MongoDB Applied
Customer stories, use cases and experience
Built with MongoDB: Leadgence
September 29, 2021
Applied
Simplifying Compliance with VComply & MongoDB
As businesses globally are facing external pressures to be more focused on privacy, security, and transparency, compliance management is needed now more than ever. With 200+ regulatory updates, 900 regulatory agencies, and the average cost of a non-compliance incident being $14 million, maintaining compliance is critical for every business, no matter the size. Tracking, maintaining, and proving compliance has traditionally been incredibly difficult, resource-intensive, and takes a significant time commitment. That's why one startup aims to simplify compliance by disrupting the antiquated industry. Enter VComply . Founded in 2019, VComply is a governance, risk management, and compliance (GRC) platform that enables its customers with a secure and easy-to-use solution. VComply is highly configurable to meet the specific needs of any organization without additional coding or infrastructure changes. The platform collects, organizes, analyzes, and automatically reports on GRC data inputted into the system to provide a high-level view of an organization's compliance posture at any given time. Combining that with the ability to surface detailed information on any control, VComply modernizes how people work and interact with GRC programs within their businesses. In this week's #BuiltWithMongoDB, we take a look at VComply to learn more about how they are truly helping organizations strengthen their risk and compliance management. We spoke with Harshvardhan Kariwala, CEO, and Ashish Jha, Vice President of Engineering at VComply to discuss the company's journey and how they decided to build with MongoDB. What inspired you to build the business? Harshvardhan: VComply is actually my third startup. At one of my previous companies, I had become hyper-focused on building the business, and I eventually lost sight of compliance. Operational functions fell through the cracks, and I ended up outsourcing our compliance programs to this corporate firm in Singapore. Fast forward a bit, they ended up forgetting to do a required compliance filing, and we ended up responsible for paying the non-compliance fines associated with that. One reporting misstep, and we were fined. That's what got me worried. We got lucky that was all that happened. It only took one time to inspire action. We then built an internal tool where the entire idea was around creating a culture of reporting excellence and internal accountability. After adopting our newly created tool, in 2018, we realized that we built a very robust solution to real day-to-day compliance problems. We thought, "Why don't we spin this off into its own product?" By that time, I was ready to get back into product development, and this was the perfect opportunity. In early 2019 we set up VComply. We quickly got our first customer, the City of Boston, and never looked back. So that's where VComply got its start. It was never meant to be sold as a product. It was more of an internal compliance tracking tool. That's how we entered the GRC space. What exactly does VComply do? What are some of its most useful product features? Harshvardhan: We help businesses be compliant, mitigate risk, and adopt a culture of transparency. If there isn't internal alignment within a company, no tool is going to help them. At its core, VComply is designed to be easy to use so that anyone in an organization can adopt a compliance-first mindset. By removing the traditional technological barriers, we found that businesses can realize the benefits quickly. That said, VComply serves as the single source of truth for everything GRC within an organization. Think tracking compliance obligations, compliance monitoring, automating activities, alerts and follow-ups, compliance evidence collection, audit trails, and more. Another popular piece of our tool is our enterprise risk management as well as policy management functionalities. You can monitor and manage risk programs, quickly identify risks, and start linking compliance obligations to mitigate that risk. What makes VComply stand out from its competitors? Harshvardhan: Most other solutions on the market require a compliance expert, are hard to navigate, and take a significant time commitment upfront to get up and running. We built VComply to be more practical and realistic with how people manage their compliance and risk programs today. VComply is easy to set up, simple to use for the end-user, and flexible to map to the specific controls a business needs to comply with without any additional coding. How did you decide to build with MongoDB? Ashish: Easy and intuitive search support, as well as indexing and automated performance suggestions, were the key drivers for us building with MongoDB. Also, training new developers is very straightforward. What has your experience been like scaling with MongoDB? Ashish: Scaling is pretty seamless with MongoDB. Setting up alerts and monitoring is very straightforward. We've had nothing but great experiences so far. Do you have a favorite technical book or podcast that you would recommend to other tech entrepreneurs? Harshvardhan: I would recommend The Great CEO Within: The Tactical Guide to Company Building by Matt Mochary. That's definitely a great read. This is a bit of an open questions, so feel free to interpret it how you'd like. What are you currently learning? Harshvardhan: That's a tough one. I think you're always learning so many different things on any given day that it's difficult to give one answer. Today, I'm learning marketing strategies, like demand gen as well as sales tactics to scale the business. Ashish: Primarily, I'm learning how engineering can augment and support other organizations within the company. Who are some tech leaders or entrepreneurs that you admire? Ashish: I do admire Jeff Bezos quite a bit. I admire the laser focus that he has and the clarity in terms of his reasoning. Harshvardhan: Elon Musk because of his ideas and execution. One thing that's great about him is how he executes his ideas flawlessly. Interested in learning more about MongoDB for Startups? Learn more about us here .
