Gabriela Preiss

4 results

Unqork + MongoDB: Use a Single View of the Customer to Enhance the User Experience

To remain competitive in financial services, companies must find new ways to optimize the customer experience as a means to boost loyalty and capture market share. One of the biggest challenges companies of all sizes face is unifying disparate data sources to create a single view of the customer. Unification enables companies to efficiently surface relevant customer information and data-backed insights to inform strategies, and build personalized customer experiences to delight end-users and optimize processes. Unfortunately, unification is easier said than done. Many firms struggle with disparate data in siloed systems across the organization. That is where MongoDB and Unqork can be a game changer. With the combination of Unqork’s industry-first no-code application platform and MongoDB’s flexible application data platform, financial services are able to ingest data from multiple upstream and legacy sources at scale, unify them into one central data platform, and push them downstream to internal applications or specialized third-party services. Unify your data, enhance the customer experience With MongoDB, you can build a single view of anything faster with a smaller investment. A single view application aggregates data from multiple sources into a central repository to create a holistic view of anything. You can integrate data across the enterprise, whether to provide a firm-wide view of asset and counterparty exposure or a single view of your customer for fraud detection and Know Your Customer (KYC) requirements. MongoDB provides: Data agnosticism: MongoDB can incorporate any type of data, no matter the source, while providing all the features needed to build a powerful application. Accelerated development: Your teams move faster with MongoDB because its dynamic schemas let them iterate changes quickly. They spend less time prepping data for storage, and more time pushing the project forward. Improved search and filtering: MongoDB's expressive query language, indexing, and aggregation capabilities make it possible to find and filter the data, no matter how the business needs to access it. The Unqork platform empowers organizations to rapidly build custom applications in complex, highly regulated industries such as financial services. With Unqork, organizations can: Integrate disparate ecosystems: Seamlessly connect new custom applications with third-party solutions and legacy systems to streamline the entire client experience — from onboarding to transacting — improving risk decisioning while reducing costs by up to 30%. Orchestrate complex business processes: Create, connect, and automate complex, customized workflows to design unified, omnichannel experiences for clients, reducing manual processes by up to 50%. This is particularly apt when it comes to complex, multistep processes such as lending, transactional workflow hubs, onboarding, and KYC. Create branded digital experiences at scale: Today, clients expect Amazon-like, digital-first experiences that are intuitive and easy to navigate. Unqork’s digital portals for retail and institutional clients empower firms to create compelling, on-brand experiences for all end-users, delivering enhanced reporting and self-service capabilities to address the expectations of a new digital age. By combining the accelerated development of Unqork with the unified single view functionality of MongoDB, companies can effectively use data to enhance user experiences and secure market share. Success story Faced with exponential growth, one of the world’s largest crypto-currency exchanges found itself unable to effectively manage demand with its current technology stack. Other legacy solutions were unable to help it optimize the onboarding of new institutional clients, resulting in a queue of nearly 2,000 investors, 200 of whom the company considered VIP. To effectively address this peak in demand, the exchange tapped into the power of Unqork + MongoDB to reduce its KYC verification process from weeks to days (and in some cases mere hours for low-risk clients). This acceleration was made possible through the automation and orchestration of integrations across multiple vendors and internal systems. Using the two platforms, the exchange transformed its customer onboarding function complete with: Enhanced digital client experience: Modern, intuitive client portal with integrated chat and video tools to boost collaboration between clients and agents Intelligent workflows: Dynamic workflow engine supporting customized client journeys based on entity type, product, and jurisdiction Improved transparency: Seamless information flow, handoffs, and real-time tracking across internal teams and clients Integrated ecosystem: Integrated ecosystem of best-in-class technologies to prefill/validate data, enabling exception-based processing Risk-based compliance: A risk-based approach to optimize compliance approvals with fully integrated audit trail Key benefits: Platform integrations: Seamless integration with various KYC/AML vendors into the app Real-time analytics: Any variety of data type analyzed in real time without excessive infrastructure, such as warehouse loads, and auto-scaling to adapt to whatever size (increasing/decreasing) of data the moment demands Speed-of-delivery: Custom KYC verification solution delivered in 16 weeks Flexible compliance: A flexible tech stack to empower the organization to future-proof against evolving regulations and compliance concerns since crypto is vulnerable to misuse and other KYC/AML challenges And this is just the beginning. With the exchange’s firm-wide customer-first initiative, Unqork + MongoDB will become the central orchestrator across all products and locations to create a unified, end-to-end