5 Ways to Learn MongoDB
MongoDB offers a variety of ways for users to gain product knowledge, get certified, and advance their careers. In this guide, we'll provide an overview of the top five ways to get MongoDB training, resources, and certifications. #1: MongoDB University The best place to go to get MongoDB-certified and improve your technical skills is MongoDB University . At our last MongoDB.local London event, we announced the launch of a brand new, enhanced university experience, with new courses and features, and a seamless path to MongoDB certification to help you take your skills and career to the next level. MongoDB University offers courses, learning paths, and certifications in a variety of content types and programming languages. Some of the key features that MongoDB University offers are: Hands-on labs and quizzes Bite-sized video lectures Badges for certifications earned Study guides and materials Getting certified from MongoDB University is a great way to start your developer journey. Our education offerings also include benefits for students and educators . #2: MongoDB Developer Center For continued self-paced learning, the MongoDB Developer Center is the place to go. The Developer Center houses the latest MongoDB tutorials, videos, community forums , and code examples in your preferred languages and tools. The MongoDB Developer Center is a global community of more than seven million developers. Within the Developer Center, you can code in different languages, get access to integrate technologies you already use, and start building with MongoDB products, including: MongoDB, the original NoSQL database MongoDB Atlas , the cloud document database as a service and the easiest way to deploy, operate, and scale MongoDB MongoDB Atlas App Services , the easy way to get new apps into the hands of your users faster #3: Instructor-led training As an IT leader, you can help your team succeed with MongoDB instructor-led training taught live by expert teachers and consultants. With MongoDB’s instructor-led training offering, you can access courses aimed at various roles. Our Developer and Operations learning paths cover fundamental skills needed to build and manage critical MongoDB deployments. Beyond that, our specialty courses help learners master their skills and explore advanced MongoDB features and products. You can also modify how you want to learn. MongoDB offers public remote courses, which are perfect for individuals or teams who want to send a few learners at a time. If your goal is to upskill your entire team with MongoDB, our courses can be delivered privately, both onsite or remotely. Instructor-led training also provides the opportunity for Q&A, providing answers to your specific questions. #4: Resources Beyond formal training programs, MongoDB is committed to providing thought leadership resources for those looking to dive deeper and learn more about MongoDB and database technologies in general. Our website offers an active blog with ongoing thought leadership and how-to articles, along with additional coding documentation , guides, and drivers. You can also check out the MongoDB Podcast for information about new and emerging technology, MongoDB products, and best practices. #5: Events You can also engage with MongoDB experts at our many events, including MongoDB World, our annual conference for developers and other IT leaders. After MongoDB World, we take our show on the road with MongoDB .local events across the globe. These events give you the opportunity to learn in a hands-on fashion and meet other MongoDB users. MongoDB also hosts MongoDB days in various global regions, focusing on developer workshops and leveling up skills. Beyond that, you can keep up with our webinars and other learning opportunities through our Events page. Build your own MongoDB story Of course, many people like to learn by doing. To get started using MongoDB Atlas in minutes, register for free .
Hydrus Helps Companies Improve ESG Performance
More organizations are embracing workforce diversity, environmental sustainability, and responsible corporate governance in an effort to improve their Environmental, Social, and Governance (ESG) performance. As investors increasingly favor ESG in their portfolios, organizations are under greater pressure to capture, store, and verify ESG metrics. San Francisco-based startup, Hydrus, is helping companies make ESG data more usable and actionable. The platform Hydrus, a MongoDB for Startups program member, is a software platform that enables enterprises to collect, store, report, and act on their environmental, social, and governance data. ESG data includes things like: How a company safeguards the environment Its energy consumption and how it impacts climate change How it manages relationships with employees, suppliers, and customers Details about the company’s leadership, executive pay, audits, and internal controls The Hydrus platform enables organizations to collect, store, and audit diversity and environmental data, and run analytics and machine learning against that data. Hydrus offers users a first-rate UI/UX so that even non-technical users can leverage the platform. With the auditing capabilities, organizations can ensure the provenance and integrity of ESG data over time. Other solutions don't allow users to go back in time and determine who made changes to the data, why they made them, what earlier versions of the data looked like, and what time the changes were made. Hydrus gives users complete visibility into these activities. The tech stack MongoDB Atlas was the preferred database for Hydrus because of the flexibility of the data model. George Lee, founder and CEO of Hydrus, says the traditional SQL database model was too limiting for the startup's needs. MongoDB's document model eliminated the need to create tables or enforce restrictions of data fields. With MongoDB, they could simply add fields without undertaking any major schema changes. Hydrus also tapped MongoDB for access to engineers and technical resources. This enabled the company to architect its platform for all of the different types of sustainability data that exist. MongoDB technical experts helped Hydrus model data for future scalability and flexibility so it could add data fields when the need arises. On top of Atlas and MongoDB technical support, Hydrus leans heavily on MongoDB Charts , a data visualization tool for creating, sharing, and embedding visualizations from MongoDB Atlas. Charts enables Hydrus to derive insights from ESG data, giving its Fortune 200 clients better visibility into their operational efficiency. Charts uses a drag-and-drop interface that makes it easy to build charts and answer questions about ESG data. A Hydrus customer using MongoDB Charts was better able to understand the impact of their footprint from a greenhouse gas perspective and a resource usage perspective. Another customer detected a 30x increase in refrigerant usage in one of its facilities. The visual analytics generated with MongoDB Charts enabled the company to make changes to improve their ESG performance. MongoDB Charts enabled Hydrus to visualize sustainability data "MongoDB Charts enables our customers to directly report their sustainability data, customize the charts, and better tell the sustainability story in a visual format," Lee says. "It's way better than the traditional format where you have data, tables, and spreadsheets everywhere." The roadmap Hydrus seeks to take the hassle out of managing a sustainable business by streamlining data collection, reporting, and auditing processes. Its platform is designed to eliminate manual tasks for sustainability managers so they can focus on decarbonization, resource usage optimization, and being able to hit their sustainability goals. Hydrus accelerates these activities by helping companies model their sustainability data around science-based targets so they can better decarbonize and meet other ESG goals. If you're interested in learning more about how to help your organization become more sustainable, decarbonize, and succeed in your sustainability journey, visit the Hydrus website . Are you part of a startup and interested in joining the MongoDB for Startups program? Apply now . For more startup content, check out our wrap-up of the 2022 year in startups .