For Banks, KYC Should Mean More than Just "Knowing Your Client"
Banks in the loan or mortgage business believe they know their clients well, yet they struggle to offer services that capitalize on customer data or tailor the loan origination experience to the individual based on the existing volume of information they have. That’s one of the key takeaways from Mortgages: A Digital Process to Be Mastered , a new report from MongoDB and FinTechFutures. The report, which surveyed 104 retail banking, business banking, and corporate banking executives, highlights that customer pain is particularly acute when — despite collecting reams of information about clients — banks and loan originators are still unable to turn around loan requests in a timely manner or offer personalized experiences. Click here to check out the Panel Discussion Do You Really Know Your Customer? According to the report, 61% of financial executives said they have industry-leading Know Your Client (KYC) processes. But at the same time, 43% also named poor digital experiences as a barrier to recruiting and retaining customers, with the inability to deliver personalized offers coming in second at 34%. Other issues commonly cited include the speed of innovation, the complexity of doing business, and the inability to serve customers in real time. So, do banks really know their customers? And if they do, what are they doing with that information if not using it to better serve their customers? What’s holding the industry back? Outdated processes, with agents and employees forced to grapple with manual processes, shuffling paper piles, and creating spreadsheets before they can get to work serving the customer. The market is crying out for better automated and data-driven decisions, and legacy systems can’t keep up. Besides the obvious waste of people resources, the lack of a holistic digital offering also hurts business. Customers increasingly cite easy to use and transparent mortgage processes, smooth onboarding, and digital processes as factors behind the lender they choose. Behind the Numbers Banks have made some progress digitizing and automating what were once almost exclusively paper-based, manual processes. But the primary driver of this transformation has been compliance with local regulations rather than an overarching strategy for really getting to know the client and achieving true customer delight. It’s a missed opportunity for a couple of reasons. First, banks create a comprehensive client profile during the onboarding process. They have enough data to perform risk assessments and personalize offers, but instead default to “new client onboarding” processes that are 30+ years old. The simple fact that the consumer is already a long term client with detailed information is ignored. The famous example is the ask for pay slips while the bank can see the actual monthly (or weekly) pay moving in to begin with. Second, most bank customers stay with their chosen financial institution for their entire lifetimes. And as the client relationship matures, the insight banks can bring to bear become deeper and richer. But a slow approval process (40%) and slow response times (39%) were two of the top areas in need of improvement cited in the survey . These are not the hallmarks of industry-leading KYC processes or deep client relationships, but of siloed data and misalignment of digital strategy. What we’re seeing is the difference between the practice of ensuring compliance with local regulations and a strategic imperative for truly understanding the client. While modernization investments have helped automate much of the paper-pushing related to compliance, transforming customer experiences and making LOS more transparent have yet to be achieved. Most executives in the survey planned on leveraging real-time analytics, AI/ML, and workflow software to improve processes. These are all technologies that can take KYC processes beyond simple compliance use cases and lead to more value-added, personalised client relationships. Boris Bialek, Global Head, Industry Solutions Smart Money Today’s clients are demanding fully modern, mobile-first banking experiences. To meet those expectations, bank executives and IT leaders plan to invest in technologies that can address some of their most glaring needs. Chief among them include getting away from manual processes like email and spreadsheets, better data analytics for decision making, and gaining access to real-time information at every touchpoint in the customer journey. The investments they’re willing to make include real-time analytics, artificial intelligence, and machine learning (AI/ML). Along with digital customer experiences (for example, chatbots and personalized recommendations), these are the three areas that bank executives and IT leaders say will drive greater market share and profitability in the loans business. So, even though many banks have started the journey toward modernization, they still have further to go before they’re able to meet the expectations of their clients. It’s not about reducing the paper-pushing or satisfying regulatory requirements involved with the LOS business. It’s about personalization and real-time experiences, hallmarks of true KYC. Mortgages are a Digital Process to be Mastered If real-time data and AI/ML are the way forward for driving value and transforming customer experiences, it will have to be accompanied by modernization of the underlying data architecture . As bank executives and IT leaders in the survey acknowledge , the lack of a digitization strategy, speed to market, and costly legacy migration are their top three concerns when digitizing their mortgage processes. A de-siloing of data and the introduction of data mesh concepts allows the leap from modernizing legacy infrastructure to digital transformation and competitive advantages. Banking innovators strive to be first to market, but legacy systems are holding them back, stymying digitization strategies. Overcoming these and other challenges requires the introduction of a modern data domain model that integrates the transactional and process workloads and augments customer data with information from other legacy and external systems. MongoDB Atlas is perfectly suited for this purpose. We have deep experience building customer 360 models that can be mapped to omnichannel interactions. In addition, MongoDB also has proven capabilities integrating risk and treasury functions (for mortgages this means funds transfer pricing and credit risk), with MongoDB Atlas being used by many banks and other financial service providers in the mortgage space, from building societies in the UK to special purpose lenders in Australia. Lastly, MongoDB’s ability to integrate mobile experiences, search capabilities, and real-time analytics (for example, scoring for consumer ratings while that consumer is on a web page) makes MongoDB the proven data platform for mortgage modernization and true digital transformation.
Make Migrating to MongoDB Atlas on AWS Easy with PeerIslands Modernization Tool Set
As cloud computing becomes commonplace across industries, organizations are rapidly adopting MongoDB Atlas because they know that true modernization is about more than just moving data as-is to the cloud—i.e. taking a “lift and shift” approach. It’s also about remodeling that same data along the way for faster and more iterative development. With MongoDB’s document-based database, developers are empowered to reimagine how they build with flexible schema design that allows them to easily model and remodel data for a wide range of use cases, while still applying governance when needed. MongoDB Atlas maps naturally to modern object-oriented programming languages, making developers' lives much easier. In contrast to the rigidity of SQL databases, MongoDB’s flexible data model means that your database schema can evolve with business requirements. This helps users build applications faster, handle diverse data types and manage applications more efficiently at scale. As a fully-managed service, MongoDB Atlas takes care of database maintenance for you and can also be scaled within and across multiple distributed data centers, providing new levels of availability and scalability previously unachievable with relational databases. The advantages of moving to MongoDB Atlas are clear, but some companies may still feel reluctant to leave behind the legacy relational databases they’re familiar with for unknown territory. This is where PeerIslands comes in. With PeerIslands, you don’t have to go it alone. The following blog introduces PeerIslands’ modernization capabilities, and how you can leverage them to migrate seamlessly to MongoDB Atlas on AWS. Why PeerIslands? PeerIslands is an enterprise-class digital transformation company composed of a team of polyglots who are comfortable across multiple technologies and cloud platforms. As a services firm, PeerIslands is focused on helping customers with both cloud-native development, and applications transformation. With best-in-the-industry talent, the team has helped several Fortune 50 companies bring large-scale transformations to life, and has received recognition from