Top 10 MongoDB Blogs of 2022

William To
December 19, 2022

2022 was a year of many milestones at MongoDB — from global events to enhancements to the MongoDB Atlas developer data platform, and everything in between. Our most popular blog posts reflected these achievements, covering announcements, events, and more, from MongoDB 6.0 to Queryable Encryption to our annual Partner of the Year awards.

Read on for a roundup of all our top blog posts from 2022.

10. Recognizing MongoDB's Partners of the Year

Partners are a big part of MongoDB’s success, helping our customers modernize their infrastructure with MongoDB, integrating MongoDB into their existing systems, and even selling Atlas on their marketplaces. Other partners also help adapt the MongoDB developer data platform to various sectors and niches, such as streaming data, ecommerce, and financial services.

Read Working Together: MongoDB’s Partner of the Year Awards to learn about a few of these organizations.

9. Syncing data with cluster-to-cluster sync

Data sync and backups are a key part of the Atlas developer data platform. Until recently, however, there was no easy way to sync data across clusters, especially if they were in different environments — think syncing a hybrid cluster with a cloud-native one.

This blog introduces cluster-to-cluster sync, which enables one-way synchronization between any two MongoDB clusters, regardless of type. The sync process is very flexible, includes real-time monitoring, and controls to pause, resume, or reverse synchronization at any time.

Read Keeping Data in Sync Anywhere with Cluster-to-Cluster Sync.

8. How MongoDB 6.0 improves time series data

Utilized in everything from IoT devices to ecommerce, time series data is an important use case for many industries. Since their release in MongoDB 5.0, time series collections have been continuously improved to help developers tackle a wider range of problems, including columnar compression for smaller storage footprints, densification for filling data gaps, and enhanced indexing on time series collections for better read performance.

Read MongoDB Announces New Time Series Capabilities Coming For MongoDB 6.0.

7. Reducing complexity with Apollo GraphQL and MongoDB Atlas

As a layer that unifies your cloud data, APIs, and services into a single, accessible schema (known as a graph), GraphQL brings a streamlined, monolithic approach to operations while retaining the benefits of a microservices architecture. This post discusses how the scalability, reliability, and usability of the MongoDB Atlas developer data platform make it an ideal data layer for any tech environment that utilizes a graph.

Read Building a Modern App Stack with Apollo GraphQL and MongoDB Atlas.

6. Improvements to change streams

Change streams enable applications to recognize data changes throughout their environment, simplifying the creation of event-driven applications and abilities like real-time personalization, notifications, and more.

This post explains the upgrades to change streams in depth, including the ability to retrieve the before and after states of documents to address more use cases; support for data definition language (DDL) operations (such as creating or dropping indexes with a single command) to simplify database management; and filtering notifications from aggregation frameworks for improved performance.

Read Change Streams in MongoDB 6.0 — Support Pre- and Post-Image Retrieval, DDL Operations, and more.

5. Breaking biases and getting more women into tech leadership

Even as representation in tech improves, only 26.7% of technologists are women — who leave the sector at a rate 45% higher than their male counterparts. For International Women’s Day, MongoDB senior product designer Ksenia Samokhvalova sat down with executives for a panel discussion on the root causes of (and potential solutions for) this issue. They discussed the differences between inclusivity and diversity, the hurdles for retention, how bias begins at a young age, and the importance of mentorship.

Read Breaking the Bias: How Can We Get More Women into Top-Level Tech.

4. Highlights from MongoDB World 2022

After three years, MongoDB World returned to New York City’s Javits Center from June 7-9 for three days of exploration, inspiration, and innovation. Readers were eager to get the scoop on everything they missed, from new product launches to workshops.

Day 1 kicked off the conference with keynotes announcing the latest features and outlining the concept of a developer data platform, an integrated set of data and application services with a unified developer experience. Day 2 included over 80 breakout sessions on topics ranging from diversity to data modeling to building with Rust. The last day featured a keynote from renowned inventor Ray Kurzweil, interactive competitions and challenges at the Builder’s Fest, and the “Chaos Presentation” from MongoDB CTO Mark Porter.

