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Leveraging Database Observability at MongoDB: Unlocking Performance Insights and Optimization Strategies
This post is the first in a three-part series on leveraging database observability. Observability has evolved into an essential information technology component, offering advanced insights into system performance beyond traditional monitoring. While monitoring aims to identify problems, observability helps understand and resolve them. Businesses prioritizing observability experience less downtime, leading to enhanced user experiences and improved ROI. Indeed, Splunk’s The State of Observability 2023 report quantified the financial impact of downtime—more than $150,000 per hour. Furthermore, observability leaders reported 33% fewer outages and achieved eight times better ROI than new adopters. Throughout this series, we'll define database observability at MongoDB, explore our suite of tools, delve into third-party integrations, and discuss everyday use cases. We will also establish a shared methodology and vocabulary for discussing observability at MongoDB and highlight the tools and features that have delivered value for our customers. Observability and MongoDB’s strategy Monitoring involves using tools to track real-time operations and alert teams to issues. As defined by Gartner, observability evolves monitoring into a process that provides deep insights into digital business applications, enhancing innovation and customer experience. The key difference is that monitoring detects the presence of issues, while observability gathers detailed information to understand and resolve them, which is crucial for modern IT infrastructure needs. Databases, in particular, play a critical role in this IT ecosystem, where performance and resilience directly impact business outcomes. This advancement is essential for DevOps, Database Administrators, and economic buyers responsible for these databases, as it enhances system reliability, encourages innovation, and supports financial objectives. Ultimately, observability provides comprehensive insights into system performance, health, and reliability by seamlessly integrating and contextualizing telemetry data. MongoDB leverages a unique observability strategy with out-of-the-box tools that automatically monitor and optimize customer databases. Explicitly designed for MongoDB environments, our system provides continuous feedback and answers critical questions—What is happening? Where is the issue? Why is it occurring? How do I fix it?—to enhance performance, increase productivity, and minimize downtime. Supporting MongoDB Atlas (our fully managed platform), Cloud Manager, and Ops Manager, as well as tailored monitoring solutions for the full range of developer data platform products (from enhanced search functionalities to app services and search nodes). Our approach meets the evolving needs of customer applications by: Leveraging MongoDB expertise: The MongoDB observability suite integrates efficiency and best practices from the beginning of the development cycle. As MongoDB platform experts, we use our deep knowledge to provide top-tier optimization insights. We apply our extensive understanding of our tools to ensure our customers benefit from a high-performing and resilient database. Offering streamlined metrics: We integrate our metrics seamlessly into our customers' central observability stacks and workflows. This creates a 'plug-and-play' experience that effortlessly aligns with popular monitoring systems like Datadog, New Relic, and Prometheus. Thus, it provides a unified view of customer application performance and deep insights into their database within a comprehensive dashboard. Breaking down MongoDB’s observability offerings Tailored database performance management MongoDB employs automated tools for comprehensive database performance management, focused on real-time optimization, strategic scaling, and best practices in schema design. Using out-of-the-box tools ensures high-performing, scalable, and cost-efficient database environments ideal for modern applications. Key features include: Performance Advisor : Provides index recommendations to improve read and write performance, significantly boosting overall efficiency. Schema Advisor : Supports flexible schema design and query execution analysis to enhance performance, scalability, and validation rules for schema compliance. Opt-in Autoscaling (only available in Atlas): Optimizes resource use, manages operational costs, ensures continuous availability, and adjusts resources based on demand, preventing downtimes. Performance Advisor in action Foundational monitoring for in-depth insights MongoDB provides foundational monitoring tools and out-of-the-box insights for optimal cluster health and performance after initial database setup. These tools both help reduce the burden of performing manual tasks while laying the groundwork for detailed and granular analysis of metrics and system performance aimed at enhancing query performance, reducing execution times, and lowering resource usage. These tools include: Monitoring Charts : These charts offer detailed metrics on hardware, database operations, replication status, sharded, and search/vector search with a fine-grained metric resolution to identify issues and track trends. Real-Time Performance Panel : This panel displays live network traffic, database operations, and hardware stats, helping to identify critical operations, evaluate query times, and monitor network load and throughput. Query Insights : The recently announced Namespace Insights provides users with collection-level latency statistics. At the same time, the enhanced cluster-centric Query Profiler gives an expanded view of query performance, significantly enhancing visibility and operational efficiency across the cluster. Learn more about both! Comprehensive alerting and seamless integrations MongoDB Atlas's sophisticated alerting system offers over 200 event types, providing teams with comprehensive control and visibility over their environments. Users can fine-tune their alerting strategy with customization options to fit their specific requirements. Additionally, MongoDB Atlas enhances team collaboration and ensures a unified view of application performance through seamless integrations with third-party tools like Slack, PagerDuty, and DataDog. These integrations simplify management tasks and leverage existing workflows for optimum operational effectiveness. What’s next? Enhanced database observability Observability is more than just a technical requirement—it's a strategic asset that enhances operational efficiency and economic viability. Through MongoDB's observability suite, organizations can optimize and scale system performance and fuel innovation. MongoDB is dedicated to continuously improving this suite to manage large-scale data better and meet demanding performance standards. Our commitment is reflected in our efforts to advance MongoDB's observability features, providing specific insights that deliver actionable intelligence tailored to our customers' needs. Look for the next post in this series, where we'll explore various tools and their integration, illustrated through common use cases. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. For more information, see Monitor Your Database Deployment .
MongoDB Sales Recognized as a Top 20 Org for Professional Development by RepVue
It’s no secret that investing in your people can lead to incredible outcomes, especially for sales organizations. Companies with engaged employees see increased productivity, more profitability, and higher earnings per share . And because go-to-market (GTM) functions are responsible for more than 40% of revenue at high-growth software companies, driving effectiveness and engagement within GTM teams is paramount. At MongoDB, the sales and customer success team makes up over 30% of our company and is one of our fastest-growing organizations. We know that providing opportunities for development and receiving support from leadership drives both engagement and career growth for our teams, ultimately making them more effective and empowered to deliver results. Within the last two years, over 160 employees have been promoted into leadership roles within our sales organization, and we’re continually growing the next generation of leaders at MongoDB. So we’re thrilled to announce that MongoDB’s sales organization has received two RepVue Reppy Awards for the Spring 2024 Reppys, highlighting our commitment to excellence in building a world-class sales team and our prioritization of career development opportunities. RepVue recognizes MongoDB for being a Top 20 org for Professional Development and a Top 20 org for Publicly Funded Companies. About the RepVue Reppy awards RepVue collects millions of data points every year from its B2B sales professional user base. This data is voluntarily submitted on RepVue and includes information about their sales organization’s compensation, culture, overall employee experience, and more. This data is then used to rate the organization across categories and generate an overall RepVue Score. Sales organizations that secure a place in the Top 20 for their category or the Top 5 in a metro area become eligible to receive Reppys. These rankings serve as a testament to the exceptional sales environment these organizations consistently deliver. Hear what some of our employees have to say about working in sales at MongoDB. Adam, Enterprise Account Executive, North America “Throughout my time at MongoDB, I’ve been challenged to learn a complex technology while working with some of the smartest people in the industry. I am regularly humbled by the team we have and the engineers we work with. Everything I’ve learned from them has helped me sharpen my skills and advance my career.” Read our blog to learn more . Marie, Regional Director, Inside Sales, EMEA “Being part of the sales organization at MongoDB isn’t just about being a good sales representative, it’s about being part of a growth mindset community. Participating in our BDR to CRO career development program has helped me take my sales career to new heights.” Read our blog to learn more . Salvatore, Area Vice President, EMEA “As a member of our sales team, you’ll find a culture of transparency and meritocracy. We are focused on developing individuals to be great salespeople and like to think that we are the best technology sales school in the world. Our team is competitive, but there is a lot of camaraderie and support for one another. You’ll have the opportunity to express yourself at your best and know that you will be valued for your unique perspectives and experiences.” Read our blog to learn more . Simon, Senior Vice President, APAC “Our role as leaders is to develop our teams, from the bottom all the way up. We need to develop the next wave of leaders so that they’re prepared to step up when the time comes. Being part of a high-growth tech company requires taking risks and making mistakes. We have a high standard, and we hold each other accountable, but it never comes at the cost of creating an environment where people are afraid to fail.” Read our blog to learn more . Cedric, Chief Revenue Officer “If you join MongoDB, you’ll have to expect a culture of learning and growth. We drive a strong culture of ownership and accountability and we are going to invest in you very aggressively in terms of training and coaching, but we are also going to push you as much as we can so that you can grow your sales skills. Whether you sit in New Delhi, Sydney, Paris, London, or New York, you have a chance in this company to take on more responsibilities and grow your career faster than at any other company in the world. ” Watch the video to learn more . Stay up to date with MongoDB awards and culture highlights by joining our talent community .
