Building a content management system (CMS) that stores and serves content to a variety of applications requires the latest technology and development approaches. For the best user experience, the CMS has to handle a large volume of data along with a great variety of unstructured data all in real-time. Typical relational database technology falls short in this area because this kind of database has difficulty incorporating new content, attributes, or features without negatively impacting performance. To address the requirements of modern content management systems you need to turn to the latest database technology that can handle a large volume of unstructured data. MongoDB, the leading database of a new generation of technology called NoSQL, lets you store and serve up any type of content within a single database. You can build these systems with MongoDB quickly and at much less expense than with decades old relational technology. MongoDB is particularly well-suited to support your CMS efforts because the software offers: A ** flexible data model ** means that you can incorporate any kind of data into your CMS, regardless of the source. This flexible model also lets you make frequent updates to the database without downtime to your application. ** Scalable to millions of users ** as MongoDB has a native horizontal scale out architecture that easily lets you accommodate additional demand as your audience grows. ** Much lower cost ** to complete your CMS project as your teams are more productive and you end up spending much less on commodity hardware to scale the system. By some estimates, it costs only 10% of what it would cost with a relational database to build a CMS on MongoDB. MongoDB unites all your content assets into a single database and makes for an overall better user experience. This helps your enterprise stay competitive as customers today expect a seamless experience from your business. Forbes used MongoDB to build a CMS in just 2 months and a new mobile site in just a month. MongoDB helped the publisher gain more insight into the social sharing of their articles so that they were able to capitalize in real-time on content that was going viral. And there are countless other enterprises who have leveraged MongoDB technology for their CMS: eBay used it to build a media metadata storage for their web properties, Pearson employed MongoDB technology to develop a cloud-based learning management system, and Carfax migrated their vehicle history database into a MongoDB CMS and found that they could service 10x more customers as a result. These are just a few examples of companies who take advantage of the database to build powerful MongoDB CMS. Find out more about how MongoDB can help you develop killer apps that will wow your customers – both internal and external alike. Download our white paper today.
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MongoDB Data Loss
Your data is your lifeblood and any loss can spell disaster for your business. Disaster can strike in many unanticipated forms whether it be a natural disaster, fire, or human error. Devoting time and energy to developing a sound backup and recovery strategy then should be a top priority. By planning ahead and putting a backup and recovery plan in place for your deployment, you can restore operations without MongoDB data loss. Backing up your MongoDB deployment to prevent data loss is easy with MongoDB’s suite of management solutions. You can reliably backup and restore MongoDB with Ops Manager for on prem deployments or with Cloud Manager for cloud-based deployments. Both solutions keep your MongoDB database healthy and optimized by converting manual, complex operations tasks into automated procedures. Point-in-time schedule backups are automatically enabled to help you prevent data loss and restore complete running clusters to any point in time with just a few clicks. Cloud Manager currently supports thousands of deployments of varying sizes, from systems using one to hundreds of servers. Organizations who run their deployments with MongoDB Enterprise Advanced can choose between Ops Manager and Cloud Manager. To get started on crafting a sound backup strategy for MongoDB, download our white paper, ** Backup and Its Role in Disaster Recovery. ** The paper covers: Key considerations when evaluating backup strategies Best practices for backing up and restoring your MongoDB data An in-depth comparison of different MongoDB backup methods
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Intro to MongoDB w/Rust
11 AM ET Are you an experienced software developer looking to build your first application in Rust? You’ve come to the right place! Rust is a powerful and expressive programming language. To get the most out of this webinar we recommend you to watch this video beforehand and Part 1 of this webinar. In the webinar we will cover the following topics: Design and architecture expectations Sync vs async communication (HTTP or gRPC vs messages or queues) Available code generators (synchronous and asynchronous) and limitations Crates that can be used for synchronous communication (for web, such as Axis, Axum, and Rocket) and other purposes, such as dotenv REST implementation tips Middleware Logging vs. tracing Use of traits, asynchronous traits and dynamic dispatch Use of newtype pattern By the end of the webinar you will have learned: What to expect when developing microservices in Rust Dos and don’ts when developing microservices in Rust Areas you can explore for your next Rust development project Join us!