How Startups Stepped Up in 2022
After muddling through the global pandemic in 2021, entrepreneurs emerged in 2022 ready to transform the way people live, learn, and work. Through the MongoDB for Startups program, we got a close-up view of their progress. What we observed was a good indication of how critical data is to delivering the transformative experiences users expect. Data access vs. data governance The increasing importance of data in the digital marketplace has created a conflict that a handful of startups are working to solve: Granting access to data to extract value from it while simultaneously protecting it from unauthorized use. In 2022, we were excited to work with promising startups seeking to strike a balance between these competing interests. Data access service provider Satori enables organizations to accelerate their data use by simplifying and automating access policies while helping to ensure compliance with data security and privacy requirements. At most organizations, providing access to data is a manual process often handled by a small team that's already being pulled in multiple directions by different parts of the organization. It's a time-consuming task that takes precious developer resources away from critical initiatives and slows down innovation. Data governance is a high priority for organizations because of the financial penalties of running afoul of data privacy regulations and the high cost of data breaches. While large enterprises make attractive targets, small businesses and startups in particular need to be vigilant because they can less afford financial and reputational setbacks. San Francisco-based startup Vanta is helping companies scale security practices and automate compliance for the most prevalent data security and privacy regulatory frameworks. Its platform gives organizations the tools they need to automate up to 90% of the work required for security audits. Futurology The Internet of Things (IoT), artificial intelligence (AI), virtual reality (VR), and natural language processing (NLP) remain at the forefront of innovation and are only beginning to fulfill their potential as transformative technologies. Through the MongoDB for Startups program, we worked with several promising ventures that are leveraging these technologies to deliver game-changing solutions for both application developers and users. Delaware-based startup Qubitro helps companies bring IoT solutions to market faster by making the data collected from mobile and IoT devices accessible anywhere it's needed. Qubitro creates APIs and SDKs that let developers activate device data in applications. With billions of devices producing massive amounts of data, the potential payoff in enabling data-driven decision making in modern application development is huge. London-based startup Concured uses AI technology to help marketers know what to write about and what's working for themselves and their competitors. It also enables organizations to personalize experiences for website visitors. Concured uses NLP to generate semantic metadata for each document or article and understand the relationship between articles on the same website. Another London-based startup using AI and NLP to deliver transformative experiences is Semeris . Analyzing legal documents is a tedious, time-consuming process, and Semeris enables legal professionals to reduce the time it takes to extract information from documentation. The company’s solution creates machine learning (ML) models based on publicly available documentation to analyze less seen or more private documentation that clients have internally The language we use in day-to-day communication says a lot about our state of mind. Sydney-based startup Pioneera looks at language and linguistic markers to determine if employees are stressed out at work or at risk for burnout. When early warning signs are detected, the person gets the help they need to reduce stress, promote wellness, and improve productivity confidentially and in real time. Technologies like AR and VR are transforming learning for students. Palo Alto-based startup Inspirit combines 3D and VR instruction to create an immersive learning experience for middle and high school students. The platform helps students who love science engage with the subject matter more deeply and those who dislike it to experience it in a more compelling format. No code and low code The startup space is rich with visionary thinkers and ideas. But the truth is that you can't get far with an idea if you don't have access to developer talent, which is scarce and costly in today's job market. We've worked with a couple of companies through the MongoDB for Startups program that are helping entrepreneurs breathe life into their ideas with low- and no-code solutions for building applications and bringing them to market. Low- and no-code platforms enable users with little or no coding background to satisfy their own development needs. For example, Alloy Automation is a no-code integration solution that integrates with and automates ecommerce services, such as CRM, logistics, subscriptions, and databases. Alloy can automate SMS messages, automatically start a workflow after an online transaction, determine if follow-up action should be taken, and automate actions in coordination with connected apps. Another example is Thunkable , a no-code platform that makes it easy to build custom mobile apps without any advanced software engineering knowledge or certifications. Thunkable's mission is to democratize mobile app development. It uses a simple drag-and-drop design and powerful logic blocks to give innovators the tools they need to breathe life into their app designs. The startup journey Although startups themselves are as diverse as the people who launch them, all startup journeys begin with the identification of a need in the marketplace. The MongoDB for Startups program helps startups along the way with free MongoDB Atlas credits, one-on-one technical advice, co-marketing opportunities, and access to a vast partner network. Are you a startup looking to build faster and scale further? Join our community of pioneers by applying to the MongoDB for Startups program. Apply now .
