MongoDB Joins Auth0 to Help Startups Combat Security Risks

Steve Jurczak
November 23, 2022
#Startups

We are excited to announce that MongoDB for Startups is collaborating with Auth0 for Startups to provide top security for applications by the most innovative startups.

Why should a startup be part of the MongoDB and Auth0 startup programs?

Customers, investors, and stakeholders expect many different things from a company, but one common requirement is responsibly managing their data.

Companies choose MongoDB because it accelerates application development and makes it easier for developers to work with data.

Developers mindful of security, compliance, and privacy when it comes to data use the robust Auth0 platform to create great customer experiences with features like single sign-on and multi-factor authentication.

“Auth0 and MongoDB are very complementary in nature. While MongoDB provides a strong, secure data platform to store sensitive workloads, Auth0 provides secure access for anyone with the proper authorization," says Soumyarka Mondal, Co-founder of Sybill.ai. "We are safely using Auth0 as one of the data stores for the encryption piece, as well as using those keys to encrypt all of our users’ confidential information inside MongoDB.”

What is the Auth0 for Startups Program?

Auth0, powered by Okta, takes a modern approach to identity and enables startups to provide secure access to any application, for any user. Through Auth0 for Startups, we are bringing the convenience, privacy, and security of Auth0 to early-stage ventures, allowing them to focus on growing their business quickly.

The Auth0 for Startups program is free for one year and supports:

  • 100,000 monthly active users

  • Five enterprise connections

  • Passwordless authentication

  • Breached password detection

  • 50+ integrations, 60+ SDKs, and 50+ social & IdP connections

What is the MongoDB for Startups Program?

MongoDB for Startups is focused on enabling the success of high-growth startups from ideation to IPO. The program is designed to give startups access to the best technical database for their rapidly scaling ventures.

Apply to our program and program participants will receive:

  • $500 in credits for all MongoDB cloud products (valid for 12 months)

  • A dedicated technical advisor for a two-hour, one-to-one consultation to help you with your data migration and optimization

  • Co-marketing opportunities

  • Access to the MongoDB developer ecosystem and access to our VC partners.

Apply to Auth0 For Startups and the MongoDB for Startups Program today.

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MongoDB and AWS: Simplifying OSDU Metadata Management

