-in security features across your entire deployment. Atlas helps organizations comply with FedRAMP certification and regulations such as HIPAA, GDPR, PCI DSS, and more. It offers robust security measures , like our groundbreaking queryable encryption, which enables developers to run expressive queries on the encrypted data. MongoDB Atlas also enhances developer productivity with its fully managed developer data platform on AWS. It offers a unified interface/API for all data and application services, seamlessly integrating into development and deployment workflows. MongoDB Atlas also integrates with Amazon CodeWhisperer . This powerful combination accelerates developer innovation for a seamless coding experience, improved efficiency, and exceptional business growth. Conclusion MongoDB Atlas and AWS have worked together for almost a decade to offer a powerful solution for organizations looking to innovate and build intelligent applications. By simplifying data management, enhancing security, and providing a unified developer experience, they ensure that organizations can focus on what truly matters: driving innovation and delivering exceptional user experiences. If you're ready to get started, MongoDB Atlas is available in the AWS Marketplace, and you have the option to start with a free tier. Get started with MongoDB Atlas on AWS today .
Apono Streamlines Data Access with MongoDB Atlas
In today's world of ever-evolving cloud technology, many organizations are struggling to effectively manage data access. From companies that have no access policies in place and allow anyone to access any data, to those that have an existing solution but it's only on-premises, there's a desperate need for cloud-based access management. Apono is an easy-to-use platform that allows centralized access management, removing the trouble of having to depend on a single person to control access to the data. Apono brings reliable access management to the cloud, providing organizations with the security they need to protect their valuable information. And, as a member of the MongoDB for Startups program, Apono is accelerating its evolution as it seeks to expand its capabilities and its offering. MongoDB for Startups offers free MongoDB Atlas credits, one-on-one technical advice, co-marketing opportunities, and access to our vast partner network. Access that's as granular as you need it As organizations work to find the right balance of granular data access, they've often relied on a combination of workflow builders to make it happen. The way this often plays out is that just one person becomes the de facto expert in managing this system, leaving everyone else in the dark. And when they're gone, so is the expertise for managing ongoing access. Apono is a go-to solution for securely managing access to the most confidential and sensitive cloud resources businesses possess, from production environments to applications. It simplifies database access management across all three major cloud providers. A lot of database access management solutions only help with cluster access management, self-hosted databases, or cloud databases — but rarely not all of them. Apono enables organizations to manage access to database solutions whether they are self-hosted or in the cloud. Apono enables highly granular permissions, going beyond granting access to a cluster. It allows you to manage access to individual databases. In MongoDB Atlas, Apono goes as far as allowing you to manage access to individual collections. Apono is unique in its ability to offer that level of granular access management. Simplified and streamlined user experience From restricting read and write access to granting temporary permissions, Apono makes it easy for administrators to manage the entire process with a few clicks. According to the company's own internal data, about 80% of administrators are able to create access flows without any help in under two minutes. It's a very intuitive solution that also gives you full visibility into who is accessing or requesting access to resources and for how long. Administrators can choose how they want to interact with the Apono UX. They can use the intuitive administrator portal, the command line interface (CLI), Terraform, or the Apono API. From an end-user standpoint, Apono supports Slack, Teams, CLI, and a web portal with time-saving administrative features like request again and favorites. Additional time-savers include the ability to automate much of the process of granting permissions. Surprisingly, many organizations still handle permissions on an ad hoc basis through informal, one-off requests over text or email. Apono enables administrators to automate access flows, which not only saves time but is also more secure because it reduces the likelihood that someone will assign the wrong permission to a person or group by mistake. Apono also makes it easy to conduct access reviews, which are often required for regulatory purposes. These reviews can also be scheduled and automated so that reports are automatically shared with the stakeholders who need them. The security perimeter in the age of the cloud Back when most systems were primarily on-prem, it was critical to set up a security perimeter that limited access to anything behind the network firewall. Today, with remote work, cloud architectures, and the proliferation of edge devices, there is no longer one single firewall. Rather, identity has become the new security perimeter. "People work from anywhere, any IP, any device, even their phones. So it's becoming increasingly important to make sure that users have just the right amount of privileges," says Sharon Kisluk, Lead Product Manager at Apono. "If I give someone standing admin access to a cluster, what happens if they destroy the entire cluster by accident?" To prevent data loss due to human error or incorrect permissions, Apono works under the principle of least privilege, which means that any user or operation is allowed to access only the information and resources that are necessary for its legitimate purpose. That's why, out of the box, Apono gives you the ability to restrict all access to critical production environments. Multi-cloud access control The maturity of today's cloud computing has led to a large majority — around 87% — of companies to deploy to multiple cloud environments. Like MongoDB Atlas , Apono is available on all three major cloud platforms: AWS, Google Cloud, and Microsoft Azure. Also like MongoDB Atlas, Apono supports self-hosted Kubernetes. "We realized that people hate working with so many different role-based access control systems," says Kisluk. "Each system has its own user management. If you create policies or permissions in AWS, you have to do the same thing in Google Cloud and Azure if you're multi-cloud, and then you have to do the same thing for the databases." With Apono, you can create access flow bundles, which is a role abstraction that works across systems. For example, you can create a role called, "prod access" that enables you to access production databases and grant permission to only those who require access to those systems. And any system that's tagged as a production system will inherit those permissions, even if they're hosted by different cloud providers. Using MongoDB Atlas combined with Apono, administrators can establish global access policies and roll them out across the entire distributed system with just a few clicks. Product roadmap Apono was recently named to the Gartner Magic Quadrant for Privileged Access Management (PAM). While the recognition was unexpected at Apono, Kisluk says it just goes to show how Apono is truly the next thing in cloud PAM. Apono is expanding its cloud PAM by offering more complex access flow scenarios, or what is often referred to as, "if this, then that." These are scenarios that are triggered based on certain conditions being met. For example, if there's a production incident, you can grant access automatically for only the duration of the bug fix without submitting a special request. Get to know Apono Apono is a self-serve solution, so anyone can sign up with their email, connect to their cloud environment and database, and start using the product. Apono will also be at AWS re:Invent to be held in Las Vegas from November 27 to December 1. Don't forget to visit them and, of course, MongoDB and find out how these two powerful solutions are simplifying and streamlining privilege access management for developers and systems administrators. Sign up for our MongoDB for Startups program today!