Top 3 Wins and Wants from the Latest TDWI Modernization Report
We recently reported that analyst and research firm TDWI had released its latest report on IT modernization: Maximizing the Business Value of Data: Platforms, Integration, and Management . The report surveyed more than 300 IT executives, data analysts, data scientists, developers, and enterprise architects to find out what their priorities, objectives, and experiences have been in terms of IT modernization. In many ways, organizations have made great progress. From new data management and data integration capabilities to smarter processes for higher business efficiency and innovations, IT departments have helped organizations get more value from the data they generate. In other cases, organizations are still stuck in data silos and struggling with improving data quality as data distribution increases due to the proliferation of multi-cloud environments. In this article, we'll summarize the top three areas where organizations are winning and the top three ways that organizations are left wanting when it comes to digital transformation and IT modernization. Download the complete report, Maximizing the Business Value of Data: Platforms, Integration, and Management , and find out the latest strategies, trends, and challenges for businesses seeking to modernize. Wins 1. Cloud migration Moving legacy applications to the cloud is essential for organizations seeking to increase operational efficiency and effectiveness, generate new business models through analytics, and support automated decision-making — the three biggest drivers of modernization efforts. And, most organizations are succeeding. Seventy-two percent of respondents in the TDWI survey reported being very or somewhat successful moving legacy applications to cloud services. Migrating to the cloud is one thing, but getting data to the right people and systems at the right time is another. For organizations to get full value of their data in the cloud, they also need to ensure the flow of data into business intelligence (BI) reports, data warehouses, and embedded analytics in applications. 2. 24/7 operations The ability to run continuous operations is a widely shared objective when organizations take on a transformation effort. Increasingly global supply chains, smaller and more dispersed office locations, and growing international customer bases are major drivers of 24/7 ops. And, according to the TDWI survey, more than two-thirds of organizations say they've successfully transitioned to continuous operations. 3. User satisfaction Organizations are also winning the race to match users' needs when provisioning data for BI, analytics, data integration, and the data management stack. Eighty percent of respondents said their users were satisfied with these capabilities. Additionally, 72% trusted in the quality of data and how it's governed, and 68% were satisfied that role-based access controls were doing a good job of ensuring that only authorized users had access to sensitive data. Wants 1. Artificial intelligence, machine learning, and predictive intelligence Machine learning (ML) and artificial intelligence (AI) comprise a key area where organizations are left wanting. While 51% of respondents were somewhat or very satisfied with their use of AI and ML data, almost the same number (49%) said they were neither satisfied nor dissatisfied, somewhat dissatisfied, or very dissatisfied. Similar results were also reported for data-driven predictive modeling. The report notes that provisioning data for AI/ML is more complex and varied than for BI reporting and dashboards, but that cloud-based data integration and management platforms for analytics and AI/ML could increase satisfaction for these use cases. 2. More value from data Perhaps related to the AI/ML point, the desire to get more value out of their data was cited as the biggest challenge organizations face by almost 50% of respondents. Organizations today capture more raw, unstructured, and streaming data than ever, and they're still generating and storing structured enterprise data from a range of sources. One of the big challenges organizations reported is running analytics on so many different data types. According to TDWI, organizations need to overcome this challenge to inform data science and capitalize modern, analytics-infused applications . 3. Easier search A big part of extracting more value from data is making it easy to search. Traditional search functionality, however, depends on technically challenging SQL queries. According to the TDWI report, 19% of users were dissatisfied with their ability to search for data, reports, and dashboards using natural language. Unsurprisingly, frustration with legacy technologies was cited as the third biggest challenge facing organizations, according to the survey. The way forward "In most cases, data becomes more valuable when data owners share data," the TDWI report concludes. Additionally, the key to making data more shareable is moving toward a cloud data platform , one that makes data more available while simultaneously governing access when there's a need to protect the confidentiality of sensitive data. Not only does a cloud data platform make data more accessible and shareable for users, it also creates a pipeline for delivering data to applications that can use it for analytics, AI, and ML. Read the full TDWI report: Maximizing the Business Value of Data: Platforms, Integration, and Management .
How to Get Mobile Data Sync Right with Mobile Backend as a Service (MBaaS)
Twenty years ago, Watts Humphrey, known today as the "Father of Software Quality," declared that every business is a software business. While his insight now seems obvious, digital technology has evolved to where we can add to it: Every business is also a mobile business. According to Gartner , 75% of enterprise data will be generated and processed away from the central data center by 2025. And according to data.ai, 84% of enterprises attribute growth in productivity to mobile apps. Today, mobile tech transforms every aspect of business. It enables the workforce through point-of-sale, inventory, service, and sales. It streamlines critical business processes like self-checkout and customer communications. And it powers essential work devices from telemetry to IoT to manufacturing. The data businesses capture on mobile and edge devices can be used to improve operational efficiency, drive process improvements, and deliver richer, real-time app experiences. But all of this requires a solution for synchronizing mobile data with backend systems, where it can be combined with other historical data, analyzed, or fed into predictive intelligence algorithms to surface new insights and trigger other value-add activities. But syncing mobile data with backend systems can be hard for a number of reasons. Mobile devices are constantly going in and out of coverage. When connections break and then resume, conflicts emerge between edits that were made on devices while offline and other data that's being processed on the backend. So conflict resolution becomes a crucial part of ensuring changes on the mobile device are captured on the backend in a way that ensures data integrity. Sync and swim Apps that are not designed with backend sync in mind can take a long time to load, are prone to crashing, and show stale information. When apps don’t deliver positive experiences, people stop trusting them — and stop using them. On the other hand, an app with robust sync between a device’s local data store and the back end lets workers see live data across users and devices, allowing for real-time collaboration and decision-making. According to Deloitte , 70% of workers don’t sit at a desk every day, so the ability to sync data will increasingly drive business outcomes. Indian startup, FloBiz , uses MongoDB Atlas Device Sync to handle the difficult job of keeping the mobile, desktop, and web apps in sync. This means even if multiple users were using the same account, going offline and online, there would be no issues, duplications or lost data. Why data sync is difficult A lot of organizations choose to build their own sync solutions. DIY solutions can go one of two ways: overly complex or oversimplified, resulting in sync that happens only a few times a day or in only one direction. It can be complicated and time-consuming for developers to write their own conflict-resolution code because building data sync the right way takes potentially thousands of lines of code. Developers frequently underestimate the challenge because it seems straightforward on the surface. They assume sync consists simply of the application making a request from the server, receiving some data, and using that data to update the app’s UI on the device. But when building for mobile devices this is a massive oversimplification. Building data sync can be more complicated than people assume. When developers attempt to build their own sync tool, they typically use RESTful APIs to connect the mobile app with the backend and exchange data between them. Mobile apps are often built more like web apps in the beginning. But once the need to handle offline scenarios arises, and because some functionality requires local persistence, then it becomes necessary to add a mobile database. Syncing with that mobile database then becomes a challenge. The exchange of data between the device and the back end gets complicated. It requires the developer to anticipate numerous offline use cases and write complex conflict-resolution code. It can be done, but it’s a time-consuming process that’s not guaranteed to solve all use cases. When data is requested, applications need to understand whether a network is available, and if not, whether the appropriate data is stored locally, leading to complex query, retry, and error handling logic. The worst part about all this complexity is that it’s non-differentiating, meaning it doesn’t set the business apart from the competition. Users expect the functionality powered by data sync and won’t tolerate anything less. An integrated, out-of-the-box solution MongoDB's Atlas Device Sync combined with Realm is a mobile backend as a service (MBaaS) solution that enables developers to build offline-first applications that automatically refresh when a connection is reestablished. Local and edge data persistence is managed by Realm, a development platform designed for modern, data-driven applications. Developers use Realm to build mobile, web, desktop, and IoT apps. Realm is a fast and scalable alternative to SQLite and Core Data for client-side persistence. The bidirectional data synchronization service between Realm and MongoDB Atlas allows businesses to do more with their data at the edge by tapping into some of MongoDB’s more powerful data processing capabilities in the cloud. Complex synchronization problems such as conflict resolution are handled automatically by MongoDB’s built-in sync. To learn more about the challenges of building real-time mobile apps that scale, with sample use cases about how thousands of businesses are handling it today, download our white paper, Building Real-time Mobile Apps that Scale .