Read our recaps for day 1, day 2, and day 3 of MongoDB World. If you are interested in watching full sessions from MongoDB World, check out our playlist on YouTube.

3. Introducing MongoDB's Prometheus monitoring integration

A popular open source monitoring platform, Prometheus features a flexible query language (PromQL), a versatile data model that supports time series data, customizable alerting, a large, active user community, and consistent updates.

In this post, learn how the integration enables Prometheus to collect hardware and monitoring metrics from MongoDB and display them directly in the Prometheus UI or via Grafana dashboards. You can simplify monitoring with the Prometheus integration for MongoDB, removing the need to toggle between interfaces and keeping all your metrics in one place.

Read Introducing MongoDB’s Prometheus Monitoring Integration.

2. Queryable Encryption

For years, Queryable Encryption (QE) existed only as a theory: What if users could query fully-encrypted data, and only have to decrypt it once the results were returned? Given that data has traditionally been encrypted at rest or in transit — but not during the querying process — a feature like QE would add an additional layer of protection and remove a known vulnerability.

We were happy to see that readers were as excited as we were at the release of Queryable Encryption in preview. This announcement was only possible after years of research and partnerships with outside experts from Brown University, the University of Chicago, and a leading organization in the field. For more background on the evolution of Queryable Encryption, check out Wired’s article, A Long Awaited Defense Against Data Leaks May Have Just Arrived.

Now, you can run fast, rich queries on encrypted data at scale, keeping it secure throughout its lifecycle. Queryable Encryption also helps speed up app development because it is easy to use and set up, is compatible with MongoDB drivers, and supports strong key management and cryptography.

Read MongoDB Releases Queryable Encryption Preview.

1. 7 reasons to upgrade to MongoDB 6.0

The release of MongoDB 6.0 was big news this year. It brought improvements and new features in areas, like security, change streams, time series collections, operations, and more — making the developer data platform even easier to run, scale, and build with. As with other releases, MongoDB 6.0 removes data silos, eliminates complexity, and frees up teams to spend less time troubleshooting custom architectures — and more time creating apps and products.

Some highlights were the inclusion of Atlas Search facets to easily filter results, the creation of initial sync (via file copy) to quickly catch up new or slow nodes in your replica sets, and the addition of new operators to aggregation frameworks for faster analysis and deeper insights.

Read 7 Big Reasons to Upgrade to MongoDB 6.0.

We hope you had a great 2022, and that you enjoyed attending our events, reading our blogs, and using the MongoDB Atlas developer data platform. As always, you can sign up for a free (forever) cluster on Atlas.

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MongoDB Underpins Massive Digital-First Data Strategy Adoption Across Asia Pacific from Singapore