Transforming Predictive Maintenance with AI: Real-Time Audio-Based Diagnostics with Atlas Vector Search
Wind turbines are a critical component in the shift away from fossil fuels toward more sustainable, green sources of energy. According to the International Energy Agency (IEA), the global capacity of wind energy has been growing rapidly, reaching over 743 gigawatts by 2023. Wind energy, in particular, has one of the greatest potentials to increase countries' renewable capacity growth. Solar PV and wind additions are forecast to more than double by 2028 compared with 2022, continuously breaking records over the forecast period. This growth highlights the increasing reliance on wind power and, consequently, the need for effective maintenance strategies. Keeping wind turbines operating at maximum capacity is essential to ensuring their continued contribution to the energy grid. Like any mechanical device, wind turbines must undergo periodic maintenance to keep them operating at optimal levels. In recent years, advancements in technology—particularly in AI and machine learning—have played a significant role by introducing predictive maintenance breakthroughs to industrial processes like periodic maintenance. By integrating AI into renewable energy systems, organizations of all sizes can reduce costs and gain efficiencies. In this post, we will dig into an AI application use case for real-time anomaly detection through sound input, showcasing the impact of AI and MongoDB Atlas Vector Search for predictive maintenance of wind turbines. Predictive Maintenance in Modern Industries Companies increasingly invest in predictive maintenance to optimize their operations and drive efficiency. Research from Deloitte indicates that predictive maintenance can reduce equipment downtime by 5–15 percent, increase labor productivity by 5–20 percent, and reduce overall new equipment costs by 3–5 percent. This helps organizations maximize their investment in equipment and infrastructure. By implementing predictive maintenance strategies, companies can anticipate equipment failures before they occur, ultimately resulting in longer equipment lifetimes, tighter budget control, and higher overall throughput. More concretely, businesses aim to reduce mean time to repair, optimal ordering of replacement parts, efficient people management, and reduced overall maintenance costs. Leveraging data interoperability, real-time analysis, modeling and simulation, and machine learning techniques, predictive maintenance enables companies to thrive in today's competitive landscape. However, despite its immense potential, predictive maintenance also presents significant challenges. One major hurdle is the consolidation of heterogeneous data, as predictive maintenance systems often need to integrate data from various formats and sources that can be difficult to integrate. Scalability also becomes a concern when dealing with the high volumes of IoT signals generated by numerous devices and sensors. And lastly, managing and analyzing this vast amount of data in real-time poses challenges that must be overcome to realize the full benefits of predictive maintenance initiatives. At its core, predictive maintenance begins with real-time diagnostics, enabling proactive identification and mitigation of potential equipment failures in real-time. Figure 1: Predictive Maintenance starts with real-time diagnostics However, while AI has been employed for real-time diagnostics for some time, the main challenge has been acquiring and utilizing the necessary data for training AI models. Traditional methods have struggled with incorporating unstructured data into these models effectively. Enter gen AI and vector search technologies, positioned to revolutionize this landscape. Flexible data platforms working together with AI algorithms can help generate insights from diverse data types, including images, video, audio, geospatial data, and more, paving the way for more robust and efficient maintenance strategies. In this context, MongoDB Atlas Vector Search stands out as a foundational element for effective and efficient gen AI-powered predictive maintenance models. Why MongoDB and Atlas Vector Search? For several reasons, MongoDB stands out as the preferred database solution for modern applications. Figure 2: MongoDB Atlas Developer Data Platform Document data model One of the reasons why the document model is well-suited to the needs of modern applications is its ability to store diverse data types in BSON (Binary JSON) format, ranging from structured to unstructured. This flexibility essentially eliminates the middle layer necessary to convert to a SQL-like format, resulting in easier-to-maintain applications, lower development times, and faster response to changes. Time series collections MongoDB excels in handling time-series data generated by edge devices, IoT sensors, PLCs, SCADA systems, and more. With dedicated time-series collections, MongoDB provides efficient storage and retrieval of time-stamped data, enabling real-time monitoring and analysis. Real-time data processing and aggregation MongoDB's adeptness in real-time data processing is crucial for immediate diagnostics and responses, ensuring timely interventions to prevent costly repairs and downtime. Its powerful aggregation capabilities facilitate the synthesis of data from multiple sources, providing comprehensive insights into fleet-wide performance trends. Developer data platform Beyond just storing data, MongoDB Atlas is a multi-cloud developer data platform, providing the flexibility required to build a diverse range of applications. Atlas includes features like transactional processing, text-based search, vector search, in-app analytics, and more through an elegant and integrated suite of data services. It offers developers a top-tier experience through a unified query interface, all while meeting the most demanding requirements for resilience, scalability, and cybersecurity. Atlas Vector Search Among the out-of-the-box features offered by MongoDB Atlas, Atlas Vector Search stands out, enabling the search of unstructured data effortlessly. You can generate vector embeddings with machine learning models like the ones found in OpenAI or Hugging Face, and store and index them in Atlas. This feature facilitates the indexing of vector representations of objects and retrieves those that are semantically most similar to your query. Explore the capabilities of Atlas Vector Search . This functionality is especially interesting for unstructured data that was previously hard to leverage, such as text, images, and audio, allowing searches that combine audio, video, metadata, production equipment data, or sensor measurements to provide an answer to a query. Let's delve into how simple it is to leverage AI to significantly enhance the sophistication of predictive maintenance models with MongoDB Atlas. Real-time audio-based diagnostics with Atlas Vector Search In our demonstration, we'll showcase real-time audio-based diagnostics applied to a wind turbine. It's important to note that while we focus on wind turbines here, the concept can be extrapolated to any machine, vehicle, or device emitting sound. To illustrate this concept, we'll utilize a handheld fan as our makeshift wind turbine. Wind turbines emit different sounds depending on their operational status. By continuously monitoring the turbine’s audio, our system can accurately specify the current operational status of the equipment and reduce the risk of unexpected breakdowns. Early detection of potential issues allows for enhanced operational efficiency, minimizing the time and resources required for manual inspections. Additionally, timely identification can prevent costly repairs and reduce overall turbine downtime, thus enhancing cost-effectiveness. Now, let’s have a look at how this demo works! Figure 3: Application Architecture Audio Preparation We begin by capturing the audio from the equipment in different situations (normal operation, high vs. low load, equipment obstructed, not operating, etc.). Once each sound is collected, we use an embedding model