Organizations generate enormous volumes of data and have a variety of tools to analyze, process, summarize and monetize information by different departments in many locations. Leading companies are turning to MongoDB to serve as the central data hub for all of their data because of its ease of use, scalability and low cost of ownership. MongoDB provides a universal repository in which data can easily be stored, processed and served to other applications.
Organizations have a wide variety of tools for analysis of their data, including OLAP, columnar databases, ETL tools, SAS and Hadoop. These tools consume data from multiple operational systems and file systems. They can then generate analytical results or reformatted versions of the data, which are shared with other areas of the enterprise and other applications. Organizations struggle to gain consensus on a schema for the various sources of data.
- High Volume Data Feeds. Data rapidly arrives from source systems or third party vendors, including market data and social media.
- Multiple Schemas. Data exists in many different schemas, and in many different structures, including log data, delimited data, images and user-generated content.
- Scale. Data volumes can easily grow to terabyte and petabyte scale. Accommodating growth should allow predictable, incremental cost, and it should not incur downtime.
- Data Lifecycle Management. When the lifecycle of the data is predictable, it should be simple to establish policies to expire data automatically from the system or to maintain a rolling window of data over time.
- Precise Access. Multiple applications need to be able to export portions of the data for analysis. It should be easy to specify selection criteria, and it should be efficient to export the data to downstream applications.
- Processing. In many cases it is more efficient to analyze the data in place. This analysis can involve simple aggregation, or it might consist of complex processing, including machine learning algorithms.
When addressed by traditional technologies such as relational databases and storage appliances, these challenges lead to a fragmented labyrinth of data and expensive projects that struggle to realize value. MongoDB is a NoSQL database that addresses today's data storage challenges.
- Document Database. MongoDB is a NoSQL database in which all data can be represented easily as documents, including binary data. Each document is self-describing, eliminating the need to establish one schema for all data. New systems can be integrated into the Data Hub easily and flexibly over time without affecting existing integrations.
- Ad Hoc Queries. A full query language and secondary indexes enable applications to extract precisely the information needed for analysis and processing.
- Real-Time Access. MongoDB operates in real-time. As data arrives it becomes immediately accessible to other systems.
- Horizontal Scaling. MongoDB's NoSQL technology allows for auto-sharding, making it easy to scale applications horizontally on commodity hardware, hosted or cloud-based, to accommodate growing user bases and increased throughput.
- Native Analytics. Using the integrated aggregation framework and Map/Reduce, MongoDB can calculate aggregates and conduct sophisticated analyses in place without exporting data to other systems.
- Improved Time to Value. MongoDB indexes data and makes it available for processing and export faster and more efficiently than traditional file systems, enabling programmers and analysts to deliver results quickly.
- Operational Simplicity. MongoDB's document data model and support for all popular programming languages makes storing and accessing any type of data simple and universal, reducing operational complexity.
- Enterprise-Grade Reliability. MongoDB provides data durability and continuous availability across data centers using native replication.
- Reduced Total Cost of Ownership (TCO). MongoDB is open-source and deployed on commodity hardware. When compared to proprietary storage and RDBMS solutions, MongoDB is far more cost-effective.