Retrieval Augmented Generation (RAG): The Open-Book Test for GenAI
The release of ChatGPT in November 2022 marked a groundbreaking moment for AI, introducing the world to an entirely new realm of possibilities created by the fusion of generative AI and machine learning foundation models, or large language models (LLMs). In order to truly unlock the power of LLMs, organizations need to not only access the innovative commercial and open-source models but also feed them vast amounts of quality internal and up-to-date data. By combining a mix of proprietary and public data in the models, organizations can expect more accurate and relevant LLM responses that better mirror what's happening at the moment. The ideal way to do this today is by leveraging retrieval-augmented generation (RAG), a powerful approach in natural language processing (NLP) that combines information retrieval and text generation. Most people by now are familiar with the concept of prompt engineering, which is essentially augmenting prompts to direct the LLM to answer in a certain way. With RAG, you're augmenting prompts with proprietary data to direct the LLM to answer in a certain way based on contextual data. The retrieved information serves as a basis for generating coherent and contextually relevant text. This combination allows AI models to provide more accurate, informative, and context-aware responses to queries or prompts. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Applying retrieval-augmented generation (RAG) in the real world Let's use a stock quote as an example to illustrate the usefulness of retrieval-augmented generation in a real-world scenario. Since LLMs aren't trained on recent data like stock prices, the LLM will hallucinate and make up an answer or deflect from answering the question entirely. Using retrieval-augmented generation, you would first fetch the latest news snippets from a database (often using vector embeddings in a vector database or MongoDB Atlas Vector Search ) that contains the latest stock news. Then, you insert or "augment" these snippets into the LLM prompt. Finally, you instruct the LLM to reference the up-to-date stock news in answering the question. With RAG, because there is no retraining of the LLM required, the retrieval is very fast (sub 100 ms latency) and well-suited for real-time applications. Another common application of retrieval-augmented generation is in chatbots or question-answering systems. When a user asks a question, the system can use the retrieval mechanism to gather relevant information from a vast dataset, and then it generates a natural language response that incorporates the retrieved facts. RAG vs. fine-tuning Users will immediately bump up against the limits of GenAI anytime there's a question that requires information that sits outside the LLM's training corpus, resulting in hallucinations, inaccuracies, or deflection. RAG fills in the gaps in knowledge that the LLM wasn't trained on, essentially turning the question-answering task into an “open-book quiz,” which is easier and less complex than an open and unbounded question-answering task. Fine-tuning is another way to augment LLMs with custom data, but unlike RAG it's like giving it entirely new memories or a lobotomy. It's also time- and resource-intensive, generally not viable for grounding LLMs in a specific context, and especially unsuitable for highly volatile, time-sensitive information and personal data. Conclusion Retrieval-augmented generation can improve the quality of generated text by ensuring it's grounded in relevant, contextual, real-world knowledge. It can also help in scenarios where the AI model needs to access information that it wasn't trained on, making it particularly useful for tasks that require factual accuracy, such as research, customer support, or content generation. By leveraging RAG with your own proprietary data, you can better serve your current customers and give yourself a significant competitive edge with reliable, relevant, and accurate AI-generated output. To learn more about how Atlas helps organizations integrate and operationalize GenAI and LLM data, download our white paper, Embedding Generative AI and Advanced Search into your Apps with MongoDB . If you're interested in leveraging generative AI at your organization, reach out to us today and find out how we can help your digital transformation.