to process the audio data to convert it to a vector. This step is crucial because by generating embeddings for each audio track, which are high-dimensional vector representations, we are essentially capturing the unique characteristics of the sound. We then upload these vector embeddings to MongoDB Atlas. By adding just a few examples of sounds in our database, they are ready to be searched (and essentially compared) with the sound emitted by our equipment during its operation in real-time. Audio-based diagnosis Now, we put our equipment into normal operation and start capturing the sound it is making in real-time. In this demonstration, we capture one-second clips of audio. Then, with the same embedding model used before, we take our audio clips and convert them to vector embeddings in real-time. This process happens in milliseconds, allowing us to have real-time monitoring of the audio. The one-second audio clips, now converted to vector embeddings, are then sent to MongoDB Atlas Vector Search, which can search for and find the most similar vectors from the ones we previously recorded in our audio preparation phase. The result is given back with a percentage of similarity, enabling a very accurate prediction of the current status of the operation of the wind turbine. These steps are performed repeatedly every second, leveraging fast embedding of vectors and quick searches, allowing for real-time monitoring based on sound. Check out the video below to see it in action! Transforming Predictive Maintenance with AI and MongoDB Predictive maintenance offers substantial benefits but poses challenges like data integration and scalability. MongoDB stands out as a preferred database solution, offering scalability, flexibility, and real-time data processing. As technology advances, AI integration promises to further revolutionize the industry. Thank you to Ralph Johnson and Han Heloir for their valuable contributions to this demo! Ready to revolutionize your predictive maintenance strategy with AI and MongoDB Atlas Vector Search? Try it out yourself by following the simple steps outlined in our Github repo ! Explore how MongoDB empowers manufacturing operations by visiting these resources: Transforming Industries with MongoDB and AI: Manufacturing and Motion MongoDB for Automotive: Driving Innovation from Factory to Finish Line
Driven by Values: One Account Executive’s Journey into Personal and Professional Growth
May is Asian American and Pacific Islander Heritage Month. Hear from Adam, Enterprise Account Executive and member of the MongoDB_API employee resource group, as he shares about his role at MongoDB, the importance of living company values, and how MongoDB has contributed to his personal and professional growth. My journey into tech I grew up as a first-generation Asian American in a town outside of San Diego. While San Diego is not traditionally known for being diverse, I’m fortunate that my town always felt relatively progressive and welcoming. The biggest challenges were the ones that existed in our own home and the career and life expectations that had been set for me as a first-generation child. My parents pushed me towards pursuing a career in medicine at a young age and invested in my education as a pre-med student. When they learned that I was shifting my career toward tech sales as an adult, it was difficult for them to digest and understand. Looking back, while non-conventional for backgrounds of tech sellers, pre-med did, in fact, set me up for success when learning an extremely technical product with a complex sales cycle. I started exploring an opportunity at MongoDB after hearing about the unique sales program and significant investment in employee growth. The hiring manager dove into my background and discussed what personally drove me, showing me how much MongoDB wants to ensure a mutually beneficial fit. I joined MongoDB, working in new logo acquisition, moved into enterprise, and am now part of a global strategic team working with one of our largest customers. I’m selling the entire suite of MongoDB’s offerings: Enterprise Advanced , Atlas (our fully managed database), and, most recently, Atlas as a Vector database powering and scaling GenAI initiatives. As I look towards the rest of this year, I have never felt more empowered to bring our expertise and technology to customers as true partners, helping them accelerate their C-level initiatives and drive meaningful impact within their organizations and to their customers. MongoDB Atlas is perfectly positioned and differentiated to support gen AI projects at scale, all within a unified developer data platform. Find a company that lives its values One of MongoDB's company values is to Be Intellectually Honest . We are encouraged to share our perspectives, even if some folks disagree, while also being open-minded to alternative perspectives so that we can all grow towards our goals together. Everyone is open to collaboration when asked, all the way up to our executive leadership. I regularly meet with both my peers and skip levels, and have never felt blocked when it made sense to connect with our executives. I remember a time early on when I was feeling overwhelmed on how to best approach breaking into an account. After working with my leader, they acknowledged my efforts in certain areas, but also quickly identified opportunities that I had completely overlooked based on my own bias within the account. With additional suggestions on how to pursue those opportunities, I was able to quickly feel comfortable with a refreshed approach and landed the first deal. Another core value at MongoDB is to Embrace the Power of Differences . One of the things I’ve appreciated greatly at MongoDB is leadership’s intentionality in creating a diverse environment and emphasizing the importance of pursuing that as an organization. Making sure that everyone feels valued, heard, and treated with respect is an absolute top priority for leaders. Finally, Build Together is a value I live every single day. On a daily basis I work with our wider sales ecosystem who help scale the sales team’s efforts and support our success. Sales Development Representatives help me scale my reach to engage individuals and teams I may have overlooked. Solutions Architects are key in pulling technical expertise from our 50k+ customers and highlighting why they chose MongoDB to accelerate innovation. Customer Success Managers are instrumental in connecting external stakeholders with our internal SMEs and thought leaders to help execute ideas. Deal Strategy and Legal support through complex close cycles, ensuring the fastest, best outcome for all parties involved. Each of these teams are experts in their own domain and are critical parts of both the pre-sales and post-sales process to ensure our teams and customers are as successful as possible. A key component of this is communication, prep, and respect for each other's roles to help drive efficiency throughout the process. A focus on personal and professional growth Throughout my time at MongoDB, I’ve been challenged to learn a complex technology while working with some of the smartest people in the industry. I am regularly humbled by the team we have and the engineers we work with. Everything I’ve learned from them has helped me sharpen my skills and advance my career. As I reflect on Asian American and Pacific Islander Heritage Month, I encourage fellow salespeople from the AAPI community to ask for help early and often, whether it’s questions about technology, growth opportunities, or customer engagement. While it may feel intimidating at first, especially earlier on in your career, if you’re at the right organization, you should feel empowered to be vulnerable and supported in your own growth. I’d also recommend finding a network or community with a variety of skill sets and experience to learn from. Doing so has allowed me to gain different perspectives and continue growing personally and professionally. Our sales ecosystem is growing globally. Join our talent community to stay informed about MongoDB culture content and career opportunities.
