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whitepaper

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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