several clients and partners, including MongoDB. With engineers trained and certified in MongoDB, PeerIslands has helped MongoDB’s ISV and retail customers modernize, moving software built for on-prem to SaaS environments more conducive to cloud environments, and was named MongoDB’s Boutique System Integrator Partner of the Year . PeerIslands can swiftly transform and migrate core, legacy, and on-premises applications to the cloud. They develop solutions based on cutting-edge microservices and serverless architecture across public cloud platforms and hybrid PaaS platforms to help users quickly get applications to customers and business users. How PeerIslands can help PeerIslands has been working with MongoDB and AWS to develop tools that address two key objectives for customers: Objective 1: Tools that address common customer questions when evaluating MongoDB MongoDB Test Data Generator: A fully UI-driven tool with an extensive data library for rapidly loading MongoDB with use-case specific, near real-world data at scale MongoDB Performance Testing tool: A performance analyzer where you can create multiple load profiles, run-use case specific MongoDB queries and understand the performance of the queries. With the test data generator and the performance testing tool, customers can get a clear view of the performance of MongoDB for their specific situation even before migrating to MongoDB MongoDB Schema Generator and Data Modeler: SchemaGen tool helps to rapidly generate draft JSON schema from your existing SQL schema. On top of this, you can then perform the data modeling exercise and generate schema to form your MongoDB schema. The schema generator also provides key information about the SQL DB like size, index, and more MongoDB Sizer: MongoDB sizing tool helps you understand the size implications of your schema and calculate Atlas sizing. With the MongoDB sizer, customers can upload their own schema and calculate the various factors that influence the Atlas sizing Codescanner: A tool for scanning your code repositories for deprecated MongoDB APIs. With the code scanner, customers can get a clear view of the application impact for upgrading MongoDB versions Objective 2: Tools that accelerate time to value by rapidly moving workloads to MongoDB COSMOS2Atlas migration: A point-and-click solution that helps COSMOS customers migrate data from COSMOS to MongoDB. This solution provides change capture capability to ease downtime requirements and makes data migration easy and seamless 1Data: A tool for addressing more complex requirements of migrating data from SQL to MongoDB Admin mobile app: A mobile app for admins to track key Atlas KPIs and approve common access requests on the go PeerIslands brings to the table an entire suite of tools for addressing all your MongoDB needs. PeerIslands use-case featuring 1Data tool One of the key requirements of modernization projects is to solve large-scale data migrations from SQL databases. There are a number of tools that are available which simply replicate data from SQL to MongoDB—but, we rarely use the same SQL schema in MongoDB. Schema transformation—however difficult to do at scale—is nonetheless required so that we can make the best use of MongoDB capabilities. Today, the typical approach is to run custom Spark jobs as they are scalable and flexible when it comes to processing schema transformations and loading the data into MongoDB. But when you go beyond migrating one or two tables in a Proof of concept (PoC) setting, the problem becomes much more complex. For instance, writing custom Spark programs for every schema transformation is cumbersome and error-prone. For even simple migrations we will have tens of Spark programs. Any defects that occur during transformation are going to cause significant issues. Also consider the following challenges: How do you extract data out of your SQL database without impacting database performance? How do you handle infrastructure provisioning and scaling? How do you orchestrate the migration? Few master tables can be migrated once but transaction tables may need both one-time migration and a daily incremental migration. How can you do this orchestration at scale? How do you know whether you have not lost data during migration? Last but not the least, once a data is migrated how do you keep it up to date? We will probably end up with a suite of tools to address these issues–SQOOP, Kafka, Spark, some kind of a job orchestration engine, an observability suite, notification workflow and so on. It will quickly become evident that migrating data from SQL to MongoDB without disrupting business could be the most daunting barrier to adopting MongoDB. Unfortunately, current tools invariably fail for complex heterogeneous migration scenarios and developers end up writing a lot of custom code. Realizing this issue, PeerIslands has been working with MongoDB and AWS to develop 1Data. 1Data is a platform that helps enterprises perform migration and real time synchronization of data between SQL databases and MongoDB. 1Data is designed to complement existing AWS services like DMS in migrating data out of SQL. Key features of 1Data: Data is fully GUI based — There is no coding required 1Data provides a single platform for both one-time migration and continuous updates 1Data is consistent across one-time migration and continuous updates. This provides a good anti-corruption layer for continuous updates The tech stack of 1Data is based on Spark, Kafka among others and is highly scalable 1Data is highly modular and has a well defined API layer. 1Data can be easily extended to your needs 1Data automatically handles all the infrastructure required for migration with AWS quick start templates High Level Solution Architecture 1Data capabilities are realized through a decoupled and highly scalable architecture. The data extract, transformation and load part are independent of each other and can easily be customized based on the specific requirements of the customer. The architecture can orchestrate between batch-based initial loads and streaming-based CDC loads. A Spark, Kafka, and Airflow-based tech stack provides excellent scalability for the 1Data platform to handle large data migrations. Figure 1: 1Data High Level solution architecture OneData Portal structures migrations using Endpoints, Tasks and DAGs (Directed Acyclic Graphs) Endpoints define source, intermediate and final data locations and can come in the form of files, databases or queues. Endpoints can also be database extracts in S3 from AWS DMS service. Task definition is the second step in the migration. Tasks act on source point and produce data in either staging or destination end point. There are a number of predefined tasks available:Extract, Transformation, Sink and Validation tasks. You can configure both streaming and batch tasks. Defining the DAGs is the final step before actual migration. DAGs are used to define the sequence in which a user wants to execute the defined tasks. The technology components used in 1Data allows for easily handling very large data migrations. Each of the components has been selected such that they can be deployed across multiple cloud platforms and can be scaled easily. Technology Stack details below: Web Portal: Angular WebAPI: Node Configuration Database: MongoDB Data Transformation & Validation: Spark Data Extraction: Sqoop, Spark, DMS Change Data Capture: Kafka, Debezium Data Sink: Spark Job/Task Orchestrator: Airflow PeerIslands has worked with AWS and MongoDB to create a Quick Start for 1Data. With Quick Start, customers can rapidly instantiate 1Data for their migration requirements. To recap, with 1Data Quick start on AWS, we can Perform heterogeneous schema transformation from SQL and load data into MongoDB Atlas on AWS Weave together continuous data updates, incremental data updates and one-time migration using a combination of batch and streaming jobs Orchestrate the migrations tasks Validate the migration ...And all without writing a single line of code! Demo Looking forward A modern, data architecture can help you unlock your business’ full potential, and gain real-time access to the insights you need, when you need them. MongoDB’s document-based database and flexible schema design help you make smarter decisions, cut costs, and take full advantage of AI/ML capabilities to empower your employees and raise customer satisfaction. The decision to migrate off your legacy systems and onto MongoDB is easy—and now the process is, too. Let PeerIslands help you get there. Our best-in-class teams leverage next-generation technologies, including Artificial Intelligence (AI), Augmented Reality (AR), Blockchain, Internet of Things (IoT), Machine Learning (ML), Mobile, and Virtual Reality (VR). Our expertise spans the modern programming stack, and we follow best practices in distributed, agile, and lean principles as well as test-driven development and DevOp. Additional Resources ISV WMP Program Contact aws-isv-workload-migration@amazon.com for details Atlas Quick Start MongoDB Atlas Starter Package Atlas Migration Guide Atlas Migration Pattern Contact us with any questions around modernization with MongoDB, AWS, and PeerIslands.