Stay Compliant with MongoDB’s Latest Certifications: ISO 9001, TISAX, HDS, and TX-RAMP
Ensuring compliance with regulations and security standards across industries and regions is a crucial aspect of MongoDB’s commitment to protecting customer data. That’s why we’re excited to announce that MongoDB Atlas has achieved certifications for ISO 9001, TISAX, HDS, and TX-RAMP, further solidifying our dedication to data security and regulatory compliance for both enterprise and public sector organizations. MongoDB Atlas achieved these certifications across AWS, Azure, and Google Cloud supported regions, thus providing customers the flexibility to adopt a multi-cloud model to support their workloads. In order to achieve each of these four new certifications, MongoDB Atlas underwent independent verification of its quality management, platform security, privacy, documentation, and organizational controls. These certifications—and the independent verifications required to achieve them—help ensure that Atlas meets organizations’ compliance, regulatory, and policy objectives, including the unique compliance needs of highly regulated industries. Read on to learn more about MongoDB’s new ISO 9001, TISAX, HDS, and TX-RAMP certifications, and how they can benefit organizations of all sizes. ISO 9001 Developed by the International Organization for Standardization (ISO), ISO 9001:2015 is an international standard for quality management systems (QMS) that is widely recognized across industries and organizations of all sizes. It provides a framework and guiding principles to systematically deliver products and services at consistently high quality to customers while ensuring compliance with regulations. MongoDB Atlas’s ISO 9001:2015 certification provides assurance to customers that we have implemented a robust QMS and are committed to consistently meeting their requirements and complying with all applicable regulations. It also emphasizes the importance of process control and continual improvement at MongoDB, which leads to greater consistency in product or service quality over time. Visit the Trust Center to learn more about MongoDB's ISO 9001 certification . TISAX The Trusted Information Security Assessment Exchange, or TISAX, is a certification program for information security in the automotive industry. Based on information security requirements created by the German Association of the Automotive Industry (VDA), TISAX helps European automotive companies streamline security evaluations by providing an industry-specific security framework for assessing information security for the wide landscape of suppliers, OEMs, and partners that contribute to the automotive supply chain. There are three assessment levels of TISAX certification. MongoDB has demonstrated compliance with the assessment level 3 (AL3) TISAX certification, which is the highest assessment level available and signifies a supplier's ability to handle and protect highly sensitive data, while also maintaining high availability. MongoDB Atlas's TISAX certification assists automotive industry customers in meeting their rigorous compliance needs. Additionally, it assures these customers that their data will be safeguarded to the highest standards within MongoDB Atlas, with robust measures in place for business continuity, disaster recovery, and risk mitigation. Visit the Trust Center to learn more about MongoDB’s TISAX certification . HDS France's HDS regulations and certification, known as Hébergeur de Données de Santé (Health Data Hosting), ensure organizations comply with basic requirements for hosting personal health data. According to the French Public Health Code, any organization hosting health data from healthcare activities in France must obtain HDS certification. By securing HDS certification, MongoDB Atlas helps those customers hosting health data in France to comply with HDS regulations and instills confidence that robust security controls and practices are in place to protect this highly sensitive data. Visit the Trust Center to learn more about MongoDB’s HDS certification . TX-RAMP The Texas Risk and Authorization Management Program, or TX-RAMP, is a certification program established in Texas that ensures the security of cloud computing services used by state governmental agencies. In order to demonstrate compliance with the security criteria required for TX-RAMP certification, MongoDB Atlas was assessed by the Texas Department of Information Resources (DIR). MongoDB Atlas for Government is already TX-RAMP certified by virtue of it being FedRAMP Moderate authorized. By securing TX-RAMP certification, MongoDB Atlas simplifies procurement for public sector customers in Texas seeking to adopt MongoDB Atlas on AWS, Azure, or Google Cloud. Visit the Trust Center to learn more about MongoDB’s TX-RAMP certification . MongoDB is dedicated to securing your data. We do so through state-of-the-art technical and organizational security controls, numerous regulatory and compliance resources, and a constantly growing collection of third-party attestations and certifications. Our new ISO 9001, TISAX, HDS, and TX-RAMP certifications help us ensure compliance with regulations and security standards across diverse industries and regions, both for enterprise and public sector organizations. To learn more about MongoDB’s technical and organizational security measures, visit the Technical and Organizational Security Measures page .
Enhancing the MongoDB Atlas Go SDK with Automated Code Generation
The MongoDB Atlas API Experience team is committed to offering a seamless developer experience to customers who build and automate against the MongoDB Atlas developer data platform. We offer various programmatic tools, such as the Atlas CLI and the Terraform Atlas Provider , and maintain the Atlas Admin API with key features, including versioning and Open API specification . Our latest offering, the MongoDB Atlas Go SDK , empowers developers to manage Atlas using Go . It eliminates the need for low-level Admin API calls by utilizing higher-level abstractions. New SDK versions are automatically generated on each Atlas update, ensuring access to the latest Atlas administrative capabilities. In this post, we’ll discuss the advantages of using an SDK over direct API calls, explore our decision for code generation over manual development, share insights from engineering challenges, review our adoption experience, and discuss some next steps for the Atlas SDKs. Benefits of using an SDK over direct API calls Direct API calls offer flexibility and fine-grained control but become cumbersome for complex interactions. Developers manually handle tasks like authentication, error handling, and response parsing, which can be time-consuming and error-prone. Additionally, keeping up with API updates can require regular low-level code updates. The Atlas Go SDK simplifies development with higher-level abstractions. Pre-built functions and structs encapsulate API interactions, reducing boilerplate code. This frees developers to focus on core application logic rather than API integration intricacies. The SDK automates authentication elements, error handling, and response parsing, reducing boilerplate code and errors. Plus, staying up-to-date becomes low-effort, as new SDK versions are auto-generated with each Atlas release. While leveraging new functionalities might require some code updates, the SDK significantly simplifies integration compared to direct API calls. Moving from a manual client to an auto-generated SDK Previously, MongoDB’s bespoke Go Client for MongoDB Atlas powered internal tools and customer applications. However, maintaining a manually written client increased toil and impacted the team’s productivity. Keeping code quality high required constant effort, leading to a backlog of unsupported Atlas features and a struggle to keep pace with new functionalities. As Go-based Atlas applications proliferated, the limitations of manual maintenance became clear. We needed a more scalable solution. We had previously built a mechanism to automatically generate the Open API specification for the Atlas Admin API. This machine-readable document details how to interact with the Atlas functionality through its REST API, accessible through the “Download” option in the MongoDB Atlas Administration API portal . This led us to explore leveraging that specification for client code generation as well. Building on this foundation, the Atlas Go SDK leverages the Open API spec and a robust delivery pipeline to streamline client generation. The pipeline scans for changes in the spec and triggers the generation of a new SDK version upon detection, producing a pull request as an intermediate output. Following our review and merge, the pipeline requires no other manual steps to continue working on the release process, ultimately publishing a new SDK version. Code generation has improved output consistency and speed over manual development, freeing up resources for other impactful projects. To showcase the time savings, adding support for a new resource in the old manual Go Client could take roughly two engineer days. The new SDK reduces that time to a less-than-an-hour code review, as all other steps are automated. Major challenges and solutions Transitioning to an auto-generated SDK wasn’t without its hurdles. Selecting the right tooling was crucial, and we explored both commercial and open-source solutions for Open API-based code generation. We opted for openapi-generator due to its open-source nature. The choice prioritized flexibility and control, ensuring long-term project autonomy. It also allowed us to open-source the Go SDK generation codebase, fostering community contributions and improvements. Leveraging the Atlas Open API specification, originally intended for documentation , presented unique challenges. While this approach offered a single source of truth, we encountered discrepancies between the spec and actual API behavior. Notably, minor, non-breaking API changes sometimes resulted in significant, breaking changes in the generated client. For instance, marking a field as optional in the API (non-breaking) turns the equivalent Go model property into a pointer (breaking). This discovery necessitated a two-fold solution. We addressed the root cause by improving the Open API specification, ensuring better alignment with code generation requirements. Then we developed an automated transformation process to optimize the spec for generating code. This transformed version remains optimized for code generation while the original spec continues to serve its original purpose of live API documentation. Having a pull request review as part of the release process has helped us ensure quality. In some early cases, it allowed us to catch issues, apply SDK-wide improvements, and add linting rules to prevent reoccurrence. Maintaining compatibility with the existing