4 Key Considerations for Unlocking the Power of GenAI
New Regulations Set to Snare Data-Handlers into Compliance
Now that the General Data Protection Regulation (GDPR) has become more firmly entrenched in the EU, several states in the U.S. are introducing similar data governance measures that will impose extra obligations on businesses that handle consumer data in those jurisdictions. California, Colorado, Connecticut, Utah, and Virginia all have new or amended data consumer privacy laws that have already gone into effect or are expected to by year's end. Control vs. controllers While most data privacy laws focus on giving consumers greater insight and control over their personal data, they also require data controllers and processors to protect the security and integrity of the data they handle for consumers. All five new state privacy laws require data controllers and processors to protect the information they process with reasonable data security measures. What constitutes reasonable remains up for debate, but recent trends point toward an information security program that goes beyond current requirements for safeguards and advocates for a more strategic approach based on risk assessment. Sectors like financial services and healthcare have long been accustomed to mandatory data security measures since both industries are subject to regulatory regimes — the Gramm-Leach-Bliley Act (GLBA), Financial Industry Regulatory Authority (FINRA), and the Payment Card Industry (PCI) for financial institutions, and the Health Insurance Portability and Accountability Act (HIPAA) for healthcare organizations. But gradually, the expansion of existing regulations and the introduction of new privacy laws at the state level are snaring more businesses that seek to do business in those jurisdictions. First, in 2013, the Omnibus Rule expanded the definition of a “business associate” to include all entities that create, receive, maintain, or transmit patient data on behalf of a covered entity as defined by HIPAA. So, businesses that were not previously subject to HIPAA requirements became bound by its requirements for safeguarding protected health information (PHI) if they had any in their systems or if they committed any transactions involving PHI. The Omnibus Rule was an early indicator that regulatory bodies would be casting a wider net to include not just traditional industry organizations but also data handlers that sat squarely in the middle of the data supply chain. Now, with more state consumer data privacy laws rolling out, more businesses will be required to implement reasonable security safeguards to protect any sensitive data anywhere in their systems. What's reasonable security? The National Institute of Standards and Technology (NIST) Cybersecurity Framework helps organizations better understand, manage, and mitigate cybersecurity risks. It encourages adaptability in the face of an evolving threat landscape, and the importance of data resilience measures to ensure the protection of critical assets and information. It's widely adopted across several industries for strengthening cybersecurity practices. The Center for Internet Security (CIS) also publishes examples of security controls that some state attorneys general specify as meeting a minimum level of information security that data handlers should meet. One of the universal threads running through most cybersecurity frameworks like those from NIST and CIS is the importance of data resilience. Data resilience is crucial because it ensures that important personal data like patient health records and bank customers' financial records remain available and intact, even in the face of unexpected events, such as hardware failures, cyberattacks, or natural disasters. It safeguards business continuity, preserves information integrity, and maintains trust by reducing the risk of data loss or downtime. Aside from the reputational harm that comes from being the victim of a cybersecurity event like a ransomware attack or data breach, there's an increasing risk that affected businesses will be subject to regulatory enforcement in the form of fines for running afoul of new restrictions. Security features and controls in MongoDB At MongoDB, we are intimately familiar with technical safeguards related to sensitive data and regulatory requirements as they relate to data security. MongoDB Atlas is designed for the needs of businesses in regulated industries. Atlas is a global, multi-cloud application data platform built around a resilient, performant, and scalable distributed database designed to ensure important data remains intact and available. Atlas is architected to provide automated database resilience and mitigate the downtime risks associated with hardware failures, unintended actions, and targeted attacks. Atlas clusters offer continuous cloud backups and multi-region clusters for database redundancy as well as multi-cloud clusters for cross-cloud database resilience. Atlas automatically distributes data across clouds based on how you've configured it, making managing multi-cloud clusters extremely easy. Multi-cloud cluster deployments are particularly relevant for organizations that must comply with data sovereignty regulations but have limited deployment options due to sparse regional coverage from their primary cloud provider. With MongoDB Atlas, administrators can encrypt MongoDB data in transit over the network and at rest in permanent storage and backups. For data in transit, support for TLS allows clients to connect to MongoDB over an encrypted channel. Data is automatically encrypted while at rest through transparent disk encryption at all three major cloud providers, AWS, Google Cloud, and Microsoft Azure. Additionally, MongoDB’s in-use encryption technologies like client-side Field-Level Encryption (FLE) and Queryable Encryption enable administrators to selectively encrypt sensitive fields, with each optionally secured with its own key. All encrypted fields on the server – stored in-memory, in system logs, at-rest, and in backups – are rendered as ciphertext, making them unreadable to any party and are only decrypted on the client side using the encryption keys. MongoDB also offers a complete set of administrative features that enable organizations to create, deploy, and manage policies for data access according to their own internal requirements, including database authentication, multi-factor authorization (MFA), and role-based access controls (RBAC). Of course, no business wants to lose data, and every business would prefer to avoid the reputational harm that comes from data breaches having data held for ransom. With the potential for hefty fines for running afoul of new privacy legislation, businesses have even more reasons to implement protective measures to ensure the resilience of their systems. As regulatory creep continues to expand across the data landscape, businesses must take it upon themselves to ensure data integrity and resilience are high priorities across the organization. For more information on data resilience features in MongoDB Atlas, download our Data Resilience Strategy with MongoDB Atlas whitepaper.
