Meet the 2024 MongoDB Community Champions!

MongoDB is excited to announce our new cohort of Community Champions!

MongoDB Community Champions comprise an inspirational global group of passionate, dedicated MongoDB advocates—including customers, partners, and inspiring community leaders. They demonstrate exceptional leadership in advancing the growth and knowledge of MongoDB’s brand and technology. The eighteen Community Champions this year represent a range of expertise and serve in a variety of professional and community roles.

For example, Zhiyang Su is a senior applied scientist specializing in search ranking. With extensive experience in natural language processing (NLP), deep learning, and high-performance systems, he excels in dialog system design and optimization. Passionate about knowledge sharing, he regularly writes technical blog posts about MongoDB, NLP, and product design.

Community Champions serve as the connective tissue between MongoDB and our community, keeping them informed about MongoDB’s latest developments and offerings. Community Champions also share their knowledge and experiences with others through a variety of media channels and event engagements.

“With my contributions, I’m helping developers to get the right thing done faster by boosting their productivity,” said Mark Paluch, Spring Data Engineer and 2024 Community Champion. “Close collaboration in the form of learning, discussing, and giving feedback is key to get there.

As members of this program, Champions gain a variety of experiences—including exclusive access to executives, product roadmaps, preview programs, an annual Champions Summit with product leaders—and relationships that grow their professional stature as MongoDB practitioners and help them be seen as leaders in the technology community.

“Building on our global Champions program, this impressive group allows us to highlight a new level of outstanding members,” said Chuck Freedman, Director of Advocacy and Enablement, Developer Relations at MongoDB. “Our team led a cross-company nomination, interview, and review process to welcome a range of qualified and inspiring individuals representing our customers, partners, and global community.”

Reflecting on this year’s selection process, Abirami Sukumaran, Developer Advocate and 2024 Community Champion, said: “I was impressed by the comprehensive nature of the interview. It wasn't just about checking boxes; it felt like a 360-degree assessment of my knowledge and enthusiasm for MongoDB Atlas, which made the entire process very positive. I am really thrilled to share my experience on this database program with enthralled developers around the globe.”

We are also currently accepting applications for the Community Creator program. The Creator program consists of community members who create and share content to help others learn and uplevel their MongoDB knowledge. Creators are given exclusive access to product sessions, priority access to content features, and swag. To learn more, please visit the MongoDB Community Creators page.

And now, without further ado, let’s meet the 2024 cohort of Community Champions!

For more, visit our MongoDB Community Champions page.

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Teach & Learn with MongoDB: Professor Abdussalam Alawini, University of Illinois at Urbana-Champaign