Zero Trust will be a Critical Practice for Security Professionals in 2023
Being a security professional in 2022 was no walk in the park. In a year that saw thousands of data breaches, even the most seasoned security professionals had their hands full. In our latest episode of the MongoDB Podcast , MongoDB Chief Information Security Officer, Lena Smart, joined tech legend and MongoDB co-founder, Dwight Merriman, to discuss the changing IT security landscape and the trends that will shape best practices for the future. Technology anti-trends As a technology entrepreneur who has been involved in a half-dozen startups, Merriman developed a sense for trends in technology that intersect with user needs. In 1995, the internet was one of those trends. Others that followed include LANs, smart phones, and AI. But what's different about security, Merriman says, is that it acts as more of an anti-trend, meaning that it's a problem that only seems to be getting harder to solve. "Information security has always been an issue," Merriman says. "But every year it gets harder. Pre-internet it was a bit easier, when you're not plugged into the entire planet. Today, the inherent complexity of modern software means there are more attack vectors." As the IT complexity anti-trend coincides with an increase in the sophistication of hackers, the job of security professionals only gets harder. "You've got everything from the kid in their basement hacking around to more sophisticated attacks like organized crime and nation-state actors," Merriman says. "How do you defend against that as a company when you have orders of magnitude less resources? As a CISO, security person, or developer, it's just getting harder every day." Merriman predicts that it's going to get harder every year for the next 10 years, and the stakes are only going to get higher. "You cannot be too paranoid," he says, "We still need to get work done. So I'm a big proponent of, you know, you can't create too much friction." Controlling what you can control Ensuring security while reducing friction is one of the core principles of data governance, which includes the processes required to establish proper handling of an organization's data. Whether you're using third-party services, integrating with the software supply chain to build new applications or services, or working across internal departments, the best approach from a security perspective is to start with as little trust as possible. " Zero Trust is a big term these days," Merriman says. "Part of your supply chain is your internal supply chain. In large companies like a Fortune 500 company, where it's so big, you might as well be separate companies. So, whatever you think about when you think about security and supply chain, do that internally too. Think of each department as a supply chain if it is a supplier for you." The concept of the Zero Trust model is based around three principles: Never trust, always verify — This ensures that anyone who accesses company data is verified at the onset of access to network resources. Provide the least amount of privilege possible — Being judicious with who can access what data is essential to keep data protected. By limiting employee and external access to only data needed to perform a specific task, you reduce the likelihood of a breach. Apply network segmentation — By dividing data (like with MongoDB clusters), you isolate and protect it, rather than keeping it all in one place that, should it be breached, puts all data at risk. “Identity is your new security perimeter. You can never be too paranoid or too vigilant when it comes to determining who can access your business’s data,” says Merriman. Breaking new ground in security The security imperative is what drove MongoDB to partner with pioneers in the academic community to develop a groundbreaking new form of security, queryable encryption . Working with Brown University cryptographer Seny Kamara and long-time collaborator Tarik Moataz, the team developed the world's first truly searchable encrypted database. It enables organizations to encrypt sensitive data from the client side, store it as fully randomized encrypted data on the database server side, and run expressive queries on the encrypted data. Queryable encryption extends the idea of Zero Trust by adding an extra layer of security for data while it's in use by anyone tasked with handling it. Designed by our Advanced Cryptography Research Group with 20 years of experience designing peer-reviewed, state-of-the-art encrypted search algorithms, Queryable Encryption is available in Preview now . Listen to the full conversation with MongoDB Chief Information Security Officer, Lena Smart and legend and MongoDB co-founder, Dwight Merriman. If your organization needs a way to construct database architectures that are not only scalable, but also secure, consider using MongoDB Atlas to build the next big thing.