How to Avoid GenAI Sprawl and Complexity
There's no doubt that generative AI and large language models (LLMs) are disruptive forces that will continue to transform our industry and economy in profound ways. But there's also something very familiar about the path organizations are taking to tap into GenAI capabilities. It's the same journey that happens anytime there's a need for data that serves a very specific and narrow purpose. We've seen it with search where bolt-on full-text search engines have proliferated, resulting in search-specific domains and expertise required to deploy and maintain them. We've also seen it with time-series data where the need to deliver real-time experiences while solving for intermittent connectivity has resulted in a proliferation of edge-specific solutions for handling time-stamped data. And now we're seeing it with GenAI and LLMs, where niche solutions are emerging for handling the volume and velocity of all the new data that organizations are creating. The challenge for IT decision-makers is finding a way to capitalize on innovative new ways of using and working with data while minimizing the extra expertise, storage, and computing resources required for deploying and maintaining purpose-built solutions. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Purpose-built cost and complexity The process of onboarding search databases illustrates the downstream effects that adding a purpose-built database has on developers. In order to leverage advanced search features like fuzzy search and synonyms, organizations will typically onboard a search-specific solution such as Solr, Elasticsearch, Algolia, and OpenSearch. A dedicated search database is yet another system that requires already scarce IT resources to deploy, manage, and maintain. Niche or purpose-built solutions like these often require technology veterans who can expertly deploy and optimize them. More often than not, it's the responsibility of one person or a small team to figure out how to stand up, configure, and optimize the new search environment as they go along. Time-series data is another example. The effort it takes to write sync code that resolves conflicts between the mobile device and the back end consumes a significant amount of developer time. On top of that, the work is non-differentiating since users expect to see up-to-date information and not lose data as a result of poorly written conflict-resolution code. So developers are spending precious time on work that is not of strategic importance to the business, nor does it differentiate their product or service from your competition. The arrival and proliferation of GenAI and LLMs is likely to accelerate new IT investments in order to capitalize on this powerful, game-changing technology. Many of these investments will take the form of dedicated technology resources and developer talent to operationalize. But the last thing tech buyers and developers need is another niche solution that pulls resources away from other strategically important initiatives. Documents to the rescue Leveraging GenAI and LLMs to gain new insights, create new user experiences, and drive new sources of revenue can entail something other than additional architectural sprawl and complexity. Drawing on the powerful document data model and an intuitive API, the MongoDB Atlas developer data platform allows developers to move swiftly and take advantage of fast-paced breakthroughs in GenAI without having to learn new tools or proprietary services. Documents are the perfect vehicle for GenAI feature development because they provide an intuitive and easy-to-understand mapping of data into code objects. Plus, the flexibility they provide enables developers to adapt to ever-changing application requirements, whether it's the addition of new types of data or the implementation of new features. The huge diversity of your typical application data and even vector embeddings of thousands of dimensions can all be handled with documents. The MongoDB Query API makes developers' lives easier, allowing them to use one unified and consistent system to perform CRUD operations while also taking advantage of more sophisticated features such as keyword and vector search , analytics, and stream processing — all without having to switch between different query languages and drivers, helping to keep your tech stack agile and streamlined. Making the most out of GenAI AI-driven innovation is pushing the envelope of what is possible in terms of the user experience — but to find real transformative business value, it must be seamlessly integrated as part of a comprehensive, feature-rich application that moves the needle for companies in meaningful ways. MongoDB Atlas takes the complexity out of AI-driven projects. Our intuitive developer data platform streamlines the process of bringing new experiences to market quickly and cost-effectively. With Atlas, you can reduce the risk and complexity associated with operational and security models, data wrangling, integration work, and data duplication. To find out more about how Atlas helps organizations integrate and operationalize GenAI and LLM data, download our white paper, Embedding Generative AI and Advanced Search into your Apps with MongoDB . If you're interested in leveraging generative AI at your organization, reach out to us today and find out how we can help your digital transformation.
Multi-Cloud Data Resilience with MongoDB Atlas
MongoDB Atlas is architected to ensure that data remains safe and secure at all times, offering automated database resilience against hardware failures and regional outages. One of the reasons why MongoDB Atlas is able to provide high levels of resilience and availability is because it's the only developer data platform that's available on all three major public cloud platforms, AWS, Microsoft Azure, and Google Cloud. In fact, deploying Atlas to multiple cloud providers or regions is a simple matter of choosing how many nodes you want to deploy and in which cloud providers. And, once you're in the cloud, you can span databases cross-cloud without having to worry about setting up complicated ETL processes, so you're never locked into one cloud provider or running the risk of concentrating all your data in a single location. By utilizing Atlas to distribute data across multiple clouds, businesses can quickly and painlessly achieve high service levels for critical applications with virtually no latency. In the event of an outage, the self-healing process kicks in — automatically electing a secondary member to take the reins within seconds without operations to the database being affected — all without any manual intervention. Having access to multiple regions across the world provides businesses with the flexibility to adhere to data sovereignty requirements without needing to compromise on availability; cloud providers that offer just one region in a specific geography can leave users vulnerable to system disruptions, but multi-cloud clusters enable organizations to deploy additional nodes in regions not provided by their primary cloud provider. You also have the option, should you need it, of using another cloud provider in countries where a provider may only have one data center in a given region. Geo-resilience in Atlas MongoDB Atlas puts you in control of where your data is stored, with more than 110 regions across AWS, Google Cloud, and Microsoft Azure — to ensure your managed databases are close enough to your application servers for fast response times. Atlas is designed to ensure maximum uptime, no matter which region or cloud provider you're using. It provides built-in geographic resilience for multi-zone, multi-region, and multi-cloud clusters with built-in data resilience features: Atlas takes proactive measures to ensure the resilience of single-region clusters, automatically distributing replica set members across different cloud availability zones for maximum protection and maximum uptime. Atlas helps businesses stay resilient in the face of regional failure by leveraging multi-region clusters to replicate data across geographic boundaries and keep operations running smoothly. For added protection and peace of mind, multi-cloud clusters provide the perfect solution to address cloud provider failure and ensure data replication across multiple clouds. With AWS Availability Zones, Google Regions and Zones, and Azure Availability Zones, each independent zone is made up of one or more discrete data centers, all equipped with redundant power, and networking. A cloud region refers to the actual geographical site within a cloud service provider's infrastructure where a cluster or replica set is deployed. Regardless of the number of zones present in a cloud region, MongoDB Atlas will always deploy replica sets with at least three members to ensure the highest levels of availability and data durability. Atlas gives you complete flexibility when it comes to configuring multi-region protection. A multi-region cluster can be hosted in multiple regions within a single cloud provider or multiple regions across multiple cloud providers. Cloud provider disruptions can take various forms, from relatively minor capacity constraints to devastating outages that can wreak havoc on your application deployments. To reduce the risk of a major outage, organizations should consider the benefits of distributing their data across multiple clouds to maximize database and application resilience. With multi-cloud clusters, you can easily access the powerful and unique tools and services within AWS, Google Cloud, and Azure — giving you global reach, low-latency performance, regional data security, and resilient data replication and migration. Plus, Atlas takes the hassle out of the equation, automatically distributing your data across clouds for maximum fault tolerance and giving you the freedom to explore cross-cloud migration options at any time. Multi-cloud clusters provide the same features as single-cloud, multi-region clusters — such as continuous cloud backups, automated data tiering, and workload isolation for data analytics and visualization — but they also offer the added advantage of increased cross-cloud resilience. For more information on multi-cloud features in MongoDB Atlas, download our Data Resilience Strategy with MongoDB Atlas whitepaper. Find out more about deploying multi-cloud clusters from our documentation.