How Legacy Modernization with WeKan and MongoDB Atlas Helps Meet Evolving Consumer Demands
COVID-19 has accelerated the growth and adoption of digital economies across the globe, and the businesses best positioned to keep pace with related changes in consumer behavior and demand will continue to gain a competitive advantage in the marketplace. According to a consumer study by FIS Global that surveyed participants to understand changes in recent buying behaviour and patterns, consumers have spent 58% more money online since the pandemic started. What’s more, 42% of respondents stated an increase in purchases from local/independent small businesses, and 27% of consumers have subscribed to one new online streaming platform. Large institutions and household brands can not risk complacency if they want to maintain market share. The customer loyalty of today will be captured by the companies that act with agility and optimize data to deliver the most seamless, custom experience for consumers. Unsurprisingly, business models that have prioritized and directed resources towards aligning their processes with digital transformation are better placed to deal with customer behaviour shifting into the digital realm. And yet, innumerable businesses are plagued by the limitations of their legacy IT systems when trying to modernize their digital experience. For many organizations, legacy systems are seen as holding back the business initiatives and business processes that rely on them...when a tipping point is reached, application leaders must look to application modernization to help remove the obstacles Stefan Van Der Zijden, VP Analyst, Gartner Continued use of these systems holds back businesses’ potential for revenue generation and building customer-facing credibility; but modernizing them reaps worthy rewards. Luckily, you don’t have to go it alone. This post will detail how organizations can undertake this modernization process, often termed “legacy modernization,” so as to leverage the speed, agility, and responsiveness required to succeed in a digital-first marketplace. What is legacy modernization? Legacy modernization refers to the process of updating an organization’s antiquated IT stack to align with new-age business goals and workflows. To drive innovation, business leaders need to be supported by technology that can help implement their goals in the real world. They need fast-paced, highly connected systems with minimal-to-zero downtime, and platforms or dashboards that provide cohesive and easily comprehensible views of the entire ecosystem. Generally, legacy IT stacks are incapable of meeting these standards which is where legacy modernization comes in. Defining legacy systems and 4 major drawbacks Essentially, a legacy system is any software or technological system that slows down an organization’s business growth and its ability to shift and adapt to changing market forces. If a software setup is unable to integrate with newer systems, workflows or processes, it qualifies as “legacy.” Generally the incompatibility of legacy technologies, and the bottlenecks that come with them, lead to major issues related to maintenance, support, updates, integration and overall user experience. Think of it this way: using a legacy system in 2021 is comparable to driving a Prius with an engine made in 2000. Legacy solutions lack flexibility and carry a significant technology debt due to dated languages, databases, architectures, and a limited supply of aging baby-boomer programmers. a Deloitte Study on Legacy Systems and Modernization The business impact of legacy systems are varied, but often adverse. They include: 1. Inability to act with agility and meet demand Generally, legacy systems can only be accessed from office computers. But in a digital-first world, mobile devices are at the core of digital transformation. If employees cannot access necessary software from anywhere at any time, their productivity and operational capacity is severely limited. The link between software and employee performance has, in fact, been well-documented . For instance, in 2015 , a computer running a 23 year-old operating system (Windows 3.1) caused planes to be grounded at Paris’ Orly airport for several hours. Needless to say, customers were not happy. 2. Decreased employee productivity and customer satisfaction Everyday people are at the heart of digital transformation. If a business wants to attract and retain customers who are increasingly reliant on their internet-powered mobile devices for day-to-day activities and transactions, they have to meet them online. And if they want to attract top talent, they need to equip their employees with the tools and agility needed to innovate. Being saddled with legacy systems will prevent companies from using newer apps and providing the best possible customer service, support and experience. Additionally, sub-par employee performance and customer service will inevitably cause financial loss due to unsatisfied customers and missed opportunities for expansion. 3. Scalability issues and security risks Legacy software is usually incapable of scaling up, which poses major obstacles to business growth. In a competitive marketplace, businesses must be able to shift strategy and optimize according to market forces, for which they need the support of their IT stack. An excellent example of this is how companies have had to adapt to remote work becoming the ‘new normal’ due to the global pandemic. The IBM 2020 Cost of a Data Breach Report puts the average cost of a data breach at USD 3.92 million. Legacy software almost always has glaring flaws in its security mechanisms for multiple reasons: withdrawal of manufacturer support, lack of updates and regular maintenance, difficulties in fixing vulnerabilities within outdated systems. Issues like security breaches will significantly harm brand credibility and repel customers from entrusting the business with their data. 4. Higher costs Administrative, support and maintenance costs are unnecessarily high when companies have to work with legacy software. Additionally, hiring and training new employees, especially developers, is difficult since there is a shortage of coders trained in legacy languages like COBOL and Natural. Most legacy systems are hosted on premise, which translates to enormous and unnecessary overhead related to maintenance and upgrades. These costs are easily eliminated by leveraging cloud computing platforms like AWS, Google Cloud, or Microsoft Azure. Despite these glaring inadequacies, the pandemic has revealed how far too many organizations continue to rely on aging IT systems. In a 2020 AppDynamics Report , 66% of technologists say “the pandemic has exposed weaknesses in their digital strategy, driving an urgent need to push through initiatives which were once a part of multi-year digital transformation programs.” A roadmap for legacy modernization The journey to legacy modernization can be an intensive one, but there are proven best practices and expert guidance to help you get started. Galvanize the key players in your organization and get started by asking the right questions: What resources can be assigned to the modernization endeavour? Do your employees possess the skills to operate the new systems? What are the specific competitive advantages that modernization needs to provide for your organization? Is there a separate support and retirement schedule in place for your legacy system? Should modernization occur in a single shift or in phases? How will this affect our business? Escaping the pressures imposed by unwieldy tech stacks has become possible with microservices and cloud-based application development and/or usage. The trick lies in decentralizing business tech offerings by migrating them from Relational Database Management Systems (RDBMS) to the Cloud via scalable solutions like MongoDB Atlas , MongoDB's hosted database-as-a-service offering. Moving from monolith to microservices architecture can be complex, but offers multiple long-term