client onboarding experience. The potential with MongoDB + Unqork The potential business value is enormous. When correlating shareholder value with its customer-experience benchmarking, McKinsey found that, between 2009 and 2019, digital leaders delivered 55% higher shareholder returns than customer-experience laggards. Benefits include: Lower administrative costs. The potential to reduce costs via digital self-service is enormous. In addition to driving down costs-per-transaction by up to 99%, Gartner estimates that 40% or more of today’s live-call volume could be handled via self-service channels. Increased customer loyalty and retention. Today’s customers have little patience for slow, opaque processes that require lots of back and forth with intermediaries. The vast majority (67%) of customers prefer self-service, according to a Zendesk study. With MongoDB + Unqork, organizations can rapidly build powerful self-service functionality that provides their customers with 24/7 access to information and services. More granular customer insights. By centralizing customer interactions and the data they produce, firms gain valuable insight into customer behaviors to help improve the client experience, unlock cross-sell and upsell opportunities, and guide future iterations of digital experiences to further enhance customer experiences and, ultimately, retention and customer lifetime value. With MongoDB + Unqork, financial firms gain the flexibility required to unify client experiences and resulting data to future-proof firms to agilely pivot to address changing client and regulatory needs — at speeds that outpace their competitors.

March 8, 2022

Manufacturing at Scale: MongoDB & IIoT

In recent years, we’ve seen a massive shift in digital transformation. As people, we’re all “connected”, by our smart devices, smart homes, smart cars, smart cities, and so on. We interact with smart devices because of the convenience it offers us- automating daily tasks and giving insight into daily movements, from how much pressure is in each car tire to whether we left our stove on before leaving the house. The reasons we use smart devices is mirrored in why businesses are adopting IoT and IIoT (Industrial IoT) on a much larger scale- for convenience, insight, predictive maintenance, automation, and productive efficiency. IoT is becoming increasingly more critical in manufacturing and engineering, connecting thousands of sensors and actors of devices in the processes before, during, and after fabrication. The implementation of IoT within manufacturing processes, from raw materials to the product or smart products has only just begun and is destined to evolve into a key differentiator for successful manufacturing companies throughout the entire supply chain. The digital transformation in IIoT comes down to data at its core and how data is generated, stored and analyzed. IIoT requires data to be collected and processed at massive volumes in real/ near real time to provide accurate and live business insights for better decision making. Shop floors are continuously optimized as new components, sensors and actors are introduced to improve OEE (Overall Equipment Effectiveness), increase quality and reduce waste. With almost every new device, additional data variety is introduced and thus requires a flexible, highly available and scalable data platform to store and analyze this data. Furthermore, with the increasing convergence of IT and OT, even more complexity and diverse data needs to be integrated and processed, which adds higher complexity to the picture. MongoDB’s general purpose data platform, allows manufacturers to store OT / time series data sets in MongoDB together with recipes or digital twins for a complete real time end-to-end picture from edge to cloud and onto mobile devices for insights and control anytime and anywhere, online or offline. A connected factory model The Industry Solutions team at MongoDB set out to demonstrate how easily MongoDB can be integrated to solve the digital transformation challenges of IIoT in manufacturing with its flexible, scalable, application data platform. Using the small scale model of a smart fabrication factory, from Fischertechnik , the team collects and sends data via MQTT and processes it in MongoDB Atlas and Realm . Similar to a full scale physical factory, the smart factory model demonstrates how easily IIoT use cases can be built on top of MongoDB’s application data platform to enable and accelerate digitalization of manufacturing processes in any industry. The Fischertechnik model factory itself is often used to train engineering students in the field of what a manufacturing fabrication would look like, as well as a model for manufacturing companies to plan for the very real setup, building, and investment of their factories . So, what initially looks like a toy in robotics gets serious quite quickly. The model factory operates as the foundation of an IIoT use case. It is made up of several components- a warehouse, multiprocessing station, and sorting area. The warehouse is where raw material is stacked and stored, and when triggered, the raw material is retrieved and moved to processing by a mobile crane. From there, the items are sorted by color (i.e. red, white, or blue), to be sent to the correct outbound destination. The process covers ordering and storing of raw material to ordering and manufacturing of end products. Throughout these processes, there are multiple sensors detecting the type and color of the items, as well as environmental aspects like temperature and how much inventory is in stock. A surveillance camera detects motion and sends alerts including photos via MQTT. This simulates the wide variety of data a smart factory would emit in real time for track and trace, monitoring and visualization, alerts and as input for machine learning