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Most Popular NoSQL Database
If you’re looking for the most popular NoSQL database then look no further than MongoDB. It’s the clear leader across several different measures. DB-Engines, which covers over 200 database systems, consistently ranks MongoDB as the most popular NoSQL database from month to month. And for a second year in a row, DB-Engines also named MongoDB the database of the year in 2015 based on several criteria such as social media mentions, the number of jobs created, professional certifications obtained, and Google searches. Gartner, a well-respected research firm, recently recognized MongoDB as a leader in their Magic Quadrant report on operational database management systems in terms of completeness of vision and ability to execute. In addition to these third-party sources, MongoDB’s popularity is evident in the number of software downloads: currently 40 million downloads and growing. MongoDB is loved by developers for its ease of use and by enterprises for addressing a wide variety of use cases. A popular use case for MongoDB is developing a single view from data that would otherwise would sit in silos across the enterprise. Key features that make this possible include: ** Document data model. ** Easily store and combine any type of data while enjoying sophisticated data access and rich indexing features. Incorporate any type of data, no matter what it looks like or where it comes from. ** Dynamic Schema. ** A flexible and dynamic schema allows for quicker iteration and less time spent preparing the data. ** Expressive Query Language. ** Indexing and aggregation capabilities make it possible to find and filter the data and build powerful features from your data. Find out how businesses gain a competitive edge from their data with MongoDB, including examples of successful single view use cases. Download our white paper today.
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NoSQL Database Challenges
Enterprises rely on decades old relational database technology for many good reasons. Relational databases often support long-standing, mission-critical applications and have robust technology and expert support. But as Big Data use cases and applications continually emerge, companies are turning to NoSQL database technology to satisfy their requirements. NoSQL databases offer many benefits over traditional relational technology including a more flexible data model, horizontal scalability, and superior performance. But along with these benefits comes certain NoSQL database challenges. One trade off is the lack of certain fundamental features that make relational databases so useful for generations of applications. Another challenge with NoSQL technology is that many of these databases serve niche use cases and cannot be applied to a broad variety of needs within the enterprise. MongoDB is unique in a crowded field of NoSQL databases. You benefit from all the innovations of NoSQL with MongoDB while still enjoying the fundamentals of relational technology. It is also a general purpose database so you can use MongoDB to address many different use cases. MongoDB offers the features that serve modern applications: Flexible data models that allow you to easily adjust to ever changing requirements Elastic scalability that accommodates varying system demand High performance in terms of throughput and latency While preserving the powerful elements of relational technology: Expressive query language that enables sophisticated use of your data Strong consistency for you to view and process data in real-time Secondary indexes for quick navigation of data Developers love MongoDB for its ease of use and enterprises love MongoDB for its broad applicability to different use cases. If you’re evaluating which NoSQL technology to invest in, read our white paper to better understand the criteria for selecting the right database for your requirements.
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NoSQL Database Implementation Considerations
The rise of Big Data in this modern era of computing has led many companies to employ NoSQL databases for Big Data applications. It’s no surprise given that this new class of databases does what older, relational technology cannot: scale across inexpensive commodity hardware, accommodate unstructured data, and align well with agile development practices. While the advantages of of NoSQL technology are compelling, the thought of replacing a well-established relational database system can be daunting to most enterprises. So consider a small-scale trial of a database such as MongoDB to test the waters. Since NoSQL databases are typically open source and free to download, you can evaluate this new technology without much risk or cost to your organization. There are many NoSQL options to consider but MongoDB stands out from a crowded field as the most popular with over 40 million downloads, consistent top ranking on DB-Engines, and recognition from Gartner as a leader of operational database management systems. In addition to its popularity, MongoDB is unique in offering the innovations of NoSQL while preserving the best elements of traditional relational databases. MongoDB offers the benefits of NoSQL databases: ** A flexible data model ** for expedient adaptation to the changing business requirements ** High performance ** to support real-time, always on user experience customers demand ** Scalability ** for growing your application to accommodate increasing demand Along with the best of relational databases: ** An expressive query language ** so you can build powerful features with your data ** Strong consistency ** to view real-time updates to your data ** Secondary indexes ** for quick data navigation MongoDB has helped organizations from the hottest startups to large Fortune 100 companies transform their businesses by leveraging their data. All at a lower cost than legacy relational databases. To find out more about how to get started with MongoDB, follow our Architecture Guide or reach out to our sales team.