In this series of interviews, we talk to students and educators around the world who are using MongoDB to make their classes more engaging and relevant. By exploring their stories, we uncover how MongoDB’s innovative platform and resources are transforming educational landscapes and empowering the next generation of tech-savvy professionals. From creative teaching approaches to advanced classroom solutions, the MongoDB for Educators program can help you transform your classroom with cutting-edge technology and free resources. It can help you provide students with an interactive and dynamic learning environment that bridges the gap between theoretical knowledge and practical application. The program includes a variety of free resources for educators crafted by MongoDB experts to prepare learners with in-demand database skills and knowledge. Program participants have access to MongoDB Atlas credits, curriculum materials, certifications, and membership in a global community of educators from over 700 universities. From theory to practice: Hands-on MongoDB Teaching Professor Abdussalam Alawini is known for his creative use of MongoDB in his courses. He heavily uses MongoDB's free cluster to demonstrate MongoDB concepts during classes, and his students also use the free cluster for their projects, giving them hands-on experience with real-world applications. Currently, a Teaching Associate Professor at the University of Illinois Urbana-Champaign, Professor Alawini’s research interests span databases, applied machine learning, and education. He is particularly focused on applying machine learning methods to enhance classroom experiences and education. His work also includes developing next-generation data management systems, such as data provenance, citation, and scientific management systems. He recently received the U of I’s 2024 Campus Excellence in Undergraduate Education award, which highlights his commitment to teaching and the impact he’s had on his students. Professor Alawini is currently collaborating with colleagues on research to map how databases, data systems, data management, and related courses are taught in introductory computer science undergraduate courses worldwide. Professor Alawini’s story offers valuable insights for educators eager to enhance their teaching and prepare students for a tech-driven future. Check out how MongoDB Atlas has revolutionized his teaching by simplifying database deployment, management, and scaling, allowing students to focus more on learning MongoDB concepts. Tell us about your educational journey and what sparked your interest in databases. My educational journey began with a bachelor's degree in Computer Science from the University of Tripoli in 2002. I then spent over six years in the industry as a database administrator, lead software developer, and IT Manager. In 2011, I returned to academia and earned two master's degrees in Computer Science and Engineering and Technology Management from Portland State University, followed by a Ph.D. in Computer Science in 2016. Subsequently, I joined the University of Pennsylvania for a two-year postdoctoral training. My interest in databases was sparked during my time as a database administrator at PepsiCo, where I enjoyed maintaining the company's databases and building specialized reports to improve business operations. I was particularly fascinated by database systems’ ability to optimize queries and handle millions of concurrent user requests seamlessly. This experience led me to focus my doctoral studies on building data management systems for scientific applications. What courses are you currently teaching at the University of Illinois Urbana-Champaign? Currently, I teach Database Systems and Data Management in the Cloud courses at the University of Illinois Urbana-Champaign. In addition, I also teach a course to University High School students to introduce them to data management and database basics. My intention with teaching databases to high schoolers is to use data management as a gateway to lower entry barriers into computing fields for non-computer science students and to recruit underrepresented minorities to computing. What inspired you to start teaching MongoDB? I was inspired to start teaching MongoDB after seeing several surveys indicating that it is the most used database in web development and one of the leading document-oriented databases. MongoDB offers several unique features that set it apart from other databases, including the aggregation pipeline, which simplifies data processing and transformation. Additionally, MongoDB's flexible schema design allows for easier handling of unstructured data, and its horizontal scalability ensures robust performance as data volumes grow. These features make MongoDB an essential tool for modern web development, and I wanted to equip my students with the skills to leverage this powerful technology. How