Go client was crucial for an easy migration experience. However, achieving perfect compatibility wasn’t always feasible due to inconsistencies in the handwritten client. We meticulously evaluated each change, balancing compatibility with the benefits of code generation. We also created a migration guide and best practices to aid with migrating existing applications. Finally, to minimize confusion and client bloat, we decided that each Go SDK release should target a single Admin API version. That simplified integration but created a versioning challenge: Go’s semantic versioning uses major bumps for breaking changes. We needed to communicate the targeted Atlas API version within the SDK version while simultaneously denoting breaking SDK changes unrelated to API updates. Our solution was to develop a unique major versioning scheme for the Go SDK. It incorporates the Admin API’s date as a prefix, with additional digits signaling breaking SDK changes for that specific API version. While unconventional, it adheres to semantic versioning and keeps developers informed. Adoption experience Our Atlas CLI tool was the proving ground for the new Go SDK. In 2023, it became the first application to migrate from the manual Go client by leveraging pre-release versions of the Go SDK. That staged adoption approach yielded significant benefits. By migrating early, we uncovered valuable improvement areas for the SDK, directly influencing its architecture. This early feedback loop also provided crucial insights into effective SDK generation. The migration wasn’t without its complexities. It addressed cross-cutting issues across three distinct layers: the Open API spec, the SDK generation, and the CLI integration, each presenting unique development challenges. However, this comprehensive approach ensured that all layers benefitted from the process, and we raised quality across the board. Following the successful CLI integration, we confidently expanded the SDK’s reach across our product portfolio. Now, it serves as a prominent and trusted middleware solution, not only for the Atlas CLI , but also for several Atlas DevOps tools. Recent additions include the Atlas integrations for AWS CloudFormation and CDK . The Terraform Atlas Provider and the Atlas Kubernetes Operator are also on the immediate roadmap, further solidifying the SDK as a core component within our DevOps ecosystem. External adoption of the Atlas Go SDK is also gaining momentum. Over 30% of customer-developed Go-based interactions with the Atlas Admin API now leverage the new SDK, a trend we see increasing monthly. Looking ahead As Atlas continues to scale, the pace of features will only continue to accelerate. Our primary focus for the Go SDK is to continuously expose new Atlas features as they are released in the Admin API while introducing quality-of-life improvements for developers at every opportunity. We strive to reduce boilerplate code, improve Error Handling, simplify Authentication / Authorization, and to enrich documentation with high-quality examples. The success of code generation has us exploring what else can be automated. Our team is looking at which other offerings could benefit from automation to free up development time for impactful projects that might be less readily automatable. Finally, we understand that our developer community thrives on various languages and technologies. Not using Go? Let us know what other languages you’d like to see supported (e.g. Python, Java, TypeScript) by sharing your feedback . Conclusion The Atlas Go SDK empowers Go developers to streamline interactions with the Atlas cloud platform. By leveraging code generation and a focus on developer experience, the SDK offers several advantages over manual API calls. Reduced complexity: pre-built functions and higher-level abstractions simplify development by tackling authentication, error handling, and response parsing. Improved maintainability: auto-generation of updated SDK versions with each Atlas release ensures access to the latest functionalities, minimizing manual code changes. Enhanced reliability: Tailored API models promote code reliability and catch potential errors at compile time. Our in-house adoption experience was a valuable proving ground, influencing the SDK’s development and uncovering key optimization points. Today, the SDK is a cornerstone within our DevOps ecosystem, accelerating the development of downstream DevOps tools. This successful transition proved the value of code generation for developer productivity and code quality. It also opened up the possibility of generating SDKs for more programming languages in the future. The Atlas Go SDK can be a valuable asset for Go developers who build solutions on Atlas. To get started with the SDK, see our Docs Page . We also welcome community contributions, so visit our Contributing Guidelines for more details. We invite you to try it , and we would love to hear your feedback. Go build with the new Atlas Go SDK today!
What’s New From MongoDB at Microsoft Build 2024
This week, thousands of engineers, database administrators, and developers are gathering in Seattle for Microsoft Build , Microsoft’s annual developer event. In addition to being on site for meetings and learning sessions, MongoDB is excited to showcase our latest innovations for building generative AI apps and more. First, we’re happy to announce that MongoDB now offers dedicated Search Nodes on Microsoft Azure . We offer both compute-optimized nodes for text or application search workloads, and memory-optimized nodes for vector, semantic search, or gen AI workloads. Search Nodes enhance performance and availability through workload isolation while reducing architectural complexity. The availability of Search Nodes on Azure is the latest example of how the partnership between MongoDB and Microsoft helps organizations of all sizes boost developer productivity and build modern applications faster. Keep reading for more on how MongoDB’s capabilities and integrations with Microsoft are helping customers create, innovate, and scale applications. Integrating services and technology to speed AI development The last year of AI innovation set a clear imperative for every organization—to meet customer expectations, they need to modernize their applications. However, many companies aren’t sure where to start with AI, so MongoDB recently announced the launch of the MongoDB AI Application Program (MAAP) alongside industry-leading AI partners. MAAP will provide customers with strategic advisory, professional services, and an integrated end-to-end technology stack from MongoDB and key partners like Microsoft. We’ve also made several technology announcements to enable building gen AI applications, including native support for MongoDB Atlas Vector Search in Microsoft Semantic Kernel , and a dedicated MongoDB Atlas integration for OpenAI’s ChatGPT Plugin . With the new integration, developers can seamlessly and securely enhance the power of large language models from OpenAI, Azure OpenAI, and Hugging Face with proprietary data in Atlas to build powerful retrieval-augmented generation applications using Python or C#. Developing faster with intelligent tools and frameworks In addition to helping developers build powerful gen AI applications through services like Atlas Vector Search, we’ve been working to enhance developer productivity, making it easier than ever to build applications with MongoDB. For example, we’ve introduced intelligent features to first-party tools like MongoDB Compass and Atlas Charts that support natural language. We also recently announced AI-powered SQL query conversions in Relational Migrator to help teams easily move their workloads to MongoDB. MongoDB is expanding the use of AI to enhance its integration with the world’s most popular integrated development environment, Visual Studio Code. We’re excited to announce the MongoDB Participant for the Github CoPilot chat experience, designed to empower developers to generate queries from natural language, understand collection schemas, and instantly access MongoDB documentation. Sign up for the private preview here . MongoDB also supports a variety of programming frameworks to improve productivity and accelerate application development—while ensuring data consistency and quality. Now generally available, the MongoDB Provider for Entity Framework Core (EF Core), encourages C# developers to build their next project on MongoDB. This new offering helps C# developers—39% of whom use EF Core—unlock the full power of MongoDB using the EF Core APIs and design patterns they already know and love. Streamlining comprehensive data analysis For years, MongoDB and Microsoft have facilitated the large-scale analysis of application-generated data to aid business development. Tools like Microsoft Power BI provide a comprehensive view of business intelligence data for developers and analysts with complex data estates using relational databases alongside MongoDB. MongoDB’s Power BI Connector for Atlas —previously supporting Import Mode—now also supports DirectQuery, which we announced a few weeks ago at MongoDB.local NYC . This allows for real-time querying of MongoDB data and is ideal for large datasets. To further enable customers working in the Microsoft ecosystem, we’ve recently made Atlas Data Federation and Atlas Online Archive generally available on Azure . These services enable users to query, transform, and create views across multiple Atlas databases and Azure cloud storage solutions, like Blob Storage and Data Lake Storage Gen2, simplifying data management and archiving within the Azure ecosystem. Building the future together MongoDB's partnership with Microsoft has made developing modern applications faster and easier. We're thrilled to announce these new capabilities at Microsoft Build 2024 , and look forward to helping our joint customers build amazing things together this year. “MongoDB’s relationship with Microsoft has never been better, and with these latest integrations, our momentum continues to grow,” said Alan Chhabra, MongoDB’s EVP of Worldwide Partners. “Already, many of the largest enterprises and most advanced startups in the world run MongoDB Atlas on Microsoft Azure. These latest innovations will empower even more of our customers to take full advantage of their data to build truly transformational generative AI-powered applications.” MongoDB’s partnership with Microsoft sets projects up for success today and tomorrow by delivering robust, integrated solutions that cater to developers' needs. MongoDB and Microsoft are pushing the boundaries for innovation and service for the developer community. To learn more about our recent announcements and for the latest product updates, visit our What’s New page. And head to our campaign page to learn more about how to build smarter and develop faster with MongoDB Atlas on Microsoft Azure.