4 grandes motivos para atualizar para o MongoDB 7.0
Ultimamente, temos pegado a estrada e feito notícia em uma série de eventos nas principais cidades do mundo. Um dos grandes destaques é o lançamento do MongoDB 7.0 , que oferece um conjunto abrangente de recursos projetados para agilizar as operações, melhorar o desempenho e aumentar a segurança. Com este lançamento, o MongoDB reafirma-se como a melhor escolha para organizações que buscam aumentar a produtividade de suas equipes de desenvolvimento à medida que constroem aplicações modernas e distribuídas. A versão 7.0 possui todos os recursos lançados nas versões anteriores, com recursos adicionais destinados a facilitar a construção de software pelos desenvolvedores. #1 - Desempenho aprimorado O MongoDB 7.0 traz melhorias significativas para trabalhar com dados Time Series , especialmente conjuntos de dados exigentes e de alto volume de todos os formatos. Essas melhorias resultam em melhor otimização e compactação de armazenamento, bem como melhor desempenho de consulta. Os desenvolvedores experimentarão um manuseio ainda melhor de dados de alta cardinalidade, melhor escalabilidade e desempenho geral; permitindo que você managed dados Time Series de maneira mais eficiente e econômica. Change streams agora oferecerá suporte a casos de uso ainda mais amplos: lidar com alterações em documentos grandes, mesmo com pré-imagens e pós-imagens, sem causar erros inesperados. #2 - Migrações mais suaves As atualizações na Cluster-to-Cluster Sync (mongosync) permitirão uma migração de dados mais eficiente em diversos cenários. Cluster-to-Cluster Sync agora oferece maior flexibilidade na sincronização entre clusters com topologias diferentes, como conjuntos de réplicas a clusters fragmentados. A sincronização filtrada permite sincronizar conjuntos de dados específicos em vez de todo o cluster. Atlas Live Migrate agora oferece suporte a migrações para clusters que executam MongoDB 6.0.4+ entregando migrações mais rápidas e resilientes em casos de interrupção durante o processo de migração. #3 - Experiência simplificada do desenvolvedor Com novos aprimoramentos no aggregation pipeline — incluindo compound wildcard indexes , percentis aproximados e operadores bit a bit — os desenvolvedores podem desfrutar de maior flexibilidade e desempenho na indexação e consulta de dados. Com o MongoDB 7.0, os desenvolvedores também podem implementar variáveis de função de usuário no aggregation pipeline , permitindo que uma única visualização exiba dados diferentes com base nas permissões dos usuários logados. Suporte para atualizações e Time Series collection exclusões refinadas na e novas métricas para ajudar a selecionar uma chave de fragmento ajudam a reduzir o esforço do desenvolvedor e agilizar o processo de desenvolvimento. #4 - Controles de segurança mais fortes O MongoDB 7.0 fortalece os recursos de segurança com Queryable Encryption para ajudar os clientes a criptografar dados confidenciais e executar consultas de igualdade em dados criptografados totalmente aleatórios. As melhorias de segurança garantem que os desenvolvedores possam criar e implantar aplicativos com confiança, sabendo que seus dados estão protegidos e em conformidade com os padrões e protocolos de segurança mais recentes. Porque esperar? Com uma série de novos recursos e melhorias projetados para tornar sua equipe mais produtiva, o MongoDB 7.0 é a escolha perfeita para organizações que buscam levar seu desenvolvimento para o próximo nível. Desde desempenho aprimorado até recursos de segurança mais robustos, o MongoDB 7.0 facilita a construção do próximo grande sucesso. Registre-se no Atlas agora e comece a construir hoje . Se desejar orientação sobre como atualizar para a versão 7.0, nossa equipe de serviços profissionais oferece suporte de atualização para ajudar a garantir uma transição tranquila. Para saber mais, consulte Consultoria MongoDB .