do you design your course content to effectively integrate MongoDB and engage students in practical learning? In all my data management courses, I focus on teaching students the concept of data models, including relational, document, key-value, and graph. In my Database Systems course, I teach MongoDB alongside SQL and Neo4J to highlight the unique features and capabilities of each data model. This comparative approach helps students appreciate the importance and applications of different databases, ultimately making them better data engineers. In my Data Management in the Cloud course, I emphasize the system's side of MongoDB, particularly its scalability. Understanding how MongoDB is built to handle large volumes of data efficiently provides students with practical insights into managing data in a cloud environment. To effectively integrate MongoDB and engage students in practical learning, I use a hybrid flipped-classroom approach. Students watch recorded lectures before class, allowing us to dedicate class time to working through examples together. Additionally, students form teams to work on various data management scenarios using a collaborative online assessment tool called PrairieLearn. This model fosters peer learning and collaboration, enhancing the overall educational experience. How has MongoDB supported you in enhancing your teaching methods and upskilling your students? I would like to sincerely thank MongoDB for Academia for the amazing support and material they provided to enhance my course design. The free courses offered at MongoDB University have significantly improved my course delivery, allowing me to provide more in-depth and practical knowledge to my students. I heavily use MongoDB's free cluster to demonstrate MongoDB concepts during classes, and my students also use the free cluster for their projects, which gives them hands-on experience with real-world applications. MongoDB Atlas has been a game-changer in my teaching methods. As a fully managed cloud database, it simplifies the process of deploying, managing, and scaling databases, allowing students to focus on learning and applying MongoDB concepts without getting bogged down by administrative tasks. The flexibility and reliability of MongoDB Atlas make it an invaluable tool for both educators and students in the field of data management. Could you elaborate on the key findings from your ITiCSE paper on students' experiences with MongoDB and how these insights can help other educators? In my ITiCSE paper, we conducted an in-depth analysis of students' submissions to MongoDB homework assignments to understand their learning experiences and challenges. The study revealed that as students use more advanced MongoDB operators, they tend to make more reference errors, indicating a need for a better conceptual understanding of these operators. Additionally, when students encounter new functionalities, such as the $group operator, they initially struggle but generally do not repeat the same mistakes in subsequent problems. These insights suggest that educators should allocate more time and effort to teaching advanced MongoDB concepts and provide additional support during the initial learning phases. By understanding these common difficulties, instructors can better tailor their teaching strategies to improve student outcomes and enhance their learning experience. What advice would you give to fellow educators who are considering implementing MongoDB in their own courses to ensure a successful and impactful experience for their students? Implementing MongoDB in your courses can be highly rewarding. Here’s some advice to ensure success: Foundation in Data Models: Teach MongoDB alongside other database types to highlight unique features and applications, making students better data engineers. Utilize MongoDB Resources: Leverage support from MongoDB for Academia, free courses from MongoDB University, and free clusters for hands-on projects. Practical Learning: Use MongoDB Atlas to simplify database management and focus on practical applications. Focus on Challenges: Allocate more time for advanced MongoDB concepts. Address common errors and use tools like PrairieLearn that capture students' interactions and learning progress to identify learning patterns and adjust instruction. Encourage Real-World Projects: Incorporate practical projects to enhance skills and relevance. Continuous Improvement: Gather feedback to iteratively improve course content and share successful strategies with peers. MongoDB is always evolving so make sure to stay tuned with their updates and new features. These steps will help create an engaging learning environment, preparing students for real-world data management. Apply to MongoDB for Educators program and explore free resources for educators crafted by MongoDB experts to prepare learners with in-demand database skills and knowledge.