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NoSQL Database Implementation
NoSQL databases have grown in popularity with the rise of Big Data applications. In comparison to relational databases, NoSQL databases are much cheaper to scale, capable of handling unstructured data, and better suited to current agile development approaches. The advantages of NoSQL technology are compelling but the thought of replacing a legacy relational system can be daunting. To explore the possibilities of NoSQL in your enterprise, consider a small-scale trial of a NoSQL database like MongoDB. NoSQL databases are typically open source so you can download the software and try it out for free. From this trial, you can assess the technology without great risk or cost to your organization. There are many NoSQL options to choose from but MongoDB is the only database that combines the innovations of NoSQL with the strongest elements of traditional relational databases. It’s perhaps one of the reasons why MongoDB has become the most popular NoSQL technology with over 10 million downloads and consistent top ranking on DB-Engines . MongoDB offers the benefits of NoSQL databases: ** A flexible data model ** that allows for expedient adaptation to the changing business requirements **High performance ** to support the real-time, always on user experience customers demand **Scalability ** to easily grow your application to meet demand Along with the best of relational databases: ** An expressive query language ** for building powerful features with your data ** Strong consistency ** for real-time updates to your data ** Secondary indexes ** for quick data navigation MongoDB has helped organizations from cutting-edge startups to the largest companies transform their businesses by leveraging their data. All this is offered at a fraction of the cost of legacy databases. Read more about how to implement MongoDB in your enterprise by downloading our white paper.
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NoSQL Database Security
Data breaches are a serious concern for any enterprise, especially as the frequency and severity of security breaches are increasing. In fact, some researchers on the matter believe that attacks will increase nearly 50% year over year. Securing your database, then, should be a top priority in database administration. NoSQL databases have grown in popularity recently for addressing the limitations of relational databases in meeting modern application requirements. But these databases have their own unique security considerations as many enterprises opt to run NoSQL deployments in the cloud. MongoDB, the leading NoSQL database according to monthly DB-Engines rankings, offers Enterprise Server, the commercial version of MongoDB with advanced security features. The Enterprise version meets strict security and compliance standards with Kerberos and LDAP authentication, Red Hat Identity Management Certification, and auditing. With these advanced security features, you can defend, detect, and control access to your data. MongoDB’s comprehensive security framework features: ** Authentication ** with integration with external security mechanisms including LDAP, Windows Active Directory, Kerberos and x.509 PKI certificates. ** Authorization. ** User-defined roles means you can configure granular permissions for a user or application, based on the privileges they need to do their job. ** Auditing. ** A native audit log lets you track access and operations performed on the database which works for regulatory compliance. ** Encryption. ** MongoDB data can be encrypted on the network and on disk. Protection of data at-rest is an integral feature within the database thanks to the introduction of MongoDB’s Encrypted storage engine. As you evaluate different NoSQL database systems, you should give particular attention to the database’s security architecture. How it handles data security has serious implications on your business. To find out more about how MongoDB defends against data breaches, download our security white paper today.
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NoSQL Databases Pros And Cons
NoSQL databases have emerged in recent years to provide the performance, scalability, and flexibility required of modern applications. This new wave of databases are much better suited for Big Data applications and agile software development practices than its relational counterparts. NoSQL databases offers many benefits, including: ** Flexible Data Model. ** Unlike relational databases, NoSQL databases easily store and combine any type of data, both structured and unstructured. You can also dynamically update the schema to evolve with changing requirements and without any interruption or downtime to your application. ** Elastic Scalability. ** NoSQL databases scale out on low cost, commodity hardware, allowing for almost unlimited growth. ** High Performance. ** NoSQL databases are built for great performance, measured in terms of both throughput and latency. These advantages account for the growing popularity of NoSQL databases. But carefully consider the NoSQL pros and cons before fully committing to a new technology. In fact, you might find that many of these new systems while offering great innovations have also sacrificed some critical capabilities that have made relational databases the gold standard over the past 40 years. MongoDB stands apart from its peers with its Nexus Architecture that incorporates the strengths of relational databases along with the innovations of NoSQL. MongoDB is the only NoSQL options which offers an expressive query language, strong consistency, and secondary indexes. Companies choose MongoDB for this reason and more. Find out more about NoSQL pros and cons by downloading our white paper today.