algorithms. The factory's infrastructure Out of the box, the Fischertechnik factory comes with a few dashboards connected to the Fischertechnik cloud, established via a WLAN router integrated in the factory. These dashboards include a: Customer View: A webshop interface, where a product can be ordered to trigger the supply chain processing Supplier View: A visualization and display of the ordering process of raw material Production View: A visualization of the factory status, production process, and sensor values from the camera and NFC/RFID readers. To emphasize and explain how MongoDB can be leveraged in this picture, the MongoDB team developed additional apps, using JavaScript, ReactJS, and Realm, to integrate and streamline data flows and processes on top of the MongoDB data platform. This included a: MongoDB Realm Order Portal: A ReactJS web application to order new products and track the process of orders. Data Visualization: A visualization of the different data types collected in MongoDB and visualized via MongoDB Charts for insights. Alert Management App: A mobile app leveraging MongoDB Realm and Realm Sync for alert notification and management offline and online. The machines of the factory are controlled by TXT controllers, Linux-based computers which use MQTT to communicate between each other and also with the cloud based applications. There are basically two types of data sent and received via MQTT- commands to trigger an action and streaming of event and time series sensor data. The main TXT controller runs a MQTT broker and replicates selected topics to a HiveMQ MQTT broker in the HiveMQ cloud. From there a redpanda kafka container collects the data streams and inserts them into MongoDB. The data persisted in MongoDB is then visualized via MongoDB Charts for real-time insights. Factory layout connected to data infrastructure The MongoDB Order Portal uses the Realm Web SDK and the serverless GraphQL API. GraphQL is used to pull data from MongoDB Atlas and the web SDK is used to add new orders (insert new documents) into a MongoDB cluster. When a new order is inserted into the Atlas database, an Atlas trigger is executed, which then sends a MQTT message directly to the HiveMQ MQTT broker, alerting the factory to process the order. The HiveMQ broker then replicates the order to the factory for processing. Sending data to the factory Receiving data from the factory is just as simple. The Factory provides a large amount of live data that can be streamed from the factory. In order to receive the data, HiveMQ and Kafka are used. The factory has an MQTT broker, which is bridged to a cloud HiveMQ broker. From the HiveMQ broker Kafka Connect with an MQTT source and a MongoDB sink connector the data is moved into MongoDB Atlas. Receiving data from the factory MongoDB & IIoT Digitalization in manufacturing means connecting IT and OT, mixing and meshing data from both domains and providing access to people and algorithms for higher levels of automation, increased efficiency and less waste. MongoDB’s application data platform is the data platform optimized for large varieties and amounts of data with a powerful query language for better decision making across a large volume of data. All easier said than done. However, Atlas helps solve these complex requirements with its ecosystem of functions, including: Real Time Analytics: As IIoT continues to boom with a surge of connected devices, limits are pushed each day with increased volumes of data. Atlas scales seamlessly, capable of ingesting enormous amounts of sensor and event data to support real time analysis for catching any critical events or changes as they happen. Dynamic Scalability: MongoDB Atlas and Realm provide automated scalability allowing you to start small and dynamically adapt your clusters with increasing/decreasing demand. Especially as sensor data gets colder over time, you can automatically offload cold data into object stores, such as S3, while maintaining the ability to query the hot and cold data through a single API. Time-Series: MongoDB 5.0 supports Time-Series Data natively through optimized storage with clustered indexes and optimized Time-Series query operators to analyze trends and identify anomalies quickly. The combination of time series data with other data structures such as digital twin models within the same data platform dramatically reduces complexity, development efforts and costs by avoiding additional technologies, ETL processes and data duplication. The MongoDB database can also be deployed next to the shop floor for data collection and analysis, making the shopfloor independent of the cloud. Pre Aggregated or raw data can then be seamlessly replicated or streamed into the public cloud for global views across factories. Additionally Realm, the serverless backend on top of MongoDB Atlas provides easy application and system integrations through REST / MongoDB Data and GraphQL APIs as well as synchronizing data with mobile devices for offline first use cases such as workforce enablement use cases. Atlas, Realm, and IIoT IIOT is an exciting realm (no pun intended) right now, with a massive opportunity for growth and innovation. The next level of innovation requires a resilient multi-functional data platform that reduces complexity, increases developer efficiency and reduces data duplication/integration while scaling elastically with your demand. What the MongoDB team scratched by quickly syncing the smart model factory with Atlas and Realm and iterating on top of that is just a fraction of the innovation we can support within manufacturing use cases. Learn more MongoDB Atlas and Realm, and how major enterprises are using these solutions for their manufacturing and IIoT needs here . This is the first of an IIoT series from MongoDB’s Industry Solutions team. Stay tuned for more to come!