July 10, 2024
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The Converged AI and Application Datastore for Insurance

In the inherently information-driven insurance industry, companies ingest, analyze, and process massive amounts of data, requiring extensive decision-making. To manage this, they rely on a myriad of technologies and IT support staff to keep operations running smoothly but often lack effectiveness due to their outdated nature. Artificial intelligence (AI) holds great promise for insurers by streamlining processes, enhancing decision-making, and improving customer experiences with significantly less time, resources, and staff compared with traditional IT systems. The convergence of AI and innovative application datastores is transforming how insurers work with data. In this post, we’ll look at how these elements are reshaping the insurance industry and offering greater potential for AI-powered applications, with MongoDB at the heart of the converged AI and application datastore. Scenario planning and flexible data layers One of the primary concerns for IT leaders and decision-makers in the insurance industry is making smart technology investments. The goal is to consolidate existing technology portfolios, which often include a variety of systems like SQL Server, Oracle, and IBM IMS. Consolidation helps reduce inventory and prepare for the future. But what does future-proofing really look like? Scenario planning is an effective strategy for future-proofing. This involves imagining different plausible futures and investing in the common elements that remain beneficial across all scenarios. For insurance companies, a crucial common thread is the data layer. By making data easier to work with, companies can ensure that their technology investments remain valuable regardless of how future scenarios unfold. MongoDB’s flexible developer data platform offers a distinct architectural advantage by making data easier to work with, regardless of the cloud vendor or AI application in use. This flexibility is vital for preparing for disruptive future scenarios, whether they involve regulatory changes, market shifts, or technological advancements. Watch now: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance The role of AI and data in insurance Generative AI is revolutionizing the insurance sector, offering new ways to manage and utilize data. According to Celent's 2023 Technology Insight and Strategy Survey, 33% of companies across different industries have AI projects in planning, 29% in development, and 19% in production (shown in Figure 1 below). This indicates a significant shift towards AI-driven solutions by insurers actively experimenting with gen AI. Figure 1: Celent Technology Insight and Strategy Survey 2023 However, there's tension between maintaining existing enterprise systems and innovating with AI. Insurance companies must balance keeping the lights on with investing in AI to meet the expectations of boards and stakeholders. The solution lies in integrating AI in a way that enhances operational efficiency without overwhelming existing systems. However, data challenges need to be addressed to achieve this, specifically around access to data. According to a Workday Global Survey , only 4% of respondents said their data is fully accessible, and 59% say their enterprise data is somewhat or completely siloed. Without a solid data foundation, insurers will struggle to achieve the benefits they are looking for from AI. Data architectures and unstructured data When adopting advanced technologies like AI and ML, which require data as the foundation, organizations often grapple with the challenge of integrating these innovations into legacy systems due to their inflexibility and resistance to modification. A robust data architecture is essential for future-proofing and consolidating technology investments. Insurance companies often deal with a vast amount of unstructured data, such as claim images and videos, which can be challenging to manage. By leveraging AI, specifically through vector search and large language models, companies can efficiently process and analyze this data. MongoDB is ideal for managing unstructured data due to its flexible, JSON-like document model, which accommodates a wide variety of data types and structures without requiring a predefined schema. Additionally, MongoDB’s flexibility enables insurers to integrate seamlessly with various technologies, making it a versatile and powerful solution for unstructured data management. For example, consider an insurance adjuster assessing damage from claim photos. Traditionally, this would require manually reviewing each image. With AI, the photos can be converted into vector embeddings and matched against a database of similar claims, drastically speeding up the process. This not only improves efficiency but also enhances the accuracy of assessments. The converged AI and application datastore with MongoDB Building a single view of data across various systems is a game-changer for the insurance industry. Data warehouses and data lakes have long provided single views of customer and claim data, but they often rely on historical data, which may be outdated. The next step is integrating real-time data with these views to make them more dynamic and actionable. A versatile database platform plays a crucial role in this integration. By consolidating data into a single, easily accessible view, insurance companies can ensure that various personas, from underwriters to data scientists, can interact with the data effectively. This integration allows for more responsive and informed decision-making, which is crucial for staying competitive in a rapidly evolving market. This can be achieved with a converged AI and application datastore, as shown in Figure 2 below. This is where operational data, analytics insights, and unstructured data become operationally ready for the applications that leverage AI. Figure 2: Converged AI and application datastore reference architecture The convergence of AI, data, and application datastores is reshaping the insurance industry. By making smart technology investments, leveraging AI to manage unstructured data, and building robust data architectures, insurance companies can future-proof their operations and embrace innovation. A versatile and flexible data platform provides the foundation for these advancements, enabling companies to make their data more accessible, actionable, and valuable. The MongoDB Atlas developer data platform puts powerful AI and analytics capabilities directly in the hands of developers and offers the capabilities to enrich applications by consolidating, ingesting, and acting on any data type instantly. Because MongoDB serves as the operational data store (ODS)—with its flexible document model—insurers can efficiently handle large volumes of data in real-time. By integrating MongoDB with AI/ML platforms, insurers can develop models trained on the most accurate and up-to-date data, thereby addressing the critical need for adaptability and agility in the face of evolving technologies. With built-in security controls across all data, whether managed in a customer environment or through MongoDB Atlas, a fully managed cloud service, MongoDB ensures robust security with features such as authentication (single sign-on and multi-factor authentication), role-based access controls, and comprehensive data encryption. These security measures act as a safeguard for sensitive data, mitigating the risk of unauthorized access from external parties and providing organizations with the confidence to embrace AI and ML technologies. If you would like to learn more about the convergence of AI and application datastores, visit the following resources: Video: The Converged AI and Application Datastore: How API's, AI & Data are Reshaping Insurance Paper: Innovation in Insurance with Artificial Intelligence The MongoDB Solutions Library is curated with tailored solutions to help developers kick-start their projects

July 18, 2024