4 grandes razones para actualizar a MongoDB 7.0
Últimamente, hemos estado viajando y siendo noticia en una serie de eventos en las principales ciudades de todo el mundo. Uno de los aspectos más destacados es el lanzamiento de MongoDB 7.0 , que ofrece un conjunto completo de funciones diseñadas para agilizar las operaciones, mejorar el rendimiento y mejorar la seguridad. Con este lanzamiento, MongoDB se reafirma como la mejor opción para las organizaciones que buscan aumentar la productividad de sus equipos de desarrollo mientras crean aplicaciones modernas y distribuidas. La versión 7.0 tiene todas las funciones lanzadas en versiones anteriores con funciones adicionales destinadas a facilitar a los desarrolladores la creación de software. #1 - Rendimiento mejorado MongoDB 7.0 aporta mejoras significativas al trabajar con datos de series temporales, especialmente conjuntos de datos exigentes y de gran volumen de todas las formas. Estas mejoras dan como resultado una mejor optimización y compresión del almacenamiento, así como un mejor rendimiento de las consultas. Los desarrolladores experimentarán un manejo aún mejor de datos de alta cardinalidad, una escalabilidad mejorada y un rendimiento general; permitiéndole gestionar datos de series temporales de forma más eficiente y rentable. Change Streams ahora admitirán casos de uso aún más amplios: manejar cambios en documentos grandes, incluso con imágenes previas y posteriores, sin causar errores inesperados. #2 - Migraciones más fluidas Las actualizaciones de la sincronización de cluster to cluster sync (mongosync) permitirán una migración de datos más eficiente en una variedad de escenarios. La sincronización de clúster a clúster ahora proporciona una mayor flexibilidad en la sincronización entre clústeres con topologías diferentes, como desde conjuntos de réplicas hasta clústeres fragmentados. La sincronización filtrada permite sincronizar conjuntos de datos específicos en lugar de todo el clúster. Atlas Live Migrate ahora admite migraciones para clústeres que ejecutan MongoDB 6.0.4+ ofrecer migraciones que sean más rápidas y resistentes en casos de interrupción durante el proceso de migración. #3 - Experiencia de desarrollador optimizada Con nuevas mejoras en el proceso de agregación, incluidos compound wildcard indexes , percentiles aproximados y operadores bit a bit, los desarrolladores pueden disfrutar de una mayor flexibilidad y rendimiento en la indexación y consulta de datos. Con MongoDB 7.0, los desarrolladores también pueden implementar variables de rol de usuario dentro de los canales de agregación, lo que permite que una vista única muestre diferentes datos según los permisos de los usuarios que han iniciado sesión. La compatibilidad con actualizaciones y eliminaciones detalladas en colecciones de series temporales y nuevas métricas para ayudar a seleccionar una clave de fragmento ayudan a reducir el esfuerzo de los desarrolladores y agilizar el proceso de desarrollo. #4 - Controles de seguridad más estrictos MongoDB 7.0 fortalece las capacidades de seguridad con Queryable Encryption para ayudar a los clientes a cifrar datos confidenciales y ejecutar consultas de igualdad en datos cifrados totalmente aleatorios. Las mejoras de seguridad garantizan que los desarrolladores puedan crear e implementar aplicaciones con confianza, sabiendo que sus datos están protegidos y cumplen con los últimos estándares y protocolos de seguridad. ¿Por qué esperar? Con una serie de nuevas características y mejoras diseñadas para hacer que su equipo sea más productivo, MongoDB 7.0 es la opción perfecta para las organizaciones que buscan llevar su desarrollo al siguiente nivel. Desde un rendimiento mejorado hasta funciones de seguridad más sólidas, MongoDB 7.0 facilita la creación del próximo gran avance. Regístrese en Atlas ahora y comience a construir hoy . Si desea orientación sobre la actualización a 7.0, nuestro equipo de servicios profesionales ofrece soporte de actualización para ayudar a garantizar una transición sin problemas. Para obtener más información, consulte MongoDB Consulting .
How to Stand Out From the Crowd When Everyone Uses Generative AI
The arrival of Generative AI powered by Large Language Models (LLMs) in 2022 has captivated business leaders and everyday consumers due to its revolutionary potential. As the dawn of another new era in technology begins, the gold rush is on to leverage Generative AI and drive disruption in markets or risk becoming a victim of said disruption. Now, a vast array of vendors are bringing to market Generative-AI enablers and products. This proliferation of fast-followers leaves executives and software developers feeling overwhelmed. These promising tools must also be able to be modified from just a demo or prototype to full-scale production use. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Success doesn't necessarily equate to differentiation, especially when everyone has access to the same tools. In this environment, the key to market differentiation is layering your own unique proprietary data on top of Generative AI powered by LLMs. Documents, the underlying data model for MongoDB Atlas , allow you to combine your proprietary data with LLM-powered insights in ways that previous tabular data models couldn't, providing the potential for a dynamic, superior level of market differentiation. The way to do this is by transforming your proprietary data - structured and unstructured - into vector embeddings. They capture the semantic meaning and contextual information of data making them suitable for various tasks like text classification, machine translation, sentiment analysis, and more. With vector embeddings, you can easily unlock a world of possibilities for your AI models. Vector embeddings provide numerical encodings that capture the structure and patterns of your data. This semantically rich representation makes calculations of relationships and similarities between objects a breeze, allowing you to create powerful applications that weren’t possible before. MongoDB's ability to ingest and quickly process customer data from various sources allows organizations to build a unified, real-time view of their customers, which is valuable when powering Generative AI solutions like chatbot and question-answer (Q-A) customer service experiences. We recently announced the release of MongoDB Vector Search , a fast and easy way to build semantic search and AI-powered applications by integrating the operational database and vector store in a single, unified, and fully managed platform — along with support integrations into large language models (LLMs). Rather than create a tangled web of cut-and-paste technologies for your new AI-driven experiences, our developer data platform built on MongoDB Atlas provides the streamlined approach you need to bring those experiences to market quickly and efficiently, reducing operational and security models, data wrangling, integration work, and data duplication, while still keeping costs and risk low. With MongoDB Atlas at the core of your AI-powered applications, you can benefit from a unified platform that combines the best of operational, analytical, and generative AI data services for building intelligent, reliable systems designed to stay in sync with the latest developments, scale with user demands, and keep data secure and performant. To find out more about how Atlas Vector Search enables you to create vector embeddings tailored to your needs (using the machine learning model of your choice including OpenAI, Hugging Face, and more) and store them securely in Atlas, download our white paper, Embedding Generative AI and Advanced Search into your Apps with MongoDB . If you're interested in leveraging generative AI at your organization, reach out to us today and find out how we can help.