January 19, 2022

Competitive Advantage Cannot Be Bought: Accelerating Digital Transformation With MongoDB's Application Data Platform

Business disruption isn’t new. But it is accelerating to a new, ever-more-disruptive level. That acceleration is being amplified by the forces of our era, from global health crises to political shifts to climate change, among others. These disruptive forces are creating huge changes in corporate behavior and consumer expectations. When considering changing business conditions, whether from adaptation or innovation, it’s worth asking, “Is this something that is going to default back to the way it was before?” In most cases, the answer is "probably not.” Disruption is here to stay. While that can be scary for incumbent enterprises, we believe that disruption is the opposite of stagnation. It is an opportunity for change, adaptation, and movement. In 2020, many companies had to completely rethink how they accomplished their business goals and were pushed to implement new strategies at an accelerated pace. Years of digital transformation were condensed into months. Companies developed new structures and flexible models — internal muscles that will continue serving them as they move forward. The pace at which digital transformation happens is only accelerating. With an increasingly digital economy, we’re seeing: No more barriers to entry. Obstacles to entering the digital economy are essentially gone. For example, a bank considering fintech disruption is no longer just competing with interregional banks. It’s now competing with every mobile-first challenger bank anywhere in the world. Competition is more high stakes than ever. A need to enable sustainable innovation. The most competitive businesses are maniacally focused on removing every obstacle so that their teams can be agile and move quickly in a scalable and sustainable way. An emphasis on data security. As more of the IT landscape moves into the cloud, and as organizations increasingly go global, data security and privacy need to be at the forefront. It’s critical that a company’s customers trust them to be the stewards of their data now and into the future. However, not everyone’s digital transformation is successful. Many companies can’t keep up with new competitors and sudden market shifts. Your competitive advantage Competitive advantage is now directly tied to how well companies are able to build software around their most important asset: data. Companies have been using commercial software since the early 1970s. What is different now is that their differentiation as a business is tied to the software they build internally. No one is expecting to win in their industry because they use an off-the-shelf CRM product, for example. That is to say: Competitive advantage cannot be bought, it must be built. This is not a new idea. Even the most basic software cannot work without proper storage, retrieval, and use of data. Otherwise, every application would have a beautiful front end but nothing functional behind them. The true art and skill of modern application development is to tap into data and wield it to create true innovation. Customer experience and expectations Almost 15 years into the smartphone and smart device era, consumer and B2B expectations for their digital interactions and experiences are extremely high and exceptionally demanding. Customers expect their digital experiences to: Be highly responsive: Digital experiences must be quick to react to events, actions, and consumer behavior. Deliver relevant information: Modern digital experiences present the most relevant information intelligently, sometimes even predicting what a consumer is searching for before they complete their thought. Embrace mobile first: Mobile is becoming the primary way customers interact with companies. They expect to be able to do everything they would’ve done on a desktop from their mobile devices. Uphold data privacy: Customers expect complete data privacy — and for companies to allow them to take control of their data when requested. Be powered by real-time analytics: Customers expect applications to be smart. In addition to all of the above, consumers expect apps to guide them, assure them, and delight them with rich experiences powered by analytics and delivered in real time. Continuously improve: Customer expectations demand improvements at a faster rate than ever before. Legacy infrastructure: A challenge in digital transformation Companies' ability to deliver on customer expectations is almost entirely reliant on their underlying data infrastructure — it’s the foundation of their entire tech stack. Modern digital experiences demand a modern data infrastructure that addresses how data is stored, processed, and used. Despite companies’ best efforts — and significant spending — it’s estimated that more than 70% of enterprises fail in their digital transformation initiatives . This alarming number can make it appear as though digital improvement is a gamble not worth taking, but this