Choosing the Right Tool for the Job: Understanding the Analytics Spectrum
Data-driven organizations share a common desire to get more value out of the data they're generating. To maximize that value, many of them are asking the same or similar questions: How long does it take to get analytics and insights from our application data? What would be the business impact if we could make that process faster? What new experiences could we create by having analytics integrated directly within our customer-facing apps? How do our developers access the tools and APIs they need to build sophisticated analytics queries directly into their application code? How do we make sense of voluminous streams of time-series data? We believe the answer to these questions in today's digital economy is application-driven analytics. What is Application-Driven Analytics? Traditionally, there's been a separation at organizations between applications that run the business and analytics that manage the business. They're built by different teams, they serve different audiences, and the data itself is replicated and stored in different systems. There are benefits to the traditional way of doing things and it's not going away. However, in today's digital economy, where the need to create competitive advantage and reduce costs and risk are paramount, organizations will continue to innovate upon the traditional model. Today, those needs manifest themselves in the demand for smarter applications that drive better customer experiences and surface insights to initiate intelligent actions automatically. This all happens within the flow of the application on live, operational data in real time. Alongside those applications, the business also wants faster insights so it can see what's happening, when it's happening. This is known as business visibility, and the goal of it is to increase efficiency by enabling faster decisions on fresher data. In-app analytics and real-time visibility are enabled by what we call application-driven analytics. Find out why the MongoDB Atlas developer data platform was recently named a Leader in Forrester Wave: Translytical Data Platforms, Q4 2022 You can find examples of application-driven analytics in multiple real-world industry use cases including: Hyper-personalization in retail Fraud prevention in financial services Preventative maintenance in manufacturing Single subscriber view in telecommunications Fitness tracking in healthcare A/B testing in gaming Where Application-Driven Analytics fits in the Analytics Ecosystem Application-driven analytics complements existing analytics processes where data is moved out of operational systems into centralized data warehouses and data lakes. In no way does it replace them. However, a broader spectrum of capabilities are now required to meet more demanding business requirements. Contrasting the two approaches, application-driven analytics is designed to continuously query data in your operational systems. The freshest data comes in from the application serving many concurrent users at very low latency. It involves working on much smaller subsets of data compared to centralized analytics systems. Application-driven analytics is typically working with hundreds to possibly a few thousand records at a time. And it's running less complex queries against that data. At the other end of the spectrum is centralized analytics. These systems are running much more complex queries across massive data sets — hundreds of thousands or maybe millions of records, and maybe at petabyte scale — that have been ingested from many different operational data sources across the organization. Table 1 below identifies the required capabilities across the spectrum of different classes of analytics. These are designed to help MongoDB’s customers match appropriate technologies and skill sets to each business use case they are building for. By mapping required capabilities to use cases, you can see how these different classes of analytics serve different purposes. If, for example, we're dealing with recommendations in an e-commerce platform, the centralized data warehouse or data lake will regularly analyze vast troves of first- and third-party customer data. This analysis is then blended with available inventory to create a set of potential customer offers. These offers are then loaded back into operational systems where application-driven analytics is used to decide which offers are most relevant to the customer based on a set of real-time criteria, such as actual stock availability and which items a shopper might already have in their basket. This real-time decision-making is important because you wouldn't want to serve an offer on a product that can no longer be fulfilled or on an item a customer has already decided to buy. This example demonstrates why it is essential to choose the right tool for the job. Specifically, in order to build a portfolio of potential offers, the centralized data warehouse or data lake is an ideal fit. Such technologies can process hundreds of TBs of customer records and order data in a single query. The same technologies, however, are completely inappropriate when it comes to serving those offers to customers in real time. Centralized analytics systems are not designed to serve thousands of concurrent user sessions. Nor can they access real-time inventory or basket data in order to make low latency decisions in milliseconds. Instead, for these scenarios, application-driven analytics served from an operational system is the right technology fit. As we can see, application-driven analytics is complementary to traditional centralized analytics, and in no way competitive to it. The benefits to organizations of using these complementary classes of analytics include: Maximizing competitive advantage through smarter and more intelligent applications Out-innovating and differentiating in the market Improving customer experience and loyalty Reducing cost by improving business visibility and efficiency Through its design, MongoDB Atlas unifies the essential data services needed to deliver on application-driven analytics. It gives developers the tools, tech, and skills they need to infuse analytics into their apps. At the same time, Atlas provides business analysts, data scientists, and data engineers direct access to live data using their regular tools without impacting the app. For more information about how to implement app-driven analytics and how the MongoDB developer data platform gives you the tools needed to succeed, download our white paper, Application-Driven Analytics: Defining the Next Wave of Modern Apps .
Built With MongoDB: Inspirit Helps Kids Learn Science Through Immersive Technology
Immersive technologies like AR and VR have tremendous potential to transform learning outcomes for students. By representing complicated, often intangible concepts in an interactive, 3D platform and VR system, they encourage engagement and improve memory recall in STEM (science, technology, engineering, and mathematics) subjects. Palo Alto-based startup, Inspirit , has developed an immersive, interactive VR platform that combines the best of both 3D and VR instruction to allow students in middle school and high school to experience science and learning through virtual reality instead of reading. Inspirit is also part of the MongoDB for Startups program , which helps startups build faster and scale further with free MongoDB Atlas credits, one-on-one technical advice, co-marketing opportunities, and access to a vast partner network. Birth of a Startup Inspirit Co-founders Amrutha Vasan and Aditya Vishwanath were conducting research at Georgia Tech and trying to understand how to bring virtual reality into education when they had a critical insight. "A lot of students tend to lose sight or just lose interest in science by the time they hit high school," Vasan says. "What we realized very quickly was that you can give teachers the tools that they need in order to do things that are way too expensive or just simply impossible to do in the real world." As students get into higher order physics, calculus, biology, and chemistry, they struggle with visualizing difficult concepts. "What we aim to do is provide interactive 3D models and interactive simulations that actually teach them core science concepts so that they build that curiosity and actually engage with the material in order to keep them in STEM programs and moving forward," Vasan says. Company evolution Inspirit has evolved since its early days. The founders initially focused on VR headsets and 3D goggles that students would share in the classroom. Once the global COVID-19 pandemic hit, it was clear that students wouldn't be going back to the classroom anytime soon. "We had to very quickly pivot into building a web platform that students could use at home and teachers could use for hybrid and online teaching," Vasan says. "Through the pandemic, we learned very quickly exactly how the education system would actually be changing. And so through that adaptation, we've now built a cross-platform product." Building a cross-platform solution aligns with one of Inspirit's core values, which is to be accessible. Inspirit is now available on the web in addition to virtual reality headsets so students can access the tools on different platforms. Inspirit isn't just an extracurricular activity. One of the biggest differentiators between Inspirit VR classroom and other platforms is that it addresses core content. It's not something that students need to use independently. "You still need a really great teacher," Vasan says. "There is no way for you to show a student a eukaryotic cell other than by drawing it on a whiteboard or giving them a diagram or a video," she says. "You still need a great teacher in order to use our platform. But they can now use this to help their students visualize difficult science concepts." Inspirit helps students who love science immerse themselves in it more while at the same time helping kids who dislike science experience it in a new, more engaging way. Building with MongoDB Inspirit chose MongoDB over other platforms because of its support and flexibility. As a startup, the founders knew their database structure would be changing constantly. "MongoDB allows for a lot of flexibility," Vasan says, "which is really important because we're probably not going to have the perfect database going into a startup. Allowing us to consistently change it as we're scaling and growing has just been very helpful." Regarding MongoDB support, which is one of the benefits of the MongoDB for Startups program, Vasan says the company got the most support from MongoDB than any of the other databases they were looking at. "That's ultimately why we ended up choosing it for our company," she says. "The things that we love the most about MongoDB are Atlas , Charts , and search indexes . Atlas has been really cool for visualizing all the data and for helping us grow and scale quickly as well." "We've had a really good partnership with the MongoDB for Startups program in particular," Vasan says. Inspirit uses AWS, and Vasan cites how easily Atlas integrates with AWS as another key benefit that she appreciates. "AWS integrates very easily with MongoDB. So to be honest, it's been pretty easy scaling our backend." Compliance was another critical requirement. "We actually need to be COPPA compliant," Vasan says, referring to the Children's Online Privacy Protection Rule. "There are a lot of data regulations for what we're using since we have sensitive student, parent, and teacher data. And so both MongoDB and AWS actually make that a lot easier." If you're interested in learning more about Inspirit's immersive learning tools, visit their website . Are you part of a startup and interested in joining the MongoDB for Startups program? Apply now . For more startup content, check out our previous blog on Qubitro .