Members of MongoDB’s Association of Southeast Asian Nations (ASEAN) team get set for their first MongoDB Day in Singapore. Singapore is fast becoming a strategic hub for companies across Asia Pacific as they reorient around a digital-first strategy, according to Simon Eid, senior vice president, APAC, MongoDB. During the past year, MongoDB’s team in Singapore has expanded from five people to more than 50 people to support exponential growth and demand in the region. Speaking at the first MongoDB Day ever held in Singapore, which was sponsored by AWS, Eid explained that, since COVID-19 hit in March 2020, companies, senior management teams, and executive boards have started asking how quickly they could pivot to a digital-first strategy. “Retailers had to become experts at ecommerce, gaming and streaming companies had to deal with a surge of demand, pharmacies had to figure out how to get medications to their customers, and time was of the essence — speed was critical,” said Eid. MongoDB fastest for digital transformation Simon Eid, senior vice president, APAC, MongoDB. Eid said businesses across Asia Pacific quickly learned that the fastest way to build new digital capabilities was to use MongoDB. Developers could move incredibly fast and ship quickly. Product leaders realized they could get to market faster than the competition, and company executives realized they could seize new opportunities or respond to new threats. “Our whole ethos as a company is to make developers insanely productive, and our real opportunity for Asia Pacific is now how to help customers go to the next level supporting operational productivity with data management at the heart of everything they do,” he said. Simply the most secure Amit Samdarshi, product manager, Open Government Producst, explians how his team innovates. MongoDB has been downloaded 265 million times, ten times more than the number of developers in the world, which is testament to its growing global reputation. Locally in Singapore and across Asia Pacific, more and more customers are choosing MongoDB, and specifically Atlas, for its simplicity, flexibility, and robust security. “Regulators, financial institutions, and the Singapore government now recognize that MongoDB and Atlas provide the most secure data management system and service available in the market — you only have to look at Singapore’s FormSG within Open Government Products as a perfect example,” said Eid. Forms for public good Presentation at MongoDB Days Singapore Open Government Singapore has a dedicated team of 100 people working for FormSG, a department credited with revolutionizing government forms digitally with MongoDB Atlas on AWS. FormSG chose MongoDB because they required the highest level of security for data encryption as well as the confidence that they would not be impacted by downtime or performance issues with many users accessing forms simultaneously. “They describe themselves as “Forms for public good,” having touched the lives of millions of people during COVID-19, streamlining how members of the public engage with the multitude of government forms, and MongoDB is the backbone of this initiative,” said Eid. “We’re very proud of what they have achieved, held up as a shining example of data management excellence by the Singapore government now being showcased to other countries around the world,” he said. Poised for further growth From healthcare to social media analytics, digital banking and food delivery services, MongoDB is powering some of the most innovative growth companies to emerge across the region: A recent example is Greenline Synergy Co. Ltd (GLS) in Thailand using MongoDB Atlas to transform healthcare for the country’s leading hospital group, Bangkok Dusit Medical Services (BDMS). Also in Thailand, Computerology is unlocking the value of social media management and social listening via analytics. Indonesian FinTech Amar Bank is the pioneer of digital banking providing microloans to people who wouldn’t be able to get financial services from traditional banks. Woowa Brothers Vietnam introduced its food delivery app, BAEMIN, to local users in May 2019, and it scaled rapidly throughout the pandemic to become one of the leading food technology companies across the country. Woowa Brothers Vietnam introduced its food delivery app BAEMIN to local users in May 2019 and it scaled rapidly throughout the pandemic to become one of the leading food technology companies across the country. Another interesting story from the wider APAC region is TableCheck, the world’s premiere booking and guest platform. TableCheck's CTO Johnny Shields was also on site at the event and presented on how MongoDB has been a fit for TableCheck's developer workflow since they were founded in 2013. He explained that MongoDB allowed them to work with their data easily and quickly while building out their APIs and application. We'll be having a full blog on their use case coming soon. MongoDB continues to invest in its Asia Pacific operations with Singapore now firmly established as the regional ASEAN hub for customers and partners. “Singapore is a highly regulated, highly secure market, which gives customers and partners confidence when working through their digital transformation strategies and the vendor partners they require. We are excited to have partnered with AWS for our first MongoDB day, which reinforces our close alignment to cloud providers who are an integral part of our customer digital transformation plans,” Eid said. Learn more about MongoDB Atlas and try for free today .

December 15, 2022
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Retrieval Augmented Generation for Claim Processing: Combining MongoDB Atlas Vector Search and Large Language Models