Data Resilience with MongoDB Atlas
Data is the central currency in today's digital economy. Studies have shown that 43% of companies that experience major data loss incidents are unable to resume business operations. A range of scenarios can lead to data loss, yet within the realm of database technology, they typically fall under three main categories: catastrophic technical malfunctions, human error, and cyber attacks. A data loss event due to a catastrophic breakdown, human error, or cyber attack is not a matter of if, but a matter of when it will occur. Hence, businesses need to focus on how to avoid and minimize the effects as much as possible. Failure to effectively address these risks can lead to extended periods of downtime of a few hours or even a few weeks following an incident. The average cost of cyberattacks is a surprising $4.45 million, with some attacks costing in the hundreds of millions. Reputational harm is harder to quantify but no doubt real and substantial. The specific industry you're in might be subject to regulatory frameworks designed to counter cyber attacks. Businesses that are subject to regulatory regimes must maintain compliance with these requirements. This can determine the configuration of your disaster recovery approach. In this blog post, we'll explain the key disaster recovery (DR) capabilities available with MongoDB Atlas . We'll also cover the core responsibilities and strategies for data resilience including remediation, and recovery objectives (RTO/RPO). Planning for data resilience in Atlas Data resilience is not a one-size-fits-all proposition, which is why we offer a range of choices in Atlas for a comprehensive strategy. Our sensible defaults ensure you're automatically safeguarded, while also offering a variety of choices to precisely align with the needs of each individual application. When formulating a disaster recovery plan, organizations commonly begin by assessing their recovery point objective (RPO) and recovery time objective (RTO). The RPO specifies the amount of data the business can tolerate losing during an incident, while the RTO indicates the speed of recovery. Since not all data carries the same urgency, analyzing the RPO and RTO on a per-application basis is important. For instance, critical customer data might have specific demands compared to clickstream analytics. The criteria for RTO, RPO, and the length of time you need to retain backups will influence the financial and performance implications of maintaining backups. With MongoDB Atlas, we provide standard protective measures by default, with customizable options for tailoring protection to the service level agreements specified by the RPO and RTO in your DR plan. These are enhanced by additional features that can be leveraged to achieve greater levels of availability and durability for your most vital tasks. These features can be grouped into two main categories: prevention and recovery. Backup, granular recovery, and resilience There are many built-in features that are designed to prevent disasters from ever happening in the first place. Some key features and capabilities that enable a comprehensive prevention strategy include multi-region and multi-cloud clusters , encryption at rest , Queryable Encryption , cluster termination safeguards , backup compliance protocols , and the capability to test resilience . (We will discuss the features in-depth in part two of this series.) While prevention might satisfy the resilience needs of certain applications, different applications may demand greater resilience against failures based on the business requirements of data protection and disaster recovery. MongoDB provides comprehensive management of data backups, including the geographic distribution of backups across multiple regions, and the ability to prevent backups from being deleted, all through an automated retention schedule. Recovery capabilities are aimed at supporting RTO and minimizing data loss and include continuous cloud backups with point-in-time recovery. Atlas cloud backups utilize the native snapshot feature of your cluster's cloud service provider, ensuring backup storage is kept separate from your MongoDB Atlas instances. Backups are essentially snapshots that capture the condition of your database cluster at a specific moment. They serve as a safeguard in case data is lost or becomes corrupted. For M10+ clusters, you have the option of utilizing Atlas Cloud Backups, which leverage the cluster's cloud service provider for storing backups in a localized manner. Atlas comes with strong default backup retention of 12 months out of the box. You also have the option to customize snapshot and retention schedules, including the time of day for snapshots, the frequency at which snapshots are taken over time, and retention duration. Another important feature is continuous cloud backup with point-in-time recovery, which enables you to restore data to the moment just before any incident or disruption, such as a cyber attack. To ensure your backups are regionally redundant and you can still restore even if the primary region that your backups are in is down, MongoDB Atlas offers the ability to copy these critical backups, with the point-in-time data, to any secondary region available from your cloud provider in Atlas. For the most stringent regulations, or for businesses that want to ensure backups are available even after a bad actor or cyber attack, MongoDB Atlas can ensure that no user, regardless of role, can ever delete a backup before a predefined protected retention period with the Backup Compliance Policy. Whatever your regulatory obligations or business needs are, MongoDB Atlas provides the flexibility to tailor your backup settings for requirements. Crucially, this ensures you can recover quickly, minimizing data loss and meeting your RPO in the event of a disaster recovery scenario. When properly configured, testing has shown that Atlas can quickly recover to the exact timestamp before a disaster or failure event, giving you a zero-minute RPO and RTO of less than 15 minutes when utilizing optimized restores. Recovery times can vary due to cloud provider disk warming and which point in time you are restoring to. So, it is important to also test this regularly. This means that regardless of your regulatory or business requirements, MongoDB Atlas allows you to configure your backups to ensure that you can meet your recovery requirements and, most importantly, recover with precision and speed to ensure that your data loss is minimal and your recovery point objectives are met should you experience a recovery event. Conclusion As regulations and business needs continue to evolve, and cyber-attacks become more sophisticated and varied, creating and implementing a data resilience strategy can be simple and manageable. MongoDB Atlas comes equipped with built-in measures that deliver robust data resilience at the database layer, ensuring your ability to both avoid incidents and promptly restore operations with minimal data loss if an incident does occur. Furthermore, setting up and overseeing additional advanced data resilience features is straightforward, with automation driven by a pre-configured policy that operates seamlessly at any scale. This streamlined approach supports compliance without the need for manual interventions, all within the MongoDB Atlas platform. For more information on the data resilience and disaster recovery features in MongoDB Atlas, download the Data Resilience Strategy with MongoDB Atlas whitepaper. To get started on Atlas today, we invite you to launch a free tier today .
