Big Data Analytics Architecture

Data is only as useful as the insights you can glean from it. Building a sophisticated analytics platform for your data can spell the difference between outperforming or lagging behind your competitors. However, this pursuit is not easy as today’s applications have to handle data that is growing too fast, moving too fast, and too diverse in content to manage. Big Data has become a popular catchphrase to describe this challenge.

To develop a solutions architecture for Big Data analytics, consider the difference between online and offline Big Data technologies. Online Big Data technologies, such as the database MongoDB, ingest and store data in real-time and in an operational capacity. Offline Big Data solutions such as Hadoop, process data in batch for retrospective analyses that may touch most or all of a company’s data. These technologies are complementary and to develop a complete analytics solution, you’ll likely need to employ both.

As the operational data store for more than a third of Fortune 100 companies, MongoDB has emerged in recent years as the world’s leading database for Big Data solutions. Companies turn to MongoDB for a more flexible, agile, lower cost way to bring Big Data analytics solutions to fruition.

Find out more about how MongoDB helps organizations of all sizes develop a successful Big Data analytics architecture. Download our white paper today.