Resources

MongoDB and Wimera on IIoT: Achieving Industrial Connectivity at Scale

__ Manufacturing companies are emerging from the pandemic with a renewed focus on digital manufacturing technologies. With so many options in the market all offering the potential to improve manufacturing operational efficiency, leaders can find it challenging to decide on which technology to invest in first. However, for all manufacturers, one area of investment is clear: Industrial Internet of Things (IIoT). A reliable, scalable IIoT deployment is the first step to embarking on the digital manufacturing journey. To enable advanced analytics use cases, it’s essential to install sensors and connect manufacturing equipment to a database to store and analyze data. In this webinar, we share how MongoDB and Wimera Systems are solving the challenge of scalable IIoT connectivity, focusing on: How MongoDB powers IIoT solutions, and best practices for embarking on your digital manufacturing journey How customers are using IIoT solutions in the real world A demo of the Wimera Systems Smart Factory Suite running on MongoDB Atlas

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A Roadmap to Financial Services Agility with Event-Driven Architecture

To bridge the gap between the past and the future, event-driven architectures can enable existing systems to be the foundation for a new generation of applications that create value in a variety of ways. More broadly, an event-driven architecture helps the organization improve business agility. This white paper is an introduction to event-driven architectures for financial services applications, covering the building blocks used in their construction and how these architectures are implemented using MongoDB. Download now to learn more!

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Telecommunications In Transition

The telecommunications industry is faced with constant change — because who a customer is, and what it means for that customer to be “connected” is always changing. It's clear that the next set of changes are being driven by the ubiquity of connected devices and "things." Whether these are consumer devices or industrial and commercial devices, this step change in connectivity is being driven by new technologies like 5G, artificial intelligence, and the Internet of Things (IoT). These shifts present a new set of challenges for telecom providers: how to use data to improve the customer experience for hundreds of millions of people, and also to enable businesses to use their networks to run tens of billions of connected “things.” Download this white paper to gain a better undertanding of the state of the telecom industry, how changing customers are demanding a changed experience, and how leading with the right data platform can help you get ahead in the cloud or on-premises.

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SQL to MongoDB: An RDBMS Migration Guide

.imageContainer div { width: 145px; display: inline-block; margin-right: 3px; } .imageContainer img { border: 1px solid #eee; box-shadow: 1px 1px 2px #aaa; border-radius: 3px; margin: 0 0 10px; line-height: 125px; } The relational database has been the foundation of enterprise data management for over thirty years. But the way we build and run applications today, coupled with growth in data sources and user loads, are pushing relational databases beyond their limits. To address these challenges, companies like Salesforce.com, MTV, and Cisco have migrated successfully from relational databases to MongoDB. This whitepaper provides a step-by-step guide for project teams that want to know how to migrate from an RDBMS to MongoDB. It also explains the considerations for teams that come from relational database backgrounds and want to build new applications on MongoDB. Download this whitepaper to learn about: Schema design for moving from relational tables to flexible and dynamic BSON documents Data integrity and how MongoDB ensures atomicity, data durability and strong consistency Query model & data analysis in MongoDB Data migration, including scripts and parallel operations Operational considerations like scaling, HA, monitoring, security, and backup Companies ranging from startups to Fortune 500s choose MongoDB to build, improve, and innovate:

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Performance Best Practices for MongoDB

.imageContainer div { width: 145px; display: inline-block; margin-right: 3px; } .imageContainer img { border: 1px solid #eee; box-shadow: 1px 1px 2px #aaa; border-radius: 3px; margin: 0 0 10px; line-height: 125px; } Wondering how to optimize performance with MongoDB? This white paper will walk you through top tips for the best MongoDB performance. Download now to learn about: Optimizing settings for your own application patterns Schema design and index choices for speed and performance Disk I/O considerations Choosing the right hardware, plus the best tips for using AWS Benchmarking MongoDB for real-world results MongoDB gives you lots of flexibility to make your own design choices. Small changes can provide huge performance improvements. Download the Performance Best Practices white paper now to make sure you’re set up for the best performance. Fill out this form and we’ll take you straight to the PDF. Companies ranging from startups to Fortune 500s choose MongoDB to build, improve, and innovate:

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Balancing Speed and Risk with a Multi-Cloud Strategy

What is multi-cloud? How can a multi-cloud strategy help accelerate your organization’s development speed? And how can it help mitigate the risks that you are facing? In an economy increasingly defined by digital experiences, success depends on speed — on how quickly teams can build, test, learn from, and adapt new software applications and services. But moving from idea to prototype to production is time-sensitive. Go too slow and your competition might eat you for lunch. Go too fast, and you must confront risks like vendor lock-in and technical sprawl or failure. A smart multi-cloud strategy helps you balance speed and risk. One scenario: Move faster by tapping in to the best tools from each cloud provider; reduce your risk by building in multi-cloud resilience. In this white paper, we explore What multi-cloud means and its benefits How a multi-cloud strategy can lead to greater developer speed The risks that multi-cloud can lessen or eliminate

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Data Modeling in MongoDB

Want to learn the Data Modeling best practices that you can leverage when designing your next application? Watch this session on Data Modeling where our experts talk through key considerations, design patterns and share real-life use case examples. Agenda topics will cover: Introduction to Data Modeling with MongoDB Data Modeling key considerations Linked vs Embedding Design Patterns Use case examples

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Payments modernization – architectures shaping the future of Asia-Pasific

The payments landscape in Asia-Pacific is evolving rapidly with customers expecting a connected and secure experience, coupled with new and changing standards and regulations. Those institutions that fail to meet these requirements risk losing out on significant revenue. Organizations are challenged with monolithic payment systems that are expensive to maintain and lack the data flexibility and third-party integration capabilities required by modern payment ecosystems. Watch this webinar to learn how MongoDB’s developer data platform offers you the capabilities to create an enriched payments experience by consolidating, ingesting, and acting on payments data instantly, delivering value-added services and features to consumers and business customers. In this payments modernization webinar, we cover: The payment trends and challenges that are changing the way organizations think about data How a modern payments data architecture scales effortlessly, with zero downtime, and analyzes any type of payments data in place and in real time How to leverage MongoDB capabilities for data enrichment, real-time analytics, always-on mobile experiences, and API driven finance Success stories of how organizations have modernized with MongoDB You'll hear from: Wei You Pan: Principal, Financial Services Industry Solutions at MongoDB

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Payments modernization – architectures shaping the future

The payments landscape is evolving rapidly with customers expecting a connected and secure experience, enhanced value generation and all of this coupled with new and changing standards and regulations. Those institutions that fail to meet these requirements risk losing out on significant revenue. Payment providers – be it a PSP acquiring consumer transactions or a financial institution with large investment and corporate banking – are challenged with monolithic payment systems that are expensive to maintain and lack the data flexibility and third-party integration capabilities required by modern payment ecosystems. Data handling and a composable architecture and design are becoming the epicenter. Watch this webinar to learn how MongoDB’s developer data platform offers you the capabilities to create an enriched payments experience by consolidating, ingesting, and acting on payments data instantly, delivering value-added services and features to consumers and business customers. In this payments modernization webinar, we cover: The payment trends and challenges that are changing the way organizations think about data How a modern payments data architecture scales effortlessly, with zero downtime, and analyzes any type of payments data in place and in real time How MongoDB serves as the core data foundation powering instant payment capabilities Success stories of how organizations have modernized with MongoDB’s operational and analytical data services You'll hear from: Boris Bialek: Managing Director of Industry Solutions, MongoDB

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Types Of NoSQL Database Management Systems