3 Key Characteristics of Modernization
Analyst and research firm TDWI released its latest report on IT modernization: Maximizing the Business Value of Data: Platforms, Integration, and Management . The report reveals the modernization strategies, objectives, and experiences of more than 300 IT executives, data analysts, data scientists, developers, and enterprise architects. Within the survey itself lies the deeper, fundamental question of what is IT modernization in today's digital economy? It's an important question because it gets at the heart of why organizations want and need to modernize in the first place. Considering the effort, expense, and risks of modernizing, there needs to be a compelling purpose guiding the process in order to keep it on track and ensure its success. By dissecting the TDWI survey questions and responses, we can deduce what the three key characteristics of modernization are. #1: Modernization capabilities If we were to examine the elements and components that comprise modernized architecture, we would get a sense of what modernization looks like but not the purpose behind its deployment. So instead, let's start by looking at the capabilities modern architecture enables so we can get a clearer view of its characteristics and why they matter. Seventy-three percent of survey respondents reported that data democratization and self-service functionality are either extremely or very important. We've heard from numerous organizations that the task of managing data access at companies is slowing down innovation. Ben Herzberg, chief data scientist for data access company, Satori, recently told us , "The majority of organizations are still managing access to data in a manual way. Everyone is feeling the bottleneck. The data analyst who wants to do their job in a meaningful way just wants to understand what data sets they can use and get access to it fast." Getting access to data can be challenging without some sort of self-service data access capability. "Sometimes you have to go through three or four different teams to get access to data," Herzberg says. "It can take a week or two." The TDWI report also indicated a long-standing trend toward easier, more intuitive experiences extending to data integration, data pipelines, data catalog interaction, and monitoring. Survey respondents' top priorities over the next 12 months support this trend. In addition to migrating and consolidating data in the cloud, they intend to prioritize the following key capabilities: Enabling better data management for data science, AI, and ML Supporting development and deployment of data driven applications Supporting expansion in self service Business intelligence (BI) and analytics users Unifying management of data across distributed systems BI and analytics platforms remain one of the fastest growing software markets. The capabilities necessary to power these systems are in high demand: self-service analytics, faster discovery, predictions based on real-time operational data, and integration of rich and streaming data sets. The survey responses also showed that handling an increase in data volume and the number of concurrent users are modernization priorities. And there's pressure to reduce data latency and increase the frequency of updates. The survey showed that one of the most challenging capabilities organizations are dealing with is enabling low latency querying, search, and analytics. Giving users the right data at the right time to answer business questions, solve problems, and innovate with data is critical today and it depends on these capabilities. #2: Modernization outcomes The capabilities organizations seek only serve their modernization goals as far as they enable specific outcomes. And it's outcomes that are ultimately driving modernization initiatives. According to the survey, the number one outcome organizations seek to bring about is gaining fuller value from the data they store and capture. Forty-six percent of respondents cited it as their top challenge. Automating decision-making is another outcome organizations are seeking. Thirty-two percent of respondents rated automating decisions in operations in processes as very important. But it relies on the timely flow of insights into apps, one of the key capabilities identified earlier. Other key modernization outcomes cited in the survey include: Increase efficiency and effectiveness Generate new business strategies and models using analytics Make faster decisions Strengthen relationships via data sharing Improve trust and data quality Increase reuse and flexibility Reduce costs Provide authorized access to live data sets Consolidate data silos Developers in the survey said they were seeking to embed richer, personalized application experiences, with 52% saying they wanted seamless access to diverse data sets and sources. But first, they'll have to overcome several challenges that so far have proved difficult to solve. Sixty-eight percent of respondents said they face challenges processing streaming data and change data capture updates, and 64% struggle to integrate streaming with fast, high volume queries, and the same percentage said they struggle with combining historical and real-time analytics. #3: Modernization platform Modernized problems require modernized solutions. And the one most most commonly cited by respondents was a data platform , which they believe is the key to maximizing value from data. A data platform solves the issue of consolidating unnecessary data silos and ensuring access to data without the hassle of manual intervention or the risk of unauthorized access. Flexibility in the data platform is critical since data environments will continue to evolve, even after modernization milestones have been met. A data platform is one of the key elements that comprise modernized architecture. The TDWI survey cited several other advantages of unifying distributed data within a data platform: Simplifying and accelerating access Discovering data relationships easier and faster Creating a logical layer for single point of access Unifying data governance Reducing unnecessary data movement Modernized architecture Fifty-four percent of respondents said they were in the process of modernizing, and 29% were planning on doing so. The most frequently cited architectural feature by those modernizing or planning to was cloud migration from on-premises systems, with the goal being to change the dimensions of what was possible. But it wasn't just shifting to the cloud that respondents mentioned. The survey also indicated the prevalence of hybrid multi-cloud architectures as well, with data integration and management that span distributed data environments. Distributed architectures can lead to higher performance by putting data closest to where it's being used. It also solves data sovereignty issues by putting data where it's required to be due to regulatory jurisdiction. The report also mentions serverless architecture due to its pay-as-you-go computing model and improved business alignment. With serverless architecture , developers can build applications without thinking about infrastructure or traditional server management. Read the full TDWI report, Maximizing the Business Value of Data: Platforms, Integration, and Management .