Payments Modernization and the Role of the Operational Data Layer
To stay relevant and competitive, payment solution providers must enhance their payment processes to adapt to changing customer expectations, regulatory demands, and advancing technologies. The imperative for modernization is clear: payment systems must become faster, more secure, and seamlessly integrated across platforms. Driven by multiple factors—real-time payments, regulatory shifts like Payment Services Directive 2 (PSD2), heightened customer expectations, the power of open banking, and the disruptive force of fintech startups—the need for payment modernization has never been more pressing. But transformation is not without its challenges. Complex systems, industry reliance on outdated technology, high upgrade costs, and technical debt all pose formidable obstacles. This article will explore modernization approaches and how MongoDB helps smooth transformations. Approaches to modernization As businesses work to modernize their payment systems, they need to overcome the complexities inherent in updating legacy systems. Forward-thinking organizations embrace innovative strategies to streamline their operations, enhance scalability, and facilitate agile responses to evolving market demands. Two such approaches gaining prominence in the realm of payment system modernization are domain-driven design and microservices architecture : Domain-driven design: This approach focuses on a business's core operations to develop scalable and easier-to-manage systems. Domain-driven design ensures that technology serves strategic business goals by aligning system development with business needs. At its core, this approach seeks to break down complex business domains into manageable components, or "domains," each representing a distinct area of business functionality. Microservices architecture: Unlike traditional monolithic architectures, characterized by tightly coupled and interdependent components, a microservices architecture decomposes applications into a collection of loosely coupled services, each of which is responsible for a specific business function or capability. It introduces more flexibility and allows for quicker updates, facilitating agile responses to changing business requirements. Discover how Wells Fargo launched their next-generation card payments by building an operational data store with MongoDB . Modernizing with an operational data layer In the payments modernization process, the significance of an operational data layer (ODL) cannot be overstated. An ODL is an architectural pattern that centrally integrates and organizes siloed enterprise data, making it available to consuming applications. The simplest representation of this pattern looks something like the sample reference architecture below. Figure 1: Operational Data Layer structure An ODL is deployed in front of legacy systems to enable new business initiatives and to meet new requirements that the existing architecture can’t handle—without the difficulty and risk of fully replacing legacy systems. It can reduce the workload on source systems, improve availability, reduce end-user response times, combine data from multiple systems into a single repository, serve as a foundation for re-architecting a monolithic application into a suite of microservices, and more. The ODL becomes a system of innovation, allowing the business to take an iterative approach to digital transformation. Here's why an ODL is considered ideal for payment operations: Unified data management: Payment systems involve handling a vast amount of diverse data, including transaction details, customer information, and regulatory compliance data. An ODL provides a centralized repository for storing and managing this data, eliminating silos and ensuring data integrity. Real-time processing: An ODL enables real-time processing of transactions, allowing businesses to handle high numbers of transactions swiftly and efficiently. This capability is essential for meeting customer expectations for instant payments and facilitating seamless transactions across various channels. Scalability and flexibility: Payment systems must accommodate fluctuating transaction volumes and evolving business needs. An ODL offers scalability and flexibility, allowing businesses to scale their infrastructure as demand grows. Enhanced security: An ODL incorporates robust security features —such as encryption, access controls, and auditing capabilities—to safeguard data integrity and confidentiality. By centralizing security measures within the ODL, businesses can ensure compliance with regulatory requirements and mitigate security risks effectively. Support for payments data monetization: Payment systems generate a wealth of data that can provide valuable insights into customer behavior, transaction trends, and business performance. An ODL facilitates real-time analytics and reporting by providing a unified platform for collecting, storing, and analyzing this data. Transform with MongoDB MongoDB’s fundamental technology principles ensure companies can reap the advantages of microservices and domain-driven design—specifically, our flexible data model and built-in redundancy, automation, and scalability. Indeed, the document model is tailor-made for the intricacies of payment data, ensuring adaptability and scalability as market demands evolve. Here’s how MongoDB helps with domain-driven design and microservice implementation to adopt industry best practices: Ease of use: MongoDB’s document model makes it simple to model or remodel data to fit the needs of payment applications. Documents are a natural way of describing data. They present a single data structure, with related data embedded as sub-documents and arrays, making it simpler and faster for developers to model how data in the application will be mapped to data stored in the database. In addition, MongoDB guarantees the multi-record ACID transactional semantics that developers are familiar with, making it easier to reason about data. Flexibility: MongoDB’s dynamic schema is ideal for handling the requirements of microservices and a domain-driven design. Domain-driven design emphasizes modeling the domain to reflect the business requirements, which may evolve over time. MongoDB's flexible schema allows you to store domain objects as documents without rigid schema constraints, facilitating agile development and evolution of the domain model. Speed: Using MongoDB for an ODL means you can get better performance when accessing data, and write less code to do so. A document is a single place for the database to read and write data for an entity. This locality of data ensures the complete document can be accessed in a single database operation that avoids the need internally to pull data from many different tables and rows. Data access and microservice-based APIs: MongoDB integrates seamlessly with modern technologies and frameworks commonly used in microservices architectures. MongoDB's flexible data model and ability to handle various data types, including structured and unstructured data, is a great fit for orchestrating your open API ecosystem to make data flow between banks, third parties, and consumers possible. Scalability: Even if an ODL starts at a small scale, you need to be prepared for growth as new source systems are integrated, adding data volume, and new consuming systems are developed, increasing workload. MongoDB provides horizontal scale-out on low-cost, commodity hardware or cloud infrastructure using sharding to meet the needs of an ODL with large data sets and high throughput requirements. High availability: Microservices architectures require high availability to ensure that individual services remain accessible even in the event of failures. MongoDB provides built-in replication and failover capabilities, ensuring data availability and minimal downtime in case of server failures. Payment modernization is not merely a trend but a strategic imperative. By embracing modern payment solutions and leveraging the power of an ODL with MongoDB, organizations can unlock new growth opportunities, enhance operational efficiency, and deliver superior customer experiences. Learn how to build an operational data layer with MongoDB using this Payments Modernization Solution Accelerator . Learn more about how MongoDB is powering industries on our solution library .
How the NFSA is Using MongoDB Atlas and AI to Make Aussie Culture Accessible
Where can you find everything from facts about Kylie Minogue, to more than 6,000 Australian home movies, to a 60s pop group playing a song with a drum-playing kangaroo ? The NFSA! Founded in 1935, the National Film and Sound Archive of Australia (NFSA) is one of the oldest archives of its kind in the world. It is tasked with collecting, preserving, and sharing Australia’s audiovisual culture. According to its website, the NFSA “represents not only [Australia’s] technical and artistic achievements, but also our stories, obsessions and myths; our triumphs and sorrows; who we were, are, and want to be.” The NFSA’s collection includes petabytes of audiovisual data—including broadcast-quality news footage, TV shows, and movies, high-resolution photographs, radio shows, and video games—plus millions of physical and contextual items like costumes, scripts, props, photographs, and promotional materials, all tucked away in a warehouse. “Today, we have eight petabytes of data, and our data is growing from one to two petabytes each year,” said Shahab Qamar, software engineering manager at NFSA. Making this wealth of data easily accessible to users across Australia (not to mention all over the world) has led to a number of challenges, which is where MongoDB Atlas—which helps developers simplify and accelerate building with data—comes in. Don’t change (but apply a few updates) Because of its broad appeal, the NFSA's collection website alone receives an average of 100,000 visitors each month. When Qamar joined the NFSA in 2020, he saw an opportunity to improve the organization’s web platform. His aim was to ensure the best possible experience for the site’s high number of daily visitors, which had begun to plateau. This included a website refresh, as well as addressing technical issues related to handling site traffic, due to the site being hosted on on-premises servers. The site also wasn’t “optimized for Google Analytics,” said Qamar. In fact, the NFSA website was invisible to Google and other search engines, so he knew it was time for a significant update, which also presented an opportunity to set up strong data foundations to build deeper capabilities down the line. But first, Qamar and team needed to find a setup that could serve the needs of the NFSA and Australia’s 26 million residents more robustly than their previous solution. Specifically, Qamar said, the NFSA was looking for a fully managed database that could also implement search at scale, as well as a system that his small team of five could easily manage. It also needed to ensure high levels of resiliency and the ability to work with more than one cloud provider. The previous NFSA site also didn’t support content delivery networks , he added. MongoDB Atlas supported all of the use cases the NFSA was looking for, Qamar said, including the ability to support multi-cloud hosting. And because Atlas is fully managed, it would readily meet the NFSA's requirements. In July 2023, after months of development, the new and greatly improved NFSA website was launched. The redesign was immediately impactful: Since the NFSA’s redesigned site was launched, the number of users visiting the collection search website has gone up 200%, and content requests—which the NFSA access team responds to on a case-by-case basis—have gone up 16%. (Getting search) back in black While the previous version of the NFSA site included search, the prior functionality was prone to crashing, and the quality of the results was often poor, Qamar said. For example, search results were delivered alphabetically rather than based on relevance, and the previous search didn’t support fine-tuning of relevance based on matches in specific fields. So, as part of its site redesign, the NFSA was looking to add full text search, relevance-based search results, faceting, and pagination. MongoDB Atlas Search —which integrates the database, search engine, and sync mechanism into a single, unified, fully managed platform—ticked all of those boxes. A search results page on the NFSA website Indeed, the NFSA compared search results from its old site to its new MongoDB Atlas site and “found that MongoDB Atlas-based searches were more relevant and targeted,” Qamar said. Previously, configuring site search required manual coding and meant downtime for the site, he noted. “The whole setup wasn’t very developer friendly and, therefore, a barrier to working efficiently with search configuration and fine-tuning,” Qamar said. In comparison, MongoDB Atlas allowed for simple configuration and fine-tuning of the NFSA's search requirements. The NFSA has also been using MongoDB Atlas Charts . Charts help the NFSA easily visualize its collection by custom grouping (like production year or genre), as well as helping the NFSA see which items are most popular with users. “Charts have helped us understand how our collection is growing and evolving over time,” Qamar said. NFSA’s use of MongoDB Charts Can’t get you (AI) out of my head Now, the NFSA—inspired by Qamar’s own training in machine learning and the broad interest in all things AI—is exploring how it can use Atlas Vector Search and generative AI tools to allow users to explore content buried in the NFSA collection. One example cited is putting transcriptions of audiovisual files in NFSA’s collection into a vector database for retrieval-augmented generation (RAG). The NFSA has approximately 27 years worth—meaning, it would take 27 years to play it all back—of material to transcribe, and is currently developing a model to accurately capture the Australian dialect so the work is transcribed correctly. Ultimately, the NFSA is interested in building a RAG-powered AI bot to provide historically and contextually accurate information about work in the NFSA’s archive. The NFSA is also exploring how it can use RAG to deliver accurate, conversation-like search results without training large language models itself, and whether it can leverage AI to help restore some of the older videos in its collection. Qamar and team are also interested in vectorizing audio-visual material for semantic analysis and genre-based classification of collection material at scale, he said. “Historically, we’ve been very metadata-driven and keyword-driven, and I think that’s a missed opportunity. Because when we talk about what an archive does, we archive stories,” Qamar said of the possibilities offered by vectors. “An example I use is, what if the world ended tomorrow? And what if aliens came to Earth and only saw our metadata, what image of Australia would they see? Is that a true image of what Australia is really like?” Qamar said. “How content is described is important, but content’s imagery, the people in it, and the audio and words being spoken are really important. Full-text search can take you somewhere along the way, but vector search allows you to look things up in a semantic manner. So it’s more about ideas and concepts than very specific keywords,” he said. If you’re interested in learning how MongoDB helps accelerate and simplify time-to-mission for federal, state, and local governments, defense agencies, education, and across the public sector, check out MongoDB for Public Sector . Check out MongoDB Atlas Vector Search to learn more about how Vector Search helps organizations like the NFSA build applications powered by semantic search and gen AI. *Note that this story’s subheads come from Australian song titles!
Announcing DirectQuery Support for the MongoDB Atlas Connector for Power BI
Last year, we introduced the MongoDB Atlas Power BI Connector , a certified solution that has transformed how businesses gain real-time insights from their MongoDB Atlas data using their familiar Microsoft Power BI interface. Today, we’re excited to announce a significant enhancement to this integration: the introduction of DirectQuery support. DirectQuery mode provides a direct connection to your MongoDB Atlas database, allowing Power BI to query data in real-time. This means that your Power BI visualizations and reports will always reflect the latest data without importing and storing data within Power BI. This is especially beneficial for analyzing large datasets where up-to-date information is crucial, ensuring decisions are made efficiently without losing performance due to repetitive data imports and storage complexities. How DirectQuery in MongoDB Atlas Power BI Connector works: The Power BI Connector is supported through MongoDB’s Atlas SQL Interface , which is easily enabled from the Atlas console. Atlas SQL, powered by Atlas Data Federation , allows you to integrate data across sources and apply transformations directly, enhancing your analytics. Once enabled, you’ll receive a SQL Endpoint or URL to input into your MongoDB Atlas SQL Connection Dialog within Power BI Desktop. Here, you can choose between two connectivity modes: Import or DirectQuery. Once connected through DirectQuery, Query folding takes place with Power Query , which is how data retrieval and transformation of source data is optimized. You can also achieve data transformation using a SQL Statement, either with the SQL Statement option in the Atlas SQL Interface or within the M Code script accessed via the Power Query Advanced Editor. After your data is transformed and ready for analysis, start building reports with your Atlas data within the Power BI Desktop! Then, simply save, publish, and distribute within the Power BI online app, which is now part of the Microsoft Fabric platform. Watch our comprehensive tutorial below covering how to connect your Atlas data to Power BI , control SQL schemas in Atlas, and use DirectQuery to gain real-time access to your data for business insights. Power BI Connector for MongoDB Atlas is a Microsoft-certified solution. It not only supports the advanced capabilities of DirectQuery but also continues to offer Import Mode for scenarios where data volume is manageable and detailed data modeling is preferred. Whether you’re analyzing real-time data streams or creating comprehensive reports, the Atlas Power BI Connector adapts to your needs, ensuring your business leverages the full power of MongoDB Atlas. DirectQuery Support is available now and can be accessed by updating your existing MongoDB Atlas Power BI Connector or downloading it here . Start transforming your data analysis and making more informed decisions with real-time Atlas data. Log in and activate the Atlas SQL Interface to try out the Atlas Power BI Connector ! If you are new to Atlas or Power BI, get started for free today on Azure Marketplace or Power BI Desktop .
The Developers' Developers: Two Australian Developers Share Their Connections to Customers
The world’s 28 million software developers are writing the foundations of our future, propelling innovation for their organizations through lines of code by creating game-changing new apps. Indeed, the US Bureau of Labor Statistics predicts that between 2022 and 2032, the number of software developers, quality assurance analysts, and testers will grow 24%, “much faster than the average for all occupations.” Fueling this innovative workforce is another group of developers, the people working behind the scenes to build the tools, technologies and platforms that other developers need to be successful: the developers’ developers. Many developers at MongoDB—which after all was built by developers for developers, and is beloved by enterprises and startups alike—fall into this camp. To learn more about what makes these developers tick, we talked with two Australia-based senior software engineers at MongoDB who love to code for their peers. For Lavender Chan and Angus Lee, there’s nothing like seeing the ripple effect of the code they have been working on and the impact it has on their customers. What’s more, the opportunity to be a “developer’s developer” has allowed Chan and Lee to find a space for deep technical work while thriving in an autonomous environment. At MongoDB, we believe developers will build the future. First, can you share more about your roles and what you’re working on? Lavender Chan (LC): I work on the Relational Migrator tool, which allows developers to migrate SQL data onto MongoDB. I joined the company two and a half years ago, and have been part of the Sydney technology scene for the last 10 years. The appeal of joining MongoDB was that it’s a large global company, but in the engineering team, you are able to have a big impact and a lot of autonomy. Relational Migrator was a greenfield project, and our team has been able to take the original product idea built out of the US and run with it. I’m a full stack developer and have touched on every feature of the tool. A lot of the engineers were able to contribute and work on new ideas. There’s also a strong emphasis on culture here which practically means a lot of the people I work with are excited to be here and passionate about their roles. Angus Lee (AL): I work in the MongoDB Charts team in Australia and think our team is a sweet spot for developers. I’ve interned for other tech companies and started my career at MongoDB. Since then I’ve been given responsibilities where I can create a lot of impact. My role at MongoDB in Sydney has also given me great opportunities to connect directly to the developers we are creating products for in a way that pushes my work to a higher level. Your roles are focused on products targeted to other developers. How does developing for developers affect your approach to your work? LC: In our roles we are creating directly for other developers, so the work that I am doing is deeply technical and specific. As Migrator is a newer product, we are able to interact directly with our customers—other developers—and often a lot of their questions are quite complex and specific, which means I go on a learning journey in debunking and fixing their problems. AL: We have a strong team culture in that as developers we want to be our own users. That means we want to use other MongoDB team products, and they use ours, so we can better identify pain points and issues for our customers. There’s a term that developers use called “dogfooding” that really sums up how we think on this, which basically means to use your own product. It means for me that I think about writing clean code to help any other developers extend on this, and how effective what I do will be for the user. What I’ve also learnt is how our product helps other products thrive. We should have done all the hard work to transform data and show it through data visualizsation tools so it’s easy for the customer. Can you tell us more about this connection to customers and how MongoDB empowers developers? LC: When Relational Migrator was released as a general product, I went to MongoDB World to work at the booth, and I talked to the developers and customers using the platform. As an engineer, it was an amazing experience and opportunity to see how it was being used and what else we could be doing. This connection of engineers with customers, as well as the ability to speak to them regularly in my role, is unique. In other companies I would need to go through support teams, to go through someone else just to push out a bug fix. Our team is very customer focused, so we can prioritizse features that our customers want. AL: One of the best moments for me at MongoDB was when I went to MongoDB World and I sat down with a customer to talk through a feature of Charts. It was a pivotal moment to see the improvements it makes for the businesses that use it, and the impact it generates for their customers beyond that. I could sit back and see the ripple effect of the code I’m writing. There is also a great feedback engine where our users can submit ideas and other users can vote for that feature. The product managers pick from these and push out features that are directly relevant to the developers using it. We really connect through our aim to create an open forum for developers and customers to provide feedback and suggest ideas. Developers are problem solvers. As part of the MongoDB Love Your Developers campaign, we believe in championing the voice of developers and giving them the freedom to experiment and innovate. How do you see this in action? LC: In other places, I was a small cog in a massive system. At MongoDB, I really have an impact and can see directly how my work translates to our final product. In Sydney, we’re a satellite office, but it’s indicative of our company culture that there is huge trust placed in these teams. We’re given high impact projects and can run with them, which means I’ve been able to watch the Relational Migrator product go from a tiny product used by only a few customers, to one that is now generally available. Not many engineers get to work for a well-established, large company and still have the opportunity to work on and release products like a startup. There is a strong global interest in AI-driven innovations. How have your connections to customers led to innovations in this area? AL: We’ve been able to take an idea for a new AI feature, Natural Language Charts , and take it from concept to being released as a feature at MongoDB.local in London. We could see from our conversations with customers, as well as broader industry trends, that there was strong interest in new AI features, so we were able to prioritize it for Charts. We started with nothing and were given the freedom to research how this feature could work using AI, create a new proof of concept, and from there we were able to push it out into a feature which was a really proud moment. Having this agility and flexibility to prioritize something new is exactly what we want to provide to our customers. I never feel like I'm just churning out code. We are connected to the work and to our customers. MongoDB is built by developers, for developers. Become part of the team changing the way the world works with data!
Empowering Aspiring Developers in Africa: The MongoDB, MyTechDev Partnership
It's been nearly a year since the announcement of the partnership between MongoDB University and MyTechDev (Dev-Net), marking a significant milestone in empowering African developers with practical coding skills and enterprise technology pathways. This partnership aims to certify 500 people in Nigeria, South Africa, Kenya, and Egypt, aligning with the rising demand for skilled software developers globally. “At MongoDB, we love developers and are pleased to provide free, on-demand educational content for new learners and professional developers who want to expand their existing skill sets on the learning platform of their choice,” said Raghu Viswanathan, Vice President, Education, Documentation, and Academia at MongoDB. One of the remarkable outcomes of this collaboration is showcased in a recent video from MyTechDev (Dev-Net), where students share their experiences with MongoDB. Through hands-on learning facilitated by MyTechDev (Dev-Net) and free on-demand courses on MongoDB University , these students not only gained technical expertise but also found a supportive community that encouraged them through challenges, paving the way for future career aspirations and entrepreneurial ventures. The MyTechDev (Dev-Net) students' testimonials reflect the impact of accessible education and industry partnerships in fostering innovation and career opportunities in technology. By equipping individuals with in-demand skills like MongoDB proficiency, this collaboration addresses the skills gap. It fuels the ambitions of aspiring developers across Africa, promising a brighter future for the tech ecosystem on the continent. Investing in education isn't just about boosting opportunities; it's about saving lives. A recent study in The Lancet Public Health says what many have long suspected: education significantly impacts health outcomes. Regardless of age, gender, or socio-economic status, every year of schooling reduces mortality by 2%. This finding is particularly pertinent in sub-Saharan Africa, home to the world's youngest population, 70% under 30, and a GDP lagging far behind. Many of these young people face dire circumstances, with poverty and lack of resources hindering their access to education. The MongoDB for Academia program provides free resources for students and educators to make the most of MongoDB. The program also offers students MongoDB Atlas credits and free certification through the GitHub Student Developer Pack. These benefits are available globally, allowing students to enter the workforce with industry-relevant skills and certifications. To learn more, students and educators can register for the MongoDB for Students or MongoDB for Educators programs.