advantages across multiple parameters. Refactoring monolithic systems requires carefully constructed strategies, the most successful of which are drawn from the Strangler Pattern approach . How do we modernize from existing legacy systems? Initiate new functionalities as microservices: Every time a business has to implement a new functionality or feature, they can incorporate it as a microservice instead of adding it to the existing monolith architecture. Not only does this prevent the legacy stack from expanding, but allows stakeholders to become acquainted with the advantages of microservice ecosystems. Dismantle the monolith: Once microservices have been introduced into an organization’s ecosystem, monolith structures need to be deconstructed for eventual elimination. Companies like FedEx and CitiBank have attested to the success of a microservices-based strategy with real world implementation. To quote FedEx CIO Rob Carter , “We began to build out the services and microservices that represent the less complex, more flexible, faster-to-market capabilities that we have today.” CitiBank, too, opted for migrating its monolith system to a microservices-based architecture so as to accelerate digital transformation. How WeKan and MongoDB Atlas can help Implementing successful, sustainable and scalable legacy modernization requires expertise in executing on the process itself, as well as the right tools that can understand and adapt to an organization’s unique needs and business goals. Databases and platforms like MongoDB and its tool suite help address the challenges of replatforming from monolith to microservice. MongoDB Atlas is the leading choice of general purpose databases for modernization. As a document-based, distributed database, MongoDB reduces time spent on development cycles and empowers developers with flexible schema and the tools they need to maintain productivity. A leap forward from traditional RDBMS, MongoDB Atlas's smart infrastructure helps organizations scale effortlessly and maintain business-critical reliability while driving lower TCO, reducing security risk, and remaining ACID compliant. Complementary to MongoDB, WeKan’s Modernization process is composed of 5 phases that aim to scope an optimal modernization journey for any business operating on legacy systems and looking for a better return on their technology investment: Diagnosis phase – The first step is to understand the current state of the business, its most critical pain points and identify major inefficiencies that can be solved through technology modernization. Prescription phase – With a good understanding of the business’ state, we propose reference solution architectures that can address most critical pain points and enhance overall performance of their technology ecosystem with a focus on always reducing the total cost of ownership (TCO) and increasing ROI on their technology spend. Validation phase – After gathering potential solutions, we then validate through POCs their tech viability, expected outcomes and leverage results from these efforts to narrow down and select the option that is best suited to the business’ needs. Requirements definition phase – With a target solution in hand, we work on defining the technical requirements and specifications of the proposed solution to ensure seamless integration to the overall technology ecosystem. Execution and Implementation phase – With the right solution architecture, technical requirements in place, and a proposed modernization plan, our modernization consultants work hand-in-hand with internal stakeholders on the development, testing, delivery and implementation of the proposed modernized solution. According to the World Economic Forum, digital transformation could generate more than $100 trillion by 2025 . Without legacy modernization, businesses will miss out on tapping into revenue streams offered by the digital economy. It is integral for organizations to leverage the many advantages of modernization so that they may gain and retain a competitive edge in a constantly connected and perpetually online marketplace. To learn more about WeKan and MongoDB Atlas's efficacy in organization-centric digital transformation, refer to our case study with RideKleen. After migrating operations to AWS, WeKan chose MongoDB Atlas, Atlas Data Lake and MongoDB Realm as their central data platform. Atlas offers a fully managed cloud database service with built-in automation, Atlas Data Lake provides federated query capabiliites to natively data query across MongoDB and AWS S3, while MongoDB Realm simplifies the critical edge-to-cloud sync and provides backend services to speed development work, including triggers, functions, and GraphQL. RideKleen case study Watch how MongoDB’s industry-best modernization services helped OTTO, Germany’s #2 global e-commerce provider and #1 site for e-commerce, fashion and lifestyle. Learn more about our Modernization Program Learn more about WeKan
Built With MongoDB: FanPlay
Pritesh Kumar and Bharat Gupta co-founded FanPlay Technologies at the beginning of the pandemic that shook the world in 2020. With their real money gaming (RMG) product, they’ve joyfully brought thousands of people together across India in a safe way, while establishing the country’s leading gaming app. For this segment of #BuiltWithMongoDB, we spoke with Pritesh about their company’s business model, how MongoDB is working to their advantage, and what celebrities are already utilizing their platform. MongoDB: What prompted you to build FanPlay? Pritesh: The emergence of COVID-19 really prompted me into the startup world again. I’ve been a founder in the past, and I knew that at this time a lot of new companies would emerge, so I decided to be part of that. The idea for FanPlay came from observing Cameo . I was really impressed by its strong viral growth and its monetization of influencers. I think these micro influencers on the platform, although they don’t make a lot of money for a single video, can add massive value to any business. And at the same time, we were looking at the RMG industry, which was and still is the fastest-growing space in online gaming. But there is a real problem of very high customer acquisition cost. So, we put one and one together and started building an influencer-led, RMG platform. We get influencers to host real-money trivia games for the fans and followers on our platform. Typically these influencers promote their own shows on their social media platforms. They gather an audience from YouTube, TikTok, and various other channels, and then they come to our platform for the gaming experience. The audience usually pays a small entry fee. From that entry fee, a prize is created, that prize goes to the winner of the game, and from that prize we take a cut. So this is our business model. MongoDB: What was your initial vision for the product, and what does it look like today? Pritesh: The product has changed a lot from what we initially envisioned. We started with a web app initially because we thought that acquiring users on the web would be much easier, but then we launched our free Android app and it did very well. From there we launched our paid-entry model. So the product has gone through three iterations so far. In the beginning we worked a lot with Instagram influencers and realized that we needed to be working with influencers on YouTube, and specifically with people more regionally significant to India, where most of our business is at the moment. We have also expanded to hosting established faces from Instagram and YouTube. MongoDB: Can you tell us about the scale of the platform? Pritesh: Currently we work with about 500 influencers that have a lot of visibility, and we host roughly 20,000 active users daily, from India. Typically we run about 20 games per day, and we’re working to scale that to 100 per day. MongoDB: What does your tech stack consist of? Pritesh: The app is built in React Native, and the back end is Node.js. Then of course for a database we use MongoDB. MongoDB