MongoDB Joins Auth0 to Help Startups Combat Security Risks
We are excited to announce that MongoDB for Startups is collaborating with Auth0 for Startups to provide top security for applications by the most innovative startups. Why should a startup be part of the MongoDB and Auth0 startup programs? Customers, investors, and stakeholders expect many different things from a company, but one common requirement is responsibly managing their data. Companies choose MongoDB because it accelerates application development and makes it easier for developers to work with data. Developers mindful of security, compliance, and privacy when it comes to data use the robust Auth0 platform to create great customer experiences with features like single sign-on and multi-factor authentication. “Auth0 and MongoDB are very complementary in nature. While MongoDB provides a strong, secure data platform to store sensitive workloads, Auth0 provides secure access for anyone with the proper authorization," says Soumyarka Mondal, Co-founder of Sybill.ai. "We are safely using Auth0 as one of the data stores for the encryption piece, as well as using those keys to encrypt all of our users’ confidential information inside MongoDB.” What is the Auth0 for Startups Program? Auth0, powered by Okta, takes a modern approach to identity and enables startups to provide secure access to any application, for any user. Through Auth0 for Startups, we are bringing the convenience, privacy, and security of Auth0 to early-stage ventures, allowing them to focus on growing their business quickly. The Auth0 for Startups program is free for one year and supports: 100,000 monthly active users Five enterprise connections Passwordless authentication Breached password detection 50+ integrations, 60+ SDKs, and 50+ social & IdP connections What is the MongoDB for Startups Program? MongoDB for Startups is focused on enabling the success of high-growth startups from ideation to IPO. The program is designed to give startups access to the best technical database for their rapidly scaling ventures. Apply to our program and program participants will receive: $500 in credits for all MongoDB cloud products (valid for 12 months) A dedicated technical advisor for a two-hour, one-to-one consultation to help you with your data migration and optimization Co-marketing opportunities Access to the MongoDB developer ecosystem and access to our VC partners. Apply to Auth0 For Startups and the MongoDB for Startups Program today.
5 Key Questions for App-Driven Analytics
Note: This article originally appeared in The New Stack . Data that powers applications and data that powers analytics typically live in separate domains in the data estate. This separation is mainly due to the fact that they serve different strategic purposes for an organization. Applications are used for engaging with customers while analytics are for insight. The two classes of workloads have different requirements—such as read and write access patterns, concurrency, and latency—therefore, organizations typically deploy purpose-built databases and duplicate data between them to satisfy the unique requirements of each use case. As distinct as these systems are, they're also highly interdependent in today's digital economy. Application data is fed into analytics platforms where it's combined and enriched with other operational and historical data, supplemented with business intelligence (BI), machine learning (ML) and predictive analytics, and sometimes fed back to applications to deliver richer experiences. Picture, for example, an ecommerce system that segments users by demographic data and past purchases and then serves relevant recommendations when they next visit the website. The process of moving data between the two types of systems is here to stay. But, today, that’s not enough. The current digital economy, with its seamless user experiences that customers have come to expect, requires that applications also become smarter, autonomously taking intelligent actions in real time on our behalf. Along with smarter apps, businesses want insights faster so they know what is happening “in the moment.” To meet these demands, we can no longer rely only on copying data out of our operational systems into centralized analytics stores. Moving data takes time and creates too much separation between application events and analytical actions. Instead, analytics processing must be “shifted left” to the source of the data—to the applications themselves. We call this shift application-driven analytics . And it’s a shift that both developers and analytics teams need to be ready to embrace. Find out why the MongoDB Atlas developer data platform was recently named a Leader in Forrester Wave: Translytical Data Platforms, Q4 2022 Defining required capabilities Embracing the shift is one thing; having the capabilities to implement it is another. In this article, we break down the capabilities required to implement application-driven analytics into the following five critical questions for developers: How do developers access the tools they need to build sophisticated analytics queries directly into their application code? How do developers make sense of voluminous streams of time series data? How do developers create intelligent applications that automatically react to events in real time? How do developers combine live application data in hot database storage with aged data in cooler cloud storage to make predictions? How can developers bring analytics into applications without compromising performance? To take a deeper dive into app-driven analytics—including specific requirements for developers compared with data analysts and real-world success stories—download our white paper: Application-Driven Analytics . 1. How do developers access the tools they need to build sophisticated analytics queries directly into their application code? To unlock the latent power of application data that exists across the data estate, developers rely on the ability to perform CRUD operations, sophisticated aggregations, and data transformations. The primary tool for delivering on these capabilities is an API that allows them to query data any way they need, from simple lookups to building more sophisticated data processing pipelines. Developers need that API implemented as an extension of their preferred programming language to remain "in the zone" as they work through problems in a flow state. Alongside a powerful API, developers need a versatile query engine and indexing that returns results in the most efficient way possible. Without indexing, the database engine needs to go through each record to find a match. With indexing, the database can find relevant results faster and with less overhead. Once developers start interacting with the database systematically, they need tools that can give them visibility into query performance so they can tune and optimize. Powerful tools like MongoDB Compass let users monitor real-time server and database metrics as well as visualize performance issues . Additionally, column-oriented representation of data can be used to power in-app visualizations and analytics on top of transactional data. Other MongoDB Atlas tools can be used to make performance recommendations , such as index and schema suggestions to further streamline database queries. 2. How do you make sense of voluminous streams of time series data? Time series data is typical in many modern applications. Internet of Things (IoT) sensor data, financial trades, clickstreams, and logs enable businesses to surface valuable insights. To help, MongoDB developed the highly optimized time series collection type and clustered indexes. Built on a highly compressible columnar storage format, time series collections can reduce storage and I/O overhead by as much as 70%. Developers need the ability to query and analyze this data across rolling time windows while filling any gaps in incoming data. They also need a way to visualize this data in real time to understand complex trends. Another key requirement is a mechanism that automates the management of the time series data lifecycle. As data ages, it should be moved out of hot storage to avoid congestion on live systems; however, there is still value in that data, especially in aggregated form to provide historical analysis. So, organizations need a systematic way of tiering that data into low-cost object storage in order to maintain their ability to access and query that data for the insights it can surface. 