Following up on our previous blog, AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases , we’ll pick up the conversation right where we left it. We discussed extensively how Atlas Vector Search can benefit the claim process in insurance and briefly covered Retrieval Augmented Generation (RAG) and Large Language Models (LLMs). MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More One of the biggest challenges for claim adjusters is pulling and aggregating information from disparate systems and diverse data formats. PDFs of policy guidelines might be stored in a content-sharing platform, customer information locked in a legacy CRM, and claim-related pictures and voice reports in yet another tool. All of this data is not just fragmented across siloed sources and hard to find but also in formats that have been historically nearly impossible to index with traditional methods. Over the years, insurance companies have accumulated terabytes of unstructured data in their data stores but have failed to capitalize on the possibility of accessing and leveraging it to uncover business insights, deliver better customer experiences, and streamline operations. Some of our customers even admit they’re not fully aware of all the data in their archives. There’s a tremendous opportunity to leverage this unstructured data to benefit the insurer and its customers. Our image search post covered part of the solution to these challenges, opening the door to working more easily with unstructured data. RAG takes it a step further, integrating Atlas Vector Search and LLMs, thus allowing insurers to go beyond the limitations of baseline foundational models, making them context-aware by feeding them proprietary data. Figure 1 shows how the interaction works in practice: through a chat prompt, we can ask questions to the system, and the LLM returns answers to the user and shows what references it used to retrieve the information contained in the response. Great! We’ve got a nice UI, but how can we build an RAG application? Let’s open the hood and see what’s in it! Figure 1: UI of the claim adjuster RAG-powered chatbot Architecture and flow Before we start building our application, we need to ensure that our data is easily accessible and in one secure place. Operational Data Layers (ODLs) are the recommended pattern for wrangling data to create single views. This post walks the reader through the process of modernizing insurance data models with Relational Migrator, helping insurers migrate off legacy systems to create ODLs. Once the data is organized in our MongoDB collections and ready to be consumed, we can start architecting our solution. Building upon the schema developed in the image search post , we augment our documents by adding a few fields that will allow adjusters to ask more complex questions about the data and solve harder business challenges, such as resolving a claim in a fraction of the time with increased accuracy. Figure 2 shows the resulting document with two highlighted fields, “claimDescription” and its vector representation, “claimDescriptionEmbedding” . We can now create a Vector Search index on this array, a key step to facilitate retrieving the information fed to the LLM. Figure 2: document schema of the claim collection, the highlighted fields are used to retrieve the data that will be passed as context to the LLM Having prepared our data, building the RAG interaction is straightforward; refer to this GitHub repository for the implementation details. Here, we’ll just discuss the high-level architecture and the data flow, as shown in Figure 3 below: The user enters the prompt, a question in natural language. The prompt is vectorized and sent to Atlas Vector Search; similar documents are retrieved. The prompt and the retrieved documents are passed to the LLM as context. The LLM produces an answer to the user (in natural language), considering the context and the prompt. Figure 3: RAG architecture and interaction flow It is important to note how the semantics of the question are preserved throughout the different steps. The reference to “adverse weather” related accidents in the prompt is captured and passed to Atlas Vector Search, which surfaces claim documents whose claim description relates to similar concepts (e.g., rain) without needing to mention them explicitly. Finally, the LLM consumes the relevant documents to produce a context-aware question referencing rain, hail, and fire, as we’d expect based on the user's initial question. So what? To sum it all up, what’s the benefit of combining Atlas Vector Search and LLMs in a Claim Processing RAG application? Speed and accuracy: Having the data centrally organized and ready to be consumed by LLMs, adjusters can find all the necessary information in a fraction of the time. Flexibility: LLMs can answer a wide spectrum of questions, meaning applications require less upfront system design. There is no need to build custom APIs for each piece of information you’re trying to retrieve; just ask the LLM to do it for you. Natural interaction: Applications can be interrogated in plain English without programming skills or system training. Data accessibility: Insurers can finally leverage and explore unstructured data that was previously hard to access. Not just claim processing The same data model and architecture can serve additional personas and use cases within the organization: Customer Service: Operators can quickly pull customer data and answer complex questions without navigating different systems. For example, “Summarize this customer's past interactions,” “What coverages does this customer have?” or “What coverages can I recommend to this customer?” Customer self-service: Simplify your members’ experience by enabling them to ask questions themselves. For example, “My apartment is flooded. Am I covered?” or “How long do windshield repairs take on average?” Underwriting: Underwriters can quickly aggregate and summarize information, providing quotes in a fraction of the time. For example, “Summarize this customer claim history.” “I Am renewing a customer policy. What are the customer's current coverages? Pull everything related to the policy entity/customer. I need to get baseline info. Find relevant underwriting guidelines.” If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: RAG for claim processing GitHub repository From Relational Databases to AI: An Insurance Data Modernization Journey Modernize your insurance data models with MongoDB and Relational Migrator

April 18, 2024