was a very clear choice for us. From a professional standpoint, as an early-stage startup, you don’t know what your product will eventually turn into, right? How will it evolve in the next six months or a year? So it’s difficult to stick to a schema. Therefore, you need a lot of flexibility. Because of our need for flexibility, SQL was out of the question, so we needed to go with NoSQL. Once we decided on NoSQL, MongoDB became the obvious choice because of the community support and documentation. As a founder, I believe in really fast execution and putting your product out there, rather than waiting for a pitch-perfect product. And that demands a lot of flexibility from the business, product, and tech sides, because we need to be able to make immediate changes based on the features that are demanded and that catch the users’ attention. With MongoDB, we are able to try a lot of product variations or tweaks very quickly. MongoDB: As you've scaled, is there a particular MongoDB feature you've benefited the most from? Pritesh: There are a few features of MongoDB Atlas that have benefitted us a lot. One is the performance metrics. It’s really really amazing, actually. You can get a very clear picture of the state of your database in a single snapshot. It helps you buy time to focus on shipping your core product and the technology behind it. It removes your focus on database management and cluster management and just does it for you right out of the box. Also, Atlas handles all of the sharding and scaling. And something that I didn’t foresee but found very useful is its scalability. Startups tend to start at a scale where the free version of any cloud product would be good enough, right? But then you quickly move into a very different kind of need and scale. It just keeps on changing! Atlas gives us that flexibility to scale up really quickly with a very minimal amount of effort. MongoDB: Have you used any of the MongoDB for Startups services? Pritesh: Yes! We had a session with a technical advisor. I found it really helpful for addressing the key features we are launching in the future, and the main challenges we are going to face when building them. I was able to discuss those and was very satisfied. The session was really good for us. MongoDB: Who is the most well-known celebrity to have hosted a game so far on FanPlay? Pritesh: The comedian Kumar Varun ! MongoDB: Who is your favorite TV or game show host? Pritesh: Amitabh Bachchan , who is a household name in India for his acting and for his role as host of Kaun Banega Crorepati (India’s Who Wants To Be A Millionaire). MongoDB: What is your favorite podcast or blog? Pritesh: The InfoQ Podcast . It goes deep into how organizations build challenging tech products. Looking to build something cool? Get started with the MongoDB for Startups program.
Built With MongoDB: Antler
Antler is a global early-stage venture capital firm that invests in the defining technology companies of tomorrow. The firm has offices in 13 cities worldwide, across six continents. Founded in Singapore in 2017, Antler is on a mission to fundamentally improve the world by enabling and investing in the world's most exceptional people. Since its launch, Antler has invested in and helped build over 250 companies. Antler enables exceptional people to build impactful technology startups by building complementary teams, supporting the teams with deep business model validation, and providing a global platform to scale their startups — and that’s why we’re thrilled to announce the firm’s partnership with MongoDB for Startups . We recently touched base with Antler Partner Björn Lindfors to talk about entrepreneurship, building and managing new companies, and partnering with MongoDB. Björn arrived at Antler after an incredible entrepreneurial journey of his own that included launching a web design studio to fix “crappy websites” during his university days, learning the ropes at Google, and serving as an executive of two companies in Singapore. Antler has raised $78 million and has offices in 13 locations across the world. MongoDB: How does Antler differentiate itself from other accelerator programs? Björn: We’re technically not an accelerator. We’re even earlier than that: we bring together between 70-100 top performers who may not even have an idea yet for phase 1 of the program, where they collaborate with other aspiring entrepreneurs to come up with an investment-worthy idea they can present to our Investment Committee. We’re very hands-on in our approach to venture building: We have a range of technical and business advisors who coach the entrepreneurs and follow their progress. For example, I’m still in weekly contact with many companies that we invested in during our 2018 sessions. We’re builders ourselves, and we want to keep adding value and serving as sounding boards for our community members. MongoDB: What do you recommend to your CTO founders when building out their companies? Björn: The first thing I encourage them to understand is that product isn’t everything. Building your product shouldn’t distract you from other things that are incredibly important for a successful business — for example, how to effectively run an engineering organization, how to hire good people, and how to make sure that your team members are constantly happy. The real struggle for me in transitioning into a CTO role at one of my former companies was the management aspect. I had completely underestimated the human elements and how much time and effort you need to spend to ensure that people are feeling happy and productive. I always tell people to focus more energy on that. MongoDB: Have you personally used MongoDB in your past companies? Björn: Yes, I have used MongoDB quite a bit, especially during my consulting years. If you’re building something quickly and don’t quite know where to start, MongoDB is the perfect partner. MongoDB is convenient and flexible enough to adjust as you transform your prototype into something more permanent. The first iteration of whatever you build is likely 80% wrong anyway, and you don’t want to be stuck with something clunky. For most consumer apps and business use cases with simple business logic, MongoDB works great. MongoDB: How has the Antler community responded to the MongoDB for Startups partnership announcement? Björn: When it was announced on our internal Slack group, which consists of thousands of people from our alumni and portfolio globally, there was a lot of excitement. MongoDB is part of the default stack that startups use to build businesses. I get questions from founders constantly about how to build, and I constantly advise them to use MongoDB. There’s no point in people teaching themselves something else for the purpose of building when MongoDB is the perfect default kit. We are excited to deepen the partnership over time. MongoDB: Why should aspiring founders work with Antler? Björn: If you’re driven, curious, and passionate about changing the world in some small, weird, and wonderful way, we are the right fit for you. We back founders working in all sectors and provide the right resources, mentorship, and community to help get their businesses off the ground. MongoDB: What do you think the future of Antler looks like? Björn: I think it could evolve in a few different ways. If you look at the entire investment landscape, at least here in Singapore and Southeast Asia, the seed investment sector has become very competitive. Seed investors are competing for deals and therefore have to provide strategic value along with funding to their portfolio companies in ways that they didn't have to before. Good entrepreneurs now have options, so the pressure is on investors to provide a lot of additional strategic value. Similarly, we’ll keep innovating on the services we can provide founders to help them be successful in this changing tech landscape. Interested in learning more about MongoDB for Startups? Learn more about us here . Special thanks to Andrew Bell for his help in compiling this piece!
