Resources

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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Ready for Production? Get MongoDB Enterprise Advanced.

MongoDB Enterprise Advanced gives you everything you need to confidently go into production with MongoDB. This datasheet will give you the details, plus what you really need to know: the price. The Enterprise Advanced package includes: Advanced security features to keep your data safe, like auditing and Kerberos and LDAP authentication Ops Manager, a set of automation, monitoring, and backup tools to keep MongoDB operations easy and simplify your life Platform certifications and software integration, so you know it’ll work in your environment And much more MongoDB Enterprise Advanced is designed for the way you do business. You can be confident that your MongoDB deployment is ready for prime time. If you’re ready to see how, fill out the form here to get immediate access to the datasheet. Companies ranging from startups to Fortune 500s choose MongoDB to build, improve, and innovate:

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Data modernization: a key element of business transformation

Many financial institutions face issues in using legacy platforms that no longer keep up with their customer and internal requirements in real time. So how can businesses evolve their data processing to become more timely, agile, and scalable while controlling infrastructure costs? Data modernisation is facilitating business transformation by moving from outdated legacy systems to a modern, multi-cloud, data platform to enable personalised customer experiences, new revenue opportunities, and a faster time-to-market. The transition from legacy to modern platforms can lead to multiple benefits for financial institutions. While reducing TCO, these transformations increase developer productivity and reduce data fragmentation and duplication, offering more value from data. For example, organisations can unlock data to support novel onboarding processes by using existing operational data to score new customers. Watch this Finextra webinar, hosted in association with MongoDB, to hear from a panel of industry experts as they discuss the following areas: How will data modernisation solve legacy issues faced by financial institutions and businesses? What strategies of data modernisation are in use? How will data modernisation impact customers? What trends are we seeing in the industry in terms of data analytics? What are some examples of use cases in which data modernisation has been successfully applied? Speakers: Gary Wright - Head of Research, Finextra Joerg Schmuecker - Director, Financial Services Industry Solutions, MongoDB Prabhakaran Pitchandi - Vice President and Global Head of Analytics and Insights, TCS Fabian Khoshbakht - MD, Head of Client Insight & Innovation Treasury Services, BNY Mellon

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MongoDB Atlas SQL Webinar

In this session we will cover how the Atlas SQL Interface uses mongosql, a SQL-92 compatible dialect that’s designed for the document model and how it leverages Atlas Data Federation functionality under-the-hood so you can query across Atlas clusters and cloud storage, like S3. We will also cover Atlas SQL Connectors and Drivers which allow you to easily connect your SQL-based business intelligence and analytics tools to Atlas, enabling you to find insights faster on live application data. Sign up now to watch the session.

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Modern, Data-Centric Commerce with MongoDB and commercetools

What are the building blocks to achieving personalized commerce experiences? MACH and advanced database capabilities provide the foundation to work with data-centric commerce easily and effortlessly. The combined commercetools and MongoDB solution provides an innovative backbone that helps you leverage data for this exact purpose. In * The Data-Centric Commerce Guide with commercetools and MongoDB, *learn how you can modernize your commerce infrastructure with commercetools Composable Commerce and MongoDB Atlas to personalize customer experiences at scale.

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FHIR API for MongoDB

MongoDB has partnered with FHIR API experts Exafluence to offer an all-in-one FHIR solution. The partnership simplifies digital transformation for healthcare by offering modernization accelerators using MongoDB Atlas. Support for real-time and batch-data ingestion Flexible deployment options (multi-cloud, on-premises) Maintains the native FHIR data format using MongoDB’s JSON based document model Learn more about the partnership, and how you can benefit, in this quick guide.

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FHIR Today, Interoperability Tomorrow

Both MongoDB and FHIR natively support the JSON format, the standard that supports rich data structures and objects prevalent in healthcare such as patient data, claims, policies, and treatment information. In addition, the flexibility of the document model at the heart of MongoDB is uniquely qualified to adapt to future changes to the FHIR format. Learn why leading healthcare providers and payers choose MongoDB as the foundation for their healthcare data platform in this solution brief.

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Application-Driven Analytics: Defining the next wave of successful modern apps

By building smarter apps and increasing the speed of business insights, Application-Driven Analytics gives you the opportunity to out-innovate your competitors and improve efficiency. To do this however, you can no longer rely only on copying data out of your operational systems into separate analytics stores. Moving data takes time. Instead, analytics has to be “shifted left” to the source of your data – to the applications themselves. We call this shift application-driven analytics. And it’s a shift that both your developers and analytics teams need to be ready to embrace. In this we cover: The rise of application-driven analytics and the problems it solves. The foundational capabilities needed to be successful on the journey to smarter applications. Organizations that are already reaping the benefits of application-driven analytics. Watch the On-demand version now!

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Leveraging MongoDB to Create Asset Administration Shells and Enable Industry 4.0

To achieve digital manufacturing through Industry 4.0, it is critical to aggregate and manage the huge amount of data generated in the factory by Industrial Internet of Things (IIoT) sensors and devices. Having connected machines, workers, and systems is just the start of the digital transformation journey. To extract real value from this connectivity, a digital twin of machines and processes have to be modeled in a standardized manner, which will help aggregate heterogeneous data and provide valuable insights to drive productivity and efficiency. This white paper explores Industry 4.0-compliant data modeling principles and how to use MongoDB Atlas to fulfill demands of Industry 4.0: interoperability, flexibility and scalability.

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MongoDB x Google Cloud Atlas 202 eWorkshop

During this three-hour workshop, you’ll get 1:1 support as you explore MongoDB's developer data platform, giving you an introduction to MongoDB Atlas on Google Cloud.

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Manage Wealth on MongoDB

Modernize Legacy Infrastructure to Deliver Enhanced Insights Wealth management firms aggregate and analyze large amounts of data from various sources to find deep insights to advise their clients. The wealth management market is very competitive, and customers expect personalized content near real-time. Most firms struggle to do this today because data is locked in rigid database systems that increase the cost and time-to-market for any change and stifle innovation. Wealth management firms must upgrade their systems' capabilities to provide real-time customer advisory based on deep insights. Modern data platforms leverage hardware and software advances to support large amounts of highly heterogeneous data for high availability. This is in contrast to the current relational database management systems, which require storing all data in rigid table schemas and are challenging to integrate with AI/ML platforms. This white paper looks at how you can use MongoDB to establish an elegant and efficient data aggregation, analytics, and client outreach platform for investment advisory firms.

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