Apono Streamlines Data Access with MongoDB Atlas

Steve Jurczak
October 30, 2023
#Startups

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!

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Retrieval Augmented Generation (RAG): The Open-Book Test for Gen AI

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.

October 26, 2023
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MongoDB Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024