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Online Vs Offline Big Data
Big data is a big buzzword these days but many are not sure exactly what it means or how to determine a good strategy for their data. Being able to define your data management strategy can mean the difference between being a market winner or loser. So what is Big Data? Simply put, it refers to data sets so massive in volume and complexity that they cannot be effectively managed by traditional software tools. The effort to harness Big Data involves many new technologies that handle data creation, storage, retrieval and analysis of data. Technologies that support Big Data typically fall into two classes: Online Big Data technologies and Offline Big Data technologies. Online Big Data systems offer operational capabilities for real-time, interactive workloads where data is ingested and stored. Examples of these applications include social networking news feeds, real-time ad servers, analytics tools, and CRM applications. In contrast, Offline Big Data systems offer analytical capabilities for retrospective, sophisticated analyses that may touch most or all of the data. Hadoop is an example of an Offline Big Data technology. But online vs offline Big Data isn’t about figuring out which one you need over the other. You most likely need both. MongoDB is an online Big Data technology that serves as an operational data store for today’s Big Data applications. The database also integrates well with many offline Big Data solutions so that you can come up with a complete data solution. Enterprises from startups to large corporations are relying on MongoDB to get them to production on their Big Data applications faster and with less effort and risk. Download our white paper on Big Data to learn more about the differences between Online Vs Offline Big Data and much more.
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Open Source Data Analysis Tools
Data is only as useful as the insights you can get from it. But in today’s era of Big Data where data is growing exponentially and at warp speed, companies are finding it really hard to make meaning of all the variety of data sitting across different systems. The enterprises who develop a smart data management strategy and make use of the best Big Data technologies have the leg up on others in their respective industries. To achieve that advantage, the first thing you need to do is evaluate the technologies exist out there to help you grapple with Big Data analytics. The technologies typically fall into one of two categories: online and offline Big Data. Online Big Data solutions are the MongoDB’s of the world, the operational databases that ingest and store data in real-time for your applications. Offline Big Data solutions, such as Hadoop, complement your online Big Data technologies by processing your data in batch so you can perform retrospective analysis for your operational data and more. You likely need to employ a mix of both to develop a sophisticated analytics platform. Most of these technologies tend to follow the open source model, as is typical these days with modern software. MongoDB is the most popular database for the modern era of Big Data applications. More than a third of Fortune 100 companies and hundreds of thousands of users can attest to MongoDB’s strengths in handling any type of data with flexibility, agility, and at a lower cost. In addition, MongoDB connects seamlessly through a new connector to industry-standard business intelligence and data visualization tools including Tableau, SAP Business Objects, Qlik, and IBM Cognos Business Intelligence. An extensive partner network of over 1,000 providers are also available to help you build a powerful analytics engine with MongoDB. Find out more about how MongoDB helps organizations of all sizes work successfully with open source data analysis tools. Download our white paper today.
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Operational Vs Analytical Big Data
In this modern era of Big Data where data is getting too unwieldy for older generations of technology to handle, there’s a new class of technologies sprouting up to meet the need. To succeed and pull away from the competition, you need a strong data management strategy that involves the right mix of technologies that meet your requirements. These new technologies that have arisen in response to Big Data handle data creation and storage, retrieving and analyzing data. When you’re evaluating the different technologies to use, you typically encounter operational vs. analytical Big Data solutions. Operational Big Data systems provide operational features to run real-time, interactive workloads that ingest and store data. MongoDB is a top technology for operational Big Data applications with over 10 million downloads of its open source software. Analytical Big Data technologies, on the other hand, are useful for retrospective, sophisticated analytics of your data. Hadoop is the most popular example of an Analytical Big Data technology. But picking an operational vs analytical Big Data solution isn’t the right way to think about the challenge. They are complementary technologies and you likely need both to develop a complete Big Data solution. MongoDB works well with Hadoop thanks to an API integration that makes it easy to integrate the two solutions. Many of our customers, such as the City of Chicago, have built amazing applications never before possible as a result of combining operational and analytical technologies. Download our white paper on Big Data to learn more about the differences between operational vs analytical Big Data and much more.
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