Resources

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.

View white paper →

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.

View white paper →

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.

View white paper →

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.

View white paper →

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.

View white paper →

RAG: Beyond the Chatbot

As enterprises seek innovative ways to leverage AI for data access and insights, retrieval-augmented generation (RAG) has emerged as a powerful tool. Initially popularized by ChatGPT’s conversational capabilities, RAG uses an advanced approach to data retrieval and generation that seamlessly integrates into complex enterprise workflows. This report explores the expansive potential of RAG for enterprise applications and provides technology leaders perspectives to inform their implementation strategies.

View datasheet →

Bridging the Application Modernization Gap

The Crucial Role of People, Skills, and Training in IT Transformation Modernization is a holistic transformation across people, processes, and technology. It's crucial for embracing cloud, automation, AI, and data-driven decision-making. This white paper explores what it truly means for an organization to become modern today. The problem: legacy systems and stifled innovation. Rigid legacy systems create technical debt, hindering innovation and scalability. Traditional modernization, characterized by manual tasks and slow progress, often falls short. What "modern" looks like: agile and cost-effective. Becoming modern means achieving resilience, flexibility, and speed. It's about faster delivery, reduced risks, and lower costs by eliminating expensive legacy overhead. Key insights: **Technology's role**: Generative AI automates complex tasks, accelerating modernization. **Flexible data**: Modern databases like NoSQL build a resilient data foundation. **AI-ready**: Modernizing data is essential for harnessing generative AI, which relies on accessible, high-quality data. **Strategic approach**: True modernization is strategic re-architecture, addressing the full stack for long-term agility. Unleash innovation To drive innovation, reduce debt, lower costs, and prepare for AI, you need a clear path. This white paper provides the strategies to thrive in a competitive market. Download now to bridge your modernization gap and secure your future.

View white paper →

Modernizing Legacy Applications with AI

###Challenges, insights, and evolution with Forward by TDWI Staying competitive today demands leveraging modern technologies. Yet, modernizing legacy applications is complex. With a forward by leading technology analyst firm TDWI, this white paper examines this critical process, focusing on integrating AI, especially Large Language Models (LLMs), into your modernization workflow. Discover how to evolve from initial misconceptions to truly effective, innovative methodologies. Key insights: **The Reality of AI Transformation**: Understand why LLMs can't seamlessly transform entire code repositories due to context window limitations and legacy system complexity. **The power of a structured approach**: Learn why a phased, comprehensive methodology is essential. **AI's role beyond just code**: AI, particularly LLMs, can do more than just rewrite code. It enhances documentation automation, significantly improves testing and optimization, and enables "agentic modernization" throughout the entire lifecycle. **Building trust and de-risking**: Explore how pilot programs provide a controlled environment to scope complexity, evaluate codebases, identify requirements, and gain stakeholder buy-in. **The critical role of data**: MongoDB's data engineering expertise is crucial for modernization. If legacy systems are holding you back, this white paper provides essential insights from real-world experience, balancing AI's potential with proven methodologies to guide your successful transformation. Download the white paper now to navigate complex modernization challenges and unlock a faster, more efficient, and innovative future.

View white paper →

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.

View white paper →

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.

View white paper →

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.

View white paper →

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.

View white paper →