Organizations can use MongoDB and Hadoop to perform risk modeling, predictive analytics and real-time data processing. The Apache Hadoop framework is a collection of open source modules, including Hadoop Distributed File System and Hadoop MapReduce, that work with MongoDB to store, process and analyze large amounts of data. By using MongoDB, you can easily add new features and attributes to your online applications and websites using a single database and with high availability. Content management systemsĬontent management systems (CMS) are powerful tools that play an important role in ensuring positive user experiences when accessing e-commerce sites, online publications, document management platforms and other applications and services. This has proved beneficial for several business sectors, including government, financial services and retail. MongoDB handles the conversion of JSON and JSON-like documents, such as BSON, into Java objects effortlessly, making the reading and writing of data in MongoDB fast and incredibly efficient when analyzing real-time information across multiple development environments. Real-time analyticsĪs companies scale their operations, gaining access to key metrics and business insights from large pools of data is critical. This flexibility lets you aggregate data across multiple environments with secondary and geospatial indexing, giving developers the ability to scale their mobile applications seamlessly. MongoDB’s JSON document model lets you store back-end application data wherever you need it, including in Apple iOS and Android devices as well as cloud-based storage solutions. However, it can’t match MongoDB’s flexibility for handling structured and unstructured data sets or its performance and reliability for mission-critical cloud applications. Cassandra uses a traditional table structure with rows and columns, which enables users to maintain uniformity and durability when formatting data before it’s compiled.Ĭassandra can offer an easier transition for enterprises looking for a NoSQL solution because it has a syntax similar to SQL it also reliably handles deployment and replication without a lot of configuration. While Cassandra (link resides outside IBM) and MongoDB are both considered NoSQL databases, they have different strengths. But MongoDB’s less-restrictive format and higher performance make it a better choice, particularly when availability and speed are primary concerns. A mature solution, MySQL is useful for a variety of situations including website databases, applications and commercial product management.īecause of its rigid nature, MySQL is preferable to MongoDB when data integrity and isolation are essential, such as when managing transactional data. In this format, schemas are used to create database structures, utilizing tables as a way to standardize data types so that values are searchable and can be queried properly. MySQL (link resides outside IBM) uses a structured query language to access stored data. Here are some common solution comparisons and best use cases that can help you decide. With so many database management solutions currently available, it can be hard to choose the right solution for your enterprise. Since MongoDB employs a dynamic schema design, users have unparalleled flexibility when creating data records, querying document collections through MongoDB aggregation and analyzing large amounts of information. Formatted as Binary JSON (Java Script Object Notation), these documents can store various types of data and be distributed across multiple systems. MongoDB documents or collections of documents are the basic units of data. This not only simplifies database management for developers but also creates a highly scalable environment for cross-platform applications and services. As a NoSQL database solution, MongoDB does not require a relational database management system (RDBMS), so it provides an elastic data storage model that enables users to store and query multivariate data types with ease. MongoDB (link resides outside IBM) is an open source, nonrelational database management system (DBMS) that uses flexible documents instead of tables and rows to process and store various forms of data.
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