is most definitely not the case. The truth is that though the way companies leverage data to build modern applications has changed, the typical data infrastructure has not kept up with the demands, making working with data the hardest part of building and evolving applications. One key factor is that typical data infrastructures are still built around legacy relational databases. These outdated infrastructures mean: Rigidity. The rigidity of the rows and columns that define relational databases make experimenting and iterating on applications difficult. Anytime a data model or schema needs to be changed as an application evolves or incorporates new types of data, developers must consider dependencies at the data layer, and the brittle nature of relational databases makes such change difficult. Data structure clashes. Relational tabular data structures are at odds with how most developers think and code, which is through object-oriented programming languages. To put it simply, developers do not think in rows and columns, which clash with modern data and the objects developers work with. Automatic failover and horizontal scaling are not natively supported. The essentials that modern applications need, such as automatic failover and support for massive scale on demand, are not natively built into legacy relational databases; these become more obstacles to overcome. With a relational data infrastructure, it’s typical for an organization to have hundreds or thousands of tables built up over years. Having to update or unwind them as it is trying to build or iterate on its applications brings innovation to a crawl — or puts it on pause altogether. As an example, Travelers , a Fortune 500 U.S.-based insurance company, recently attempted to modernize an underwriting application. Their most profitable unit, business insurance, required much faster software delivery and response times. The company attempted to solve this with standard solutions, such as implementing agile development, breaking down monoliths with microservices, and rolling out continuous delivery. Despite their best efforts, however, legacy relational databases held them back. Travelers’ senior director of architecture at the time, Jeff Needham, in reference to their attempts at transformation, said, “At the end, it was still the legacy database. We implemented all these things, but we still had to wait days for a database change to be implemented.” Both Travelers’ failing result and eventual frustration are shared by many organizations that get ensnared in their own data infrastructures. What about NoSQL? For teams that need to deliver more modern application experiences or operate at faster speeds, it might appear that the most obvious path is to add NoSQL datastores as a bandage to address relational shortcomings. But doing so requires ETL (extract, transform, and load data from one source to another), adding more complexity to data management. These teams quickly realize that non-relational or NoSQL databases only excel at a few select objectives, with otherwise limited capabilities (including limited query capabilities and the lack of data consistency guarantees). The truth is that NoSQL databases can never really replace relational databases because they’re suitable only for niche use cases. In the end, it’s not just one database being added and requiring management but several — one for graph data and traversals, one for time series, one for key values, and so on. The ever-increasing need to address diversified data workloads means a new managed database for each type of data, creating even more silos. The bottom line is that adding NoSQL to cover what relational databases can’t makes the data environment even more complex than it was before. Beyond operational databases Today, an organization’s application data infrastructure is made up of more than just operational databases. To deliver rich search capabilities, companies often add separate search engine technologies to their environments, putting the onus on their teams to manage the movement of data between systems. To enable low-latency and offline-first application experiences on mobile devices, they often add separate local data storage technologies. Syncing data from devices to the backend becomes another spinning plate for developers to keep up with since it involves complexities such as networking code and conflict resolution. Finally, to create rich analytics-backed application experiences, more often than not organizations use ETL for their data, reformatting it along the way for an entirely separate analytics database. Every step of the way, more time, people, and money goes toward what is now a growing data infrastructure problem — an increasing sprawl of complexity — and eating away at development cycles that could otherwise be spent innovating their products. Spaghetti architecture is a tax on innovation As they try to solve data issues by adding new components, services, or technologies, many companies find themselves trapped in “spaghetti architecture,” meaning overly complex and siloed architectures piled on top of already heavy infrastructures. Each bit of technology has its quirks from operational, security, and performance standpoints, demanding expertise and attention — and making data integration difficult. Moving data between systems requires dedicated people, teams, and money. Massive resources go into dealing with the incredible amount of data duplication. But beyond just cost, development resources must go toward dealing with multiple operational and security models when data is distributed across so many different systems. This makes it incredibly difficult to innovate in a scalable, sustainable way. In fact, this is why many digital transformations fail: inadequate data infrastructures, burning through resources, and “solutions” creating more complexity. And all while, they are falling behind their competitors. We think of all of this as a recurring tax on innovation tied to an ever-growing data infrastructure problem that we call DIRT (data and innovation recurring tax). DIRT is recurring because it never goes away by itself. It’s a 2,000-pound boulder strapped to a team’s back today, tomorrow, and five years from now. It will continue to weigh down teams until they address it head on. Eliminate DIRT DIRT is a real problem, but there are equally real, and realistic, solutions. The most successful and advanced organizations avoid such complexities altogether by building data infrastructures focused on four key guidelines: Doubling developer productivity. Companies’ success depends on their developers’ ability to create industry-leading applications, so these businesses prioritize removing any obstacles to productivity, including rigid data structures, fragmented developer experiences, and backend maintenance. Prioritizing elegant, repeatable architectures. The companies that will win the race toward data integrity understand the cost of bespoke data infrastructures and technologies that only make their production environments more complex. These companies use niche technologies only when absolutely necessary. Intentionally addressing security and data privacy. Successful businesses don’t let data security and privacy become a separate and massive project. They’re able to satisfy sophisticated requirements without compromising simplicity or the developer experience. Leveraging the power of multi-cloud. These companies don’t compromise on deployment flexibility. They’re ahead of data gravity and can deploy a single application globally across multiple regions and multiple clouds without having to rewrite code or spend months in planning. How MongoDB helps MongoDB provides companies with an application data platform that allows them to move fast and simplify how they build with data for any application. This allows organizations to spend less effort rationalizing their data infrastructure and focus more on innovation and building out their unique differentiation, eliminating DIRT. The document model is flexible and maps to how developers think and code. The flexible document data model makes it easy to model and remodel data as application requirements change, increasing developer productivity by eliminating the headache of rows and columns. Instead, documents map exactly to the objects that developers work with in their code. This is the core insight that MongoDB’s founders had at least a decade ago: Data systems fundamentally need a different data model to be able to match modern development. This is also why MongoDB has become so popular with developers, with 75 million+ downloads. MongoDB documents are a superset of all other data models. The MongoDB document model upholds the superset of legacy functions, allowing users to store and work with data of various types and shapes. In contrast to niche databases, it covers the needs of relational data, objects, cache formats, and specialized data such as GIS data types or time series data. Document databases are not just one of many other databases to be used simultaneously. Advanced organizations realize that the document model underpins a broad spectrum of use cases. For example, the simplest documents serve as key-value pairs. Documents can be modeled as the nodes and edges of a graph. Documents are actually more intuitive for modeling relationships with support for embedding and nested structures. The ability to work with diverse varieties of data fits neatly within the document data model, giving MongoDB a concrete foundation to build from. MongoDB features a powerful, expressive, and unified interface. This provides for improved productivity because developers do not need to research, learn, and stay up-to-date on multiple ways to work with data across their different workloads. It’s also much more natural to use than SQL because the developer experience is one that feels like an extension of programming languages. The experience is idiomatic to each programming language and paradigm; developers can view MongoDB documents