4 Grandes Raisons de Passer à MongoDB 7.0
Dernièrement, nous avons pris la route et fait l'actualité lors d'une série d'événements dans les plus grandes villes du monde. L'un des points forts est le lancement de la version 7.0 de MongoDB , qui offre une suite complète de fonctionnalités conçues pour rationaliser les opérations, améliorer les performances et renforcer la sécurité. Avec cette version, MongoDB se réaffirme comme un choix de premier ordre pour les organisations qui cherchent à stimuler la productivité de leurs équipes de développement lorsqu'elles créent des applications modernes et distribuées. La version 7.0 reprend toutes les fonctionnalités des versions précédentes et en ajoute d'autres destinées à faciliter la création de logiciels pour les développeurs. #1 - Amélioration des performances MongoDB 7.0 apporte des améliorations significatives sur la façon de gérer les données Time Series, en particulier les ensembles de données exigeants et volumineux de toutes formes. Ces améliorations se traduisent par une meilleure optimisation du stockage et de la compression, ainsi que par des performances accrues en matière d'interrogation. Les développeurs bénéficieront d'une meilleure gestion des données de grande cardinalité, d'une meilleure évolutivité et de meilleures performances globales, ce qui vous permettra d'accéder aux données Time Series de manière plus efficace et plus rentable. Change Streams supportera désormais des cas d'utilisation encore plus larges : gérer les modifications dans les documents volumineux, même avec des pré-images et des post-images, sans provoquer d'erreurs inattendues. #2 - Des migrations plus fluides Les mises à jour de Cluster-to-Cluster Sync (mongosync) permettront une migration plus efficace des données dans divers scénarios. Cluster-to-Cluster Sync offre désormais une plus grande souplesse dans la synchronisation entre des clusters ayant des topologies différentes, par exemple entre des clusters définis par réplique et des clusters partitionnés. La synchronisation filtrée permet de synchroniser des données spécifiques définir au lieu de l'ensemble du cluster. Atlas Live Migrate supporte désormais les migrations pour les clusters utilisant MongoDB 6.0.4+. des migrations plus rapides et plus résilientes en cas d'interruption du processus de migration. #3 - Une expérience simplifiée pour les développeurs Grâce aux nouvelles améliorations apportées au site aggregation pipeline - notamment les index composés de caractères génériques , les percentiles approximatifs et les opérateurs bitwise - les développeurs bénéficient d'une plus grande souplesse et de meilleures performances en matière d'indexation et d'interrogation des données. Avec MongoDB 7.0, les développeurs peuvent également mettre en œuvre des variables de rôle d'utilisateur dans aggregation pipeline, ce qui permet à Single View d'afficher des données différentes en fonction des autorisations des utilisateurs du journal. Le support pour les mises à jour et les suppressions à grain fin dans les collections Time Series et les nouvelles mesures pour aider à sélectionner une clé de sharding aident à réduire l'effort du développeur et à rationaliser le processus de développement. #4 - Contrôles de sécurité renforcés MongoDB 7.0 renforce les capacités de sécurité avec Queryable Encryption pour aider les clients à crypter les données sensibles et à exécuter des requêtes d'égalité sur des données cryptées entièrement randomisées. Les améliorations apportées à la sécurité permettent aux développeurs de créer et de déployer des applications en toute confiance, en sachant que leurs données sont protégées et conformes aux normes et protocoles de sécurité les plus récents. Pourquoi attendre ? Avec une foule de nouvelles fonctionnalités et d'améliorations conçues pour rendre votre équipe plus productive, MongoDB 7.0 est le choix idéal pour les organisations qui cherchent à faire passer leur développement à la vitesse supérieure. De l'amélioration des performances au renforcement des fonctionnalités de sécurité, MongoDB 7.0 facilite la création de la prochaine grande entreprise. Inscrivez-vous dès maintenant à Atlas et commencez à construire dès aujourd'hui . Si vous souhaitez obtenir des conseils sur la mise à niveau vers la version 7.0, notre équipe de services professionnels propose un soutien à la mise à niveau afin d'assurer une transition en douceur. Pour en savoir plus, voir MongoDB Consulting .