Built With MongoDB: Vectorly
When Sam Bhattacharyya spent time in the Peace Corps as a teacher in Mexico, he learned how much of a barrier the lack of internet bandwidth was for his classes. The students simply did not have the resources needed to take advantage of online learning, which was a problem Sam soon became fixated on. Years later, Sam founded his company Vectorly with a goal to fix that bandwidth issue via an AI-based video compression solution that streams low-resolution videos and turns them into a high-definition viewing experience. Vectorly is a software development kit (SDK) that companies can integrate into their video applications. Vectorly released its minimal viable product (MVP) for use by early customers in February 2021 and has a total of 20 companies that are actively using the product. In this edition of #BuiltWithMongoDB, we talked with Sam about how Vectorly’s software works, how he got started with MongoDB for Startups, and the future of this fast-growing industry. MongoDB: What's Vectoryl's mission? Sam: We’re building a technology that uses artificial intelligence (AI) to upscale and enhance video in real time on users’ devices, as they watch it. So, what that lets a user do is stream low-resolution video content and watch it in high definition. We have about 100 AI models on our server. Most of them are for AI upscaling, for different kinds of content and different quality levels. Based on feedback from customers, we've also been building AI filters for, say, virtual background replacement. All that data is loaded in real time from the server every time you load the library. With our SDK, you specify that you want to use this AI filter on that library, and you have an API token that calls our API and that returns the AI model in real time to your device so you can watch the upscaled video. AI takes some computing power, which can be a concern especially on low-end devices, and we’re conscious of that, so we pay close attention to performance and frame rate to make sure our AI models do not overload the devices users are working on. MongoDB: What are some of the use cases for Victory? Sam: The first is to think of a user that is watching Netflix with a slow internet connection. Because the network is so slow, that user’s going to end up with a low-resolution version of the video. But we have AI filters that can pop in and start to upscale and enhance the video and make it look as if it’s high definition. The other use case is around video conferencing, where all kinds of things can affect call quality or user experience, from background noise to blurry video. You can use AI to correct any of those issues that come up. MongoDB: What does your tech stack consist of? Sam: Our product is a software library, which is for the web, and it’s all built in JavaScript. The main JavaScript functionality we’re using is called WebGL, which is a graphics pipeline that lets you access the GPU on devices. We have a bunch of AI models on our server, which are just numbers stored in JSON files. Our SDKs load the AI models in real time, and we use MongoDB to track and store event data, as well as basic metadata. MongoDB: How did you choose MongoDB? Sam: I've been using MongoDB since I started programming in 2012. Although the first programming course I took used the LAMP stack (Linux, Apache, MySQL, and PHP), SQL seemed unintuitive, and the LAMP stack in general just felt bulky. When I started my first personal programming project, I looked for alternatives, and I found this new thing called the MEAN stack (MongoDB, Express.js, AngularJS, and Node.js). I thought it was the greatest thing in the world that you could use JavaScript in the front end and the back end, and that you could even use JSON like notation for the database. Having a full JavaScript stack made so much sense. Every web development project I've started since has used the MEAN stack. When it came time to hack together the first version of Vectorly, MongoDB was our first choice for the database. MongoDB: How has the experience been working with MongoDB? Sam: It’s been fantastic. We had to come up with this model of tracking users and usage of our platform in a very short amount of time, because the first version we released had no tracking whatsoever. One of the things that saved us a lot of time was the MongoDB Charts function, because it really allows us to track what we’re doing. It was super quick to set up. Looking to build something cool? Get started with the MongoDB for Startups program.
Built With MongoDB: Phable
Hundreds of millions of people across India face chronic diseases. India has the second-highest number of diabetics in the world, and citizens with high blood pressure, thyroid conditions, and other chronic ailments are underserved in the country because there’s no robust system in place governing how the treatment and diagnosis will be handled. Given the lack of a proper infrastructure, diseases slip under the radar because they’re not caught early on. That’s where Phable comes in. According to TechCrunch , "Phable has created a more transparent and real-time communication channel that allows a doctor to nudge their patients to take their medicine on time, and make any necessary changes to the lifestyle or medication cycle, or request a follow-up appointment. The app itself can be used for tele-consultation, the demand for which has skyrocketed in recent quarters as coronavirus forced people to stay indoors.” The company, which has raised $12 million in funding from India’s Manipal Hospitals and venture capital and investment management firm SOSV, reaches 350,000 patients, 5,000 doctors, and a staff of 100 people across Chennai and Bangalore. In this edition of #BuiltWithMongoDB, we talk with Phable’s Creative and Marketing Consultant Ganesh Chandrashekar and Engineering Manager Venkatesh Walajabad about what drives their business. MongoDB: What is the Phable product offering right now? Ganesh: We currently have two products: patient facing, and doctor facing. For patients, we give them a sense of their everyday health and handhold them through the process of understanding their first symptoms, getting prescribed a treatment plan, and recording their ongoing lifestyle changes. We help map and manage those lifestyle changes at a fundamental level, while giving them intelligent insights to help them make small tangible changes to everyday habits. We also connect them to doctors in a more real-time manner, so doctors have deep visibility into a patient’s health, and the patients can get personalized recommendations from doctors. While our focus is on preventive and personalized care, we have some value-added services that ease our users’ journey. They can order medicines from the app, schedule video consultations with doctors, and request lab tests directly. We’re building a broader health tech ecosystem where we are able to partner with the relevant companies — including some leading names in health device manufacturing, insurance companies, and medicine providers. For doctors, we’ve built a full product suite with a decision support system and EMR. So we’re able to help them digitize their practice, prioritize and process patient data, simplify clinic management and build better relationships with their patients. MongoDB: Has COVID-19 impacted product adoption or any of the features that are being used? Ganesh: Our growth has been in parallel with the pandemic over the past year. The pandemic gave a sense of urgency, and put the spotlight back on