Developers need solutions that align with the realities of modern data and iterative software development practices. NoSQL databases have emerged in recent years as an answer to the limitations of traditional relational databases and to provide the performance, scalability and flexibility required of modern applications. Most aspects of these NoSQL technologies vary greatly and have little in common except for the fact that they do not use a relational data model. There are four types of NoSQL database management systems : Key-value stores are the simplest NoSQL databases. Every single item in a key value database is stored as an attribute name (or "key") together with its value. Examples include Riak, Voldemort, and Redis. Wide-column stores store columns of data together instead of rows and are optimized for queries over large datasets. Cassandra and HBase are wide-column databases. Document databases pair each key with a complex data structure known as a document. Documents can contain many different key-value pairs, or key-array pairs, or even nested documents. MongoDB is a document database. Graph databases are used to store information about networks, such as social connections. Examples include Neo4J and HyperGraphDB. NoSQL databases are rising in popularity as companies apply them to a growing number of use cases. To learn more about why MongoDB is the most widely-used NoSQL database, read our free white paper, “Top 5 Considerations.” Related Database Topics What are the different types of databases ? What is a managed-database ? What is database management ? What are database management systems ? What is database hosting ?

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Unstructured Data Management

Before the advent of online and mobile applications, managing data was pretty straightforward. Databases followed the relational model of organizing data into a pre-defined structure of rows and columns that were contained in tables. But today’s data hardly resembles the data of yesterday. Data generated by modern applications are predominantly unstructured – e.g., text, video files, social media posts, emails – and doesn’t fit neatly into rows and columns. NoSQL databases arose to address the limitations of relational databases and are particularly suited for unstructured data management. MongoDB, the leading NoSQL solution according to DB-Engine rankings, is particularly adept at storing unstructured data. MongoDB’s document data model stores all related data together within a single document, making it much more flexible than the rigid structure of the relational database model. Unstructured data management presents both a challenge and an opportunity for today’s enterprises. Thousands of organizations from the latest startup to large Fortune 100 companies have used MongoDB to build amazing applications that leverage unstructured data. Learn more about the success of these companies with unstructured data management and how you can benefit from their example. Download our white paper today.

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When To Use Apache Spark With MongoDB

Apache Spark is a powerful processing engine designed for speed, ease of use, and sophisticated analytics. Spark particularly excels when fast performance is required. MongoDB is a popular NoSQL database that enterprises rely on for real-time analytics from their operational data. As powerful as MongoDB is on its own, the integration of Apache Spark extends analytics capabilities even further to perform real-time analytics and machine learning. With Spark and MongoDB, developers can build more functional applications faster using a single database technology. The integration of these two Big Data technology also saves operations teams the hassle of shuttling data between separate operational and analytics infrastructure. For CIOs, the combined forces enable faster time-to-insight for their businesses, with lower cost and risk. Here are just a few of scenarios of when to use Apache Spark with MongoDB. Rich Operators & Algorithms. Spark supports over 100 different operators and algorithms for processing data. Developers can use these to perform advanced computations that would otherwise require more programming effort to combine the MongoDB aggregation framework with application code. For example, a web analytics platform built on MongoDB would provide insight into the performance of your content by geography and by audience. Adding Spark’s machine learning algorithms would allow you to go even further by taking those insights and then serving up targeted content recommendations for your readers. Processing Paradigm. Many programming languages can use their own MongoDB drivers to execute queries against the database, returning results to the application where additional analytics can be run using standard machine learning and statistics libraries. In this scenario, a developer could use the MongoDB Python or R drivers to query the database. But this process becomes increasingly complex as you need to distribute the application across multiple threads and nodes. Using Apache Spark makes this kind of distributed processing easier and faster to develop because Spark jobs can be directly performed against data in MongoDB. As a result, the integration makes fast, real-time analysis possible. Skills Re-Use. With libraries for SQL, machine learning and others – combined with programming in Java, Scala and Python – developers can leverage existing skills and best practices to build sophisticated analytics workflows on top of MongoDB. Together MongoDB and Apache Spark are enabling success by turning analytics into real-time action. Learn more about how this integration can benefit your organization by downloading our white paper.

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