3. How do you create intelligent applications that automatically react to events in real time? Modern applications must be able to continuously analyze data in real time as they react to live events. Dynamic pricing in a ride-hailing service, recalculating delivery times in a logistics app due to changing traffic conditions, triggering a service call when a factory machine component starts to fail, or initiating a trade when stock markets move—these are just a few examples of in-app analytics that require continuous, real-time data analysis. MongoDB Atlas has a host of capabilities to support these requirements. With change streams , for example, all database changes are published to an API, notifying subscribing applications when an event matches predefined criteria. Atlas triggers and functions can then automatically execute application code in response to the event, allowing you to build reactive, real-time, in-app analytics. 4. How do you combine live application data in hot database storage with aged data in cooler cloud storage to make predictions? Data is increasingly distributed across different applications, microservices , and even cloud providers. Some of that data consists of newly ingested time-series measurements or orders made in your ecommerce store and resides in hot database storage. Other data sets consist of older data that might be archived in lower cost, object cloud storage. Organizations must be able to query, blend, and analyze fresh data coming in from microservices and IoT devices along with cooler data, APIs, and third-party data sources that reside in object stores in ways not possible with regular databases. The ability to bring all key data assets together is critical for understanding trends and making predictions, whether that's handled by a human or as part of a machine learning process. 5. How can you bring analytics into your applications without compromising their performance? Live, customer-facing applications need to serve many concurrent users while ensuring low, predictable latency and do it consistently at scale. Any slowdown degrades customer experience and drives customers toward competitors. In one frequently cited study, Amazon found that just 100 milliseconds of extra load time cost them 1% in sales . So, it's critical that analytics queries on live data don’t affect app performance. A distributed architecture can help you enforce isolation between the transactional and analytical sides of an application within a single database cluster . You can also use sophisticated replication techniques to move data to systems that are totally isolated but look like a single system to the app. Next steps to app-driven analytics As application-driven analytics becomes pervasive, the MongoDB Atlas developer data platform unifies the core data services needed to make smarter apps and improved business visibility a reality. Atlas does this by seamlessly bridging the traditional divide between transactional and analytical workloads in an elegant and integrated data architecture. With MongoDB Atlas, you get a single platform managing a common data set for both developers and analysts. With its flexible document data model and unified query interface, the Atlas platform minimizes data movement and duplication and eliminates data silos and architectural complexity while unlocking analytics faster and at lower cost on live operational data. It does all this while meeting the most demanding requirements for resilience, scale, and data privacy. For more information about how to implement app-driven analytics and how the MongoDB developer data platform gives you the tools needed to succeed, download our white paper, Application-Driven Analytics .
MongoDB World 2022 Recap — Performance Gotchas of Replicas Spanning Multiple Data Centers
Indeed has more than 25 million open jobs online at any one time. It stores more than 225 million resumes on Indeed systems, and it has 250 million unique users every month. Indeed operates enterprise-wide global clusters in the cloud across multiple availability zones all around the world, including the United States, Asia-Pacific, Europe, and Australia. Indeed is also a MongoDB super user. About 50% of everything Indeed does is built on MongoDB. In a recent session at MongoDB World 2022, Indeed senior cloud database engineer Alex Leong shared real-world experiences of performance issues when spanning replica sets across multiple data centers. He also covered how to identify these issues and, most importantly, how to fix them. This article provides highlights from Leong’s presentation, including dealing with changes in sync sources, replication lags, and more. Resilience and performance Indeed maintains multiple data centers for resiliency. Having multiple data centers ensures there's no single point of failure and keeps data in close proximity to job seekers' locations. This approach facilitates faster response times and better overall end user experience. Running multiple data centers can introduce other performance issues, however. One issue involves the initial sync of new nodes in the system, which needs to happen as quickly as possible to avoid returning stale data. Write concern is a critical consideration because, if there's an interruption on a primary node and a failover to a secondary, when you eventually roll back to the primary, any changes that were captured on the secondary while the system was running in failover mode must be preserved. Also, when you're running multiple data centers, changes in sync sources can occur that go unnoticed. Replication lags can occur when data centers are located far apart from each other. Overriding sync sources When you have an environment with hundreds of millions of users and enormous volumes of data spanning several geographic regions, spinning up and synchronizing a new node in a replica set creates logistical hurdles. To start, you have to decide where the new node syncs from. It seems logical that the default decision would be to sync with the nearest node. But, as Leong said in his session, at times you may not get the nearest sync source, and you may have to override the default sync source to choose the best one. This decision needs to be made early, Leong said, because doing so later means any progress you've made toward syncing the new node will have been wasted. Replication lags Replication lags can occur between the primary and secondary nodes for several reasons, including downtime (planned or unplanned) on the primary server, a network failure, or disk failure. Whatever the reason, there are ways to speed things up. In his session, Leong illustrates how to use the WiredTiger cache size to accelerate replication between nodes. Changes in sync sources Leong uses the term sync topology to describe how primary and secondary nodes are configured for syncing data between them. In some scenarios, a secondary node can change its sync source (sync topology) from one node to another, perhaps because the first node was busy at the time. MongoDB makes this change automatically, and it might not be noticed without looking at the log. Fixing cross-data center write concerns According to Leong, when write performance decreases, 99% of the time it's because of a change in sync sources. To be proactive, Leong creates a write performance monitor to identify and self-heal decreases in write performance so he doesn't have to find out the hard way (from users). Other critical performance issues covered in the session include chained replication , which is the process by which secondary nodes replicate from node to node, changing write concern when a secondary node goes down, and how to configure write concerns across Availability Zones in AWS. For more details, watch the complete session from MongoDB World 2022: Performance Gotchas of Replicas Spanning Multi Datacenters .