healthcare and understanding health at a more granular level. A lot of the new features we have — especially virtual doctor consultations — were developed at a breakneck speed to cater to users at home during the pandemic. MongoDB: You released the video consultations really quickly, especially given the uncertainty surrounding COVID-19 in 2020. How did you approach that from the technical side? Ventkatesh: Because there were a lot of unknowns in building this, we wanted to experiment and release in certain phases so we could gather feedback and then add features on top of that. We are quite nimble at Phable as a whole: we started with consultations, moved on to an ecommerce platform, and then added wallet features. Similarly, for the video consultation product, we released in chunks — experimenting with users, analyzing their usage, and then shipping the feature more widely. MongoDB: How did the team decide to build with MongoDB? Venkatesh: The decision for MongoDB happened right from Day 1, because the team wanted to go with a MEAN [MongoDB, Express.js , AngularJS , and Node.js ] and MERN [MongoDB, Express.js, ReactJS, and Node.js] stack. Initially we used the community version, and then early last year we shifted to MongoDB Atlas. We wanted to use all the clustering capabilities and backup support, in addition to the profiling and detection of slow queries. We use a lot of those features to figure out where our bottlenecks are. We got some credits through MongoDB for Startups, but MongoDB Atlas is still on the more expensive side for us. Even though it is a little expensive, the advantages that we get from MongoDB Atlas far outweigh the cost. We use AWS for our server needs, and we have a fair bit of integration between AWS and MongoDB via VPC peering so all data is more secure, in addition to the encryption MongoDB provides. MongoDB: How is your engineering team structured? Venkatesh: There are 18 engineers on the team — and we’re trying to add more so we can launch more features and expand into new markets. Readers take note: We are hiring engineers for our India offices! MongoDB: How has scaling with MongoDB been, especially given how much you've grown during COVID_19? Venkatesh: MongoDB Atlas takes care of all the autoscaling for us. We worked closely with a consultant to figure out what minimum and maximum instances we need for our clusters, and then we rely on MongoDB to do the autoscaling. During a calmer period, MongoDB Atlas scales down perfectly well and reduces the costs; in a high-growth period, it scales up to accommodate for the traffic. We love that it automanages things so we don’t have to worry about it day to day. Because MongoDB’s features take care of most of the work, we don’t need a dedicated person to oversee this — we plan a few months ahead, and then we let MongoDB take care of the work. Looking to build something cool? Get started with the MongoDB for Startups program.
Built With MongoDB: Milky Way AI
Sagar Setu received his PhD in helicopter flight dynamics and has a fascination with deep learning and integration within the field of aerospace. However, helicopter flight dynamics is not what Sagar is involved in today. Through Entrepreneur First , an international program that helps entrepreneurs launch companies, Sagar met Eunice Wong , a fellow aspiring entrepreneur, who introduced Sagar to the world of retail, which he calls a “fantastic playground” for any engineer to be involved in. The pair founded Milky Way AI, with Wong as CEO and Sagar as CTO. Milky Way AI is designed to empower the largest retailers and brands globally with real-time visibility into how their products and their competitors’ products are being merchandised across thousands of stores. In this edition of #BuiltWithMongoDB, we chat with Sagar about the ways Milky Way AI creates opportunities for retailers, his favorite MongoDB features, and how the competitive AI industry keeps him motivated. MongoDB: What does the product look like now, and how does it work? Sagar: Our flagship product is called InstaShelf. It’s a mobile app that we put in the hands of distributors and merchandisers. When there is a person stocking the shelves and we put the app in the hands of that person, they are able to snap a photo, which then goes through our proprietary computer vision engine. This generates a variety of insights that are valuable for both the distributor and the brands. We are looking into how we can share this same data with and make it equally valuable to the retailers. We developed this product over the last year and launched our beta three months ago. Since then we have gotten quite good traction in terms of users in a number of countries that are deploying the product. MongoDB: Let's talk about that traction. How far along are you? Sagar: We started in January with a 15-store pilot for Kelloggs in Singapore. We have deployed across 150 stores now, and we are set to deploy across a few hundred more in Malaysia and the Philippines. By the end of next year, we hope to be in three more countries — just with Kelloggs. The typical number of users for each of our pilots is around 15 to 20 merchandisers visiting between 50 and 60 stores. In a typical audit, the user takes 10 to 15 photographs and our AI identifies what's on the shelf from these pictures, reporting on key metrics such as a brand’s share of shelf compared with a competitor brand, products that are out of stock, product placement compliance, and so forth. MongoDB: What does your tech stack consist of? Sagar: The web and mobile components of our solution are built using MongoDB, React Native, React, Node.js, and Python Flask. The computer vision pipeline is built on both TensorFlow and Pytorch. We use MongoDB for all our database requirements — transactional and analytical. Our top criteria for choosing the tech stack were proven scalability and stability, and a wide developer pool. It was important in the early stages to keep the team lean and the product flexible, and the choice of MongoDB Atlas turned out to be a great one. The support for being schemaless was crucial in allowing us to stay nimble as we learned the nitty-gritty of the domain. With features such as triggers and BI Connector, we could orchestrate various components of the solution right from the Atlas GUI, saving us hundreds of working hours. MongoDB: What are some of your favorite features in MongoDB? Sagar: My favorite feature is the support with autoscaling, which is the primary concern if you’re building anything into production. I’ve never had to worry about that. I don’t even think about it; I have just turned the features on, and it allows so much creativity. With MongoDB Atlas, I have peace of mind. MongoDB: What is something that you are learning right now? Sagar: Learning is a constant, working in the field of AI. A wonderful plus point is you always have so much competition: there might be a paper tomorrow that basically undoes everything you’ve done — something comes out that is far superior to the method you just took two months to deploy. So I’m always reading, learning, and trying to improve our solutions. MongoDB: What’s one of your favorite books? Sagar: The Selfish Gene . It’s not exactly technical, but more on the scientific side. That’s more of my kind of read. I really like the thought process the book instills in you. It gives you an understanding of the world — the good, the bad, and learning not to take things personally. Looking to build something cool? Get started with the MongoDB for Startups program.