In this decade of the 2020s, the energy sector is experiencing two major changes at the same time: The transition from fossil to renewables, and the digital transformation that changes the way businesses operate through better applications and tools that help streamline and automate processes. To support both of these challenges, the Open Group OSDU Forum has created a new data platform standard for the energy industry that seeks to reduce data silos and enable transformational workflows via an open, standards-based API set and supporting ecosystem. OSDU (Open Subsurface Data Universe) is an industry-defining initiative that provides a unified approach to store and retrieve data in a standardized way in order to allow reductions in infrastructure cost, simplify the integration of separate business areas, and adopt new energy verticals within the same architectural principles. Amazon Web Services (AWS) — as an early supporter of OSDU — provides a premier, cloud-first offering available across more than 87 availability zones and 27 regions. MongoDB — an OSDU member since 2019 — and AWS are collaborating to leverage MongoDB as part of the AWS OSDU platform for added flexibility and to provide a robust multi-region OSDU offering to major customers. Why MongoDB for OSDU? OSDU provides a unique challenge, as its architecture is set to support a varied data set originating from the oil and gas industry, while also being extensible enough to support the expanding requirements of new energy and renewables. It must be able to support single-use on a laptop for beginning practitioners, yet scale to the needs of experts with varying deployment scenarios — from on-premises, in-field, and cloud — and from single tenant on one region to multi-region and multi-tenant applications. Furthermore, OSDU architectural principles separate raw object data from the metadata that describes it, which puts an additional burden on the flexibility needed to manage OSDU metadata, while supporting all the above requirements. Enter MongoDB Since 2008, MongoDB has championed the use of the document model as the data store that supports a flexible JSON-type structure, which can be considered a superset of different existing data types — from tabular, key-value, and text to geo-spatial, graph, and time series. Thus, MongoDB has the flexibility not only to support just the main metadata services in OSDU but also to adapt to the needs of domain-specific services as OSDU evolves. The flexibility of MongoDB allows users to model and query the data in a variety of ways within the same architecture without the need to proliferate disparate databases for each specific data type, which incurs overhead both in terms of deployment, cost and scale, and the ability to query. The schema flexibility inherent in this document model allows developers to adapt and make changes quickly, without the operational burden that comes with schema changes with traditional tabular databases. MongoDB can also scale from the smallest environment to massive, multi-region deployments, with cross-regional data replication support that is available today across more than 90 regions with MongoDB Atlas . With the addition of MongoDB’s cluster-to-cluster sync , MongoDB can easily support hybrid deployments bridging on-premises or edge to the cloud, a requirement that is increasingly important for energy supermajors or for regions where data sovereignty is paramount. Example: LegalTag An example of the benefit of MongoDB’s document model is OSDU’s LegalTag Compliance Service , which governs the legal status of data in the OSDU data ecosystem. It is a collection of JSON properties that governs how the data can be consumed and ingested. With MongoDB, the properties are directly stored, indexed, and made available to be queried — even via full-text search for more advanced use cases. The schema flexibility simplifies integrating additional derived data from ingested data sources, which is utilized for the further enrichment of the LegalTag metadata. Here the JSON document can accommodate more nodes to integrate this data without the need for new tables and data structures that need to be created and managed. AWS OSDU with MongoDB MongoDB and AWS collaborated to provide a MongoDB-based metadata implementation (Figure 1), which is available for all main OSDU services: Partition, Entitlements, Legal, Schema, Storage. The AWS default ODSU Partition service leverages MongoDB due to its simple replication capabilities (auto-deployable via CloudFormation, Terraform, and Kubernetes), which simplify identifying the correct connection information at runtime to the correct OSDU partition in a multi-region and multi-cluster deployment. The OSDU Entitlements service manages authorization and permissions for access to OSDU services and its data-using groups. The most recent OSDU reference implementation for Entitlements leverages a graph model to manage the relationship between groups, members, and owners. Thus, AWS again chose MongoDB with its inherent graph capabilities through the document model to simplify the implementation without the need to integrate a further dedicated database technology into the architecture. Figure 1:   MongoDB metadata service options with AWS OSDU. Other potential benefits for OSDU MongoDB also offers workload isolation , which provides the ability to dedicate instances only for reporting workloads against the operational dataset. This provides the ability to create real-time observability of the system based on the activity on metadata. Triggers and aggregation pipelines allow the creation of an alternate view of activity in real-time, which can easily be visualized via MongoDB Charts (part of Atlas) without the need for a dedicated visualization system. Flexibility and consistency A major use case for both the energy industry and the direction of OSDU is the ability to capture and preprocess data closest to where it originated. For remote locations where direct connections to the cloud are prohibitive, this approach is often the only option — think Arctic or off-shore locations. Additionally, certain countries have data sovereignty laws that require an alternative deployment option outside of the public cloud. A MongoDB-based OSDU implementation can provide a distinct advantage, as MongoDB as a data platform itself supports deployment in the field (e.g., off-shore), on-premises, in private cloud (e.g., Kubernetes, Terraform), public cloud (e.g., AWS) and as a SaaS implementation (e.g., Atlas). Adoption of MongoDB for OSDU provides consistency across different deployment/cloud scenarios, thereby reducing the overhead for managing and operating a disparate set of technologies where multiple scenarios are required. Conclusion OSDU has been created to change the way data is collected and shared across the oil and gas and energy industry. Its intent is to accelerate digital transformation within the industry. The range of use cases and deployment scenarios requires a solution that provides flexibility in the supported datasets, flexibility for the developer to innovate without additional schema and operational burden, as well as flexibility to be deployable in various environments. Through the collaboration of AWS and MongoDB, there is an additional metadata storage option available for OSDU that provides a modern technology stack with the performance and scalability for the most demanding scenario in the energy industry. 1. MongoDB Atlas 2. MongoDB Edge Computing 3. OSDU Data platform on AWS

November 22, 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