Resources

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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Testing Database Performance

Achieving high performance with your application database when it is in production is absolutely critical. Any downtime or service interruption could spell disaster for your business. Testing database performance and establishing benchmarks, then, should be a regular practice of your operations team to gauge system performance. It gives insight into which elements to tweak for even better performance. So when MongoDB released the latest version, 3.0, and touted its enhancements to performance, we thought to run a series of tests to support that claim. Using YCSB, a popular testing framework used to compare the performance of NoSQL databases, we saw between 7 to 10 times better performance compared to the MongoDB 2.6. United Software Associates, an independent third party, tested MongoDB performance against other leading NoSQL vendors. The results found MongoDB far exceeded the others in terms of throughput and latency across a number of configurations. For this reason and more, MongoDB leads all NoSQL database products according to the latest DB-Engines rankings. Find out more about our tests by downloading the comparative benchmarks.

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Database Schema Example

Choosing the right database schema for your application impacts performance and your ability to quickly adapt to evolving business requirements. A flexible data model, such as the one found in MongoDB, lets you store or aggregate any type of data and dynamically change schema without application downtime. Data in MongoDB is stored in documents and similarly structured documents are typically organized into collections. Also, MongoDB documents tend to have all data for a given record in a single document. Let’s use a database schema example from a blogging application to help illustrate the document model in MongoDB. For the blog, you could model the data as two collections -- one to represent visitors and the other for the articles. Each document of this blog database could contain multiple comments, multiple tags, and multiple categories, each expressed as an embedded array. Here is an illustration of this database schema example: To learn more about MongoDB’s flexible data model and its benefits, read our white paper.

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Designing A Database Schema

Designing a database schema is one of the important first steps in the design phase of an application. Choosing the right one not only affects the application’s performance, it also determines how flexible your application is to adapt to future requirements or evolving business needs. Schema design in traditional relational databases must be determined upfront before any data can be added to the application. This inflexible approach makes it hard to update your application to meet new data types. If you can’t keep up in today’s world of Big Data and ever evolving data types you risk losing out to the competition. When designing a database schema for your application, consider using the flexible schema design of the newer generation of NoSQL databases. A flexible data model is better suited to handle the large volume and variety of data typically generated by modern applications. A flexible data model lets you: Quickly adapt to the evolving needs of the business Simplify combining data from multiple sources Save time updating existing data and incorporating new types of data into your system MongoDB, the leading NoSQL database, offers a document data model that allows for iterative and adaptive data modeling without a predefined schema. With MongoDB, you can dynamically modify the schema without interruption, simplify your design and reduce the overall effort to develop applications. To learn more about designing a database schema with MongoDB read the white paper or contact us.

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Data Management Strategy

Most organizations maintain many different systems which each have their own unique rules and processes for handling data. But there’s little to gain from having your data live in silos. A sound data management strategy can help you unlock unlimited opportunities from your data by unifying data across the enterprise. But developing a single view of data across the organization is not easy and many successful companies have failed in this pursuit. MongoDB can easily help you achieve that single view. As a next generation database offering a flexible data model, MongoDB allows for enterprise-wide data integration, all for less money than traditional database technologies. How MongoDB makes single view possible: ** Manages any type of data. ** Be able to build powerful applications that incorporate any type of data from any source. **Dynamic schema. ** Be able to iterate more quickly thanks to flexible, dynamic schema. Teams are productive as they spend less time prepping the data and more time moving the project forward. ** Less cost. ** Efficiency gains greatly impact the bottom line while being able to use commodity hardware means your projects can be a fraction of the cost associated with a relational database. To learn more about what a smart data management strategy with MongoDB can do for you, download our white paper.

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Building Real-time Mobile Apps that Scale

Mobile has impacted every aspect of our lives. Whether we are looking to schedule grocery deliveries, find our next apartment, or schedule a doctor appointment, we can confidently say “there’s an app for that”. Conditioned by years of chart topping apps like TikTok, Instagram, and Spotify, mobile users expect to be able to collaborate in real-time without noticeable delays. Users also expect to feel connected at all times even if their internet connection is spotty – nothing’s more frustrating than seeing a gray screen with the dreaded “no internet connection”. This means mobile developers need to build reactive, collaborative, always-on experiences into all of their mobile apps. But building real-time mobile apps is hard. It requires keeping a mobile database and the backend in sync at all times regardless of connectivity issues or data conflicts.

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MongoDB Atlas Security

At MongoDB, we want you to have full confidence in the security and resiliency of the systems and technology that we maintain, and the products that we provide to facilitate secure growth and innovation in your company. This document conveys the depth of our commitment to customer trust by providing a detailed understanding of MongoDB Atlas Security controls and capabilities. Learn more about how MongoDB has strong security defaults to help protect your data throughout the data lifecycle and how the developer data platform has gone through stringent requirements to attain various certifications like HIPAA, GDPR, ISO, PCI, FedRAMP Moderate for various industry-specific, data locality and security controls and regulations.

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MongoDB for Public Sector

Public sector organizations — government agencies, defense contractors, state and local government entities, and more — face a set of challenges unique to this vertical. These organizations must balance the need to comply with strict regulations like FedRAMP, CJIS, ITAR, and more with the external pressure to innovate in alignment with progress in the private sector. When you add bureaucratic red tape and complicated internal decision-making hierarchies to the mix, public sector organizations face the perfect storm of obstacles to digital transformation. To navigate these challenges and emerge successfully on the other side of digital transformation, public sector organizations need a data platform underpinning their mission-critical applications. Discover how MongoDB is powering mission-critical applications across government agencies, education institutions, and more.

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MongoDB for Public Sector

Government agencies constantly face impediments to progress. From complying with regulations to slow-moving upgrades of legacy systems, innovation through development of modern applications is expensive, time consuming, and difficult to Achieve. With MongoDB Atlas for Government, built on the leading developer data platform, your development teams can accelerate innovation and break down data silos, all in a secure environment compliant with FedRAMP regulations. Learn more about how MongoDB helps public sector organizations overcome their biggest challenges.

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Generative AI With MongoDB Atlas on AWS Datasheet

MongoDB Atlas on Amazon Web Services (AWS) is the fast and secure way to bring enterprise data to gen AI. The Atlas developer data platform enables you to unify operational data, vector data, and meta data in a single place and launch scalable gen AI-powered experiences, backed by enterprise data, in minutes. Read our datasheet to learn how, with MongoDB and AWS, you can easily build enterprise-ready applications that: get you to market fast with minimal cost and complexity. run on proven, tested components. scale efficiently and optimize resources.

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