Big Data can take both online and offline forms. Online Big Data refers to data that is created, ingested, trans- formed, managed and/or analyzed in real-time to support operational applications and their users. Big Data is born online. Latency for these applications must be very low and availability must be high in order to meet SLAs and user expectations for modern application performance. This includes a vast array of applications, from social networking news feeds, to analytics