in their native format and work directly with the data without the need for abstraction layers such as object relational mappers (ORMs), data abstraction layers (DALs), and more — they can simply be removed or retired. Furthermore, multiple different teams working in different programming environments — from C# to Java to C++ — can access the same data at their leisure, allowing simplification and integration of data domains. MongoDB: The application data platform MongoDB is more than just a database. It is a multi-purpose, multi-faceted application data platform. This means that MongoDB recognizes data comes in a wide variety of formats, needs to be processed, stored, trained (and so on) in a broad variety of ways, and needs to be regulated, audited, encrypted, and secured in a similarly diverse set of ways. Data is one of the most valuable yet complex assets companies have. MongoDB simplifies many different use cases to wield this important asset in an intelligent, beautiful way by offering a unified interface to work with data generated by modern applications. MongoDB brings together two foundational concepts — the document model and a unified query API — in the form of an operational and transactional database. MongoDB’s application data platform offers: A transactional database: MongoDB has the transactional guarantees and data governance capabilities required to not just supplement but actually replace relational databases. Distributed multi-document transactions are fully ACID compliant, making it the transactional database behind core mission-critical applications. Search capabilities: Fully integrated full-text search eliminates the need for separate search engines. The MongoDB platform includes integrated search capabilities, including an extended query API, so developers are not forced to stand up a dedicated search engine to enable application search. All of the operations, including data movement, is handled natively within the MongoDB platform. Mobile readiness: MongoDB Realm’s flexible local datastore includes seamless edge-to-cloud sync for mobile and computing at the edge. MongoDB Realm enables agility for mobile developers through a natural development experience that syncs data from the backend to the front end with minimal code required. Things like conflict resolution, networking code, and plumbing are all handled automatically. Real-time analytics: MongoDB offers real-time analytics with workload isolation and native data visualization. As more organizations design smarter applications with MongoDB, they can call on real-time analytics tied to either machine learning or direct application experiences. Data lake: With MongoDB, developers can run federated queries across operational databases, transactional databases, and cloud object storage. Queries can also be extended across multiple clusters, or even to data sitting outside of MongoDB. MongoDB’s architecture is able to federate and aggregate data for analytical purposes as needed. A sustainable platform: In real-world applications, no capabilities matter if the platform is not secure, resilient, and scalable. Only sustainable frameworks can evolve with changes in the market and demand for the product. Scalability and compliance: Everything at MongoDB is built on a distributed systems architecture, with turnkey global data distribution for data sovereignty and fast access. This is not just for horizontal scaling and linear cost economics as workloads get larger, but it also helps organizations handle data distribution for their global applications, keeping relevant data close to the user but, for example, distributed across different geographic regions as needed to deliver a low-latency experience. MongoDB can also be used to isolate data by country to address data sovereignty regulations. Security: MongoDB holds industry-leading data privacy controls with client-side field level encryption, having built security into the core of the database, whether it's with encrypted storage, role-based access controls, or enterprise-grade auditing. In a world where there are often third-party providers involved, this gives more control to the end customer so they can definitively say that no third-party provider can access sensitive data, preventing full breaches of security. Multi-cloud: With MongoDB, developers have the flexibility to deploy across clouds in nearly 80 regions. Extending their databases across multiple clouds allows developers to leverage the innovative services that may be associated with another provider, build cross-cloud resiliency, and get additional reach without having to stand up separate databases in different regions. This, in turn, allows for a unified developer experience across data workloads, a simpler operational and security model, an automated and transparent data movement between services, and reduction of the dreaded data duplication. Interested in getting started with MongoDB Atlas for your digital transformation? Start for free here or contact us directly.

June 21, 2021

Simplifying Data Science with Iguazio and MongoDB: Modernization with Machine Learning

For the most innovative, forward-thinking companies, “data” has become synonymous with “big data” — and “big data” has become synonymous with “machine learning and AI.” The amount of data you have is raw knowledge. The ability to connect the dots in a cohesive picture that lets you see major projections, personalizations, security breaches, etc., in real time — that’s wisdom. Or, as we like to call it, data science. MongoDB Cloud is the leading cloud data platform for modern applications. Iguazio , initially inspired by the powerful Iguazu Falls in South America, is the leading data science platform built for production and real-time use cases. We’re both disrupting and leading various industries through innovation and highly forethought intelligence. It makes perfect sense for us to work together to create a powerful, data-driven solution. Iguazio Data Science & MLOps platform optimizes data science for your use cases Iguazio enables enterprises to develop, deploy, and manage their AI applications, drastically shortening the time required to create real business value with AI. Using Iguazio, organizations can build and run AI models at scale and in real time, deploy them anywhere (multi-cloud, on-prem or edge), and bring to life their most ambitious AI-driven strategies. Enterprises spanning a wide range of verticals use Iguazio to solve the complexities of machine learning operations ( MLOps ), and accelerate the machine learning workflow by automating the following, end-to-end processes: Data collection — ingested from any diverse source, whether structured, unstructured, raw or real-time Data preparation — through exploration and manipulation at scale (go big!) Continuous model training — through acceleration and automation Rapid model and API deployment Monitoring and management of AI applications in production As a serverless, cloud-native data science platform, Iguazio reduces the overhead and complexity of developing, deploying, and monitoring AI models, guarantees consistent and reproducible results, and allows mobilizing, scaling, and duplicating functions to multiple enforcement points. MongoDB delivers unprecedented flexibility for real-time data science integration With its scalable, adaptable data processing model, ability to build rich data pipelines, and capacity to scale out while doing both in parallel, MongoDB is a foundational persistence layer for data science. It allows you to use your data intelligently in complex analytics, drawing new conclusions and identifying actions to take. Data science and data analytics go hand-in-hand, fueled by big data. The MongoDB data platform handles data analytics by: Enabling scalability and distributed processing — processing data with a query framework that reduces data movement by knowing where that data is and optimizing in-place computation Accelerating insights — delivering real-time insight and actions Supporting a full data lifecycle — intelligent data tiering from ingestion to transactions to retirement Leveraging a rich ecosystem of tools and machine learning for data science Here's a look at how Iguazio and MongoDB partner to synthesize a seamless production environment: MongoDB and Iguazio: from research to production in weeks Iguazio fuses with MongoDB to allow intelligent, complex data compilations that lead to real-world ML/AI results like streaming and analytics, IoT applications, conversational interfaces, and image recognition. Data science is opening opportunities for businesses in all areas, from financial services to retail, marketing, telco, and IoT, and those opportunities create demands on data that continue to grow. Iguazio swiftly reduces the development and deployment of data science projects from months to weeks, transforming how businesses, developers, and product owners use and imagine new use cases for their data. Together, MongoDB and Iguazio establish a joint hybrid/multi-cloud data science platform. MongoDB’s unique features create the perfect seeding ground for Iguazio’s data science platform. They include: MongoDB’s high-performing, highly ranked data platform experience No data duplication Optimization for real-time, an essential factor for data science An elastic, flexible model that adjusts to ever-changing load requirements Production that’s ready in minutes Meanwhile, Iguazio’s powerful ML pipeline automation simplifies the complex data science layer by creating a production-ready environment with an end-to-end MLOps solution, including: A feature store for managing features (online and offline) that resides in MongoDB Data exploration and training at scale, using built-in distribution engines such as Dask and Horovod Real-time feature engineering using Iguazio’s Nuclio-supported serverless functions Model management and model monitoring, including drift detection Open and integrated Python environment, including built-in libraries and Jupyter Notebook-as-a-Service Data and data science in the real world When we think of data, stagnant databases may come to mind. But data in action is live, quick, and moves in real time. Data science is no different — and it has quickly incorporated itself in every sector of virtually every industry: Fraud prevention — distinguishing legitimate from fraudulent behavior and learning to prevent new tactics over time Predictive maintenance — finding patterns to predict and prevent failures Real-time recommendation engines — processing consumer data for immediate feedback Process optimization — minimizing costs and improving processes and targets Remote monitoring — quickly detecting anomalies, threats, or failures Autonomous vehicles — continuously learning new processes and landscapes to optimize safety, performance, and maintenance Smart scheduling — increasing coordination among nearly infinite variables Smart mobility systems — using predictive optimization to maintain efficiency, safety, and accuracy IoT & IIoT — generating insights to identify patterns and predict behavior Data science today MongoDB enables a more intuitive process for data management and exploration by simplifying and enriching data. Iguazio helps turn data into smarter insights by simplifying organizations’ modernization into machine learning, AI, and the ongoing spectrum of data science — and we’ve only just scratched the surface. To learn more about how, together, Iguazio and MongoDB can transform your data processes into intelligent data science, check out our joint webinar discussing multiple client use cases. MongoDB and modernization To learn more about MongoDB’s overall modernization strategy for moving from legacy RDBMS to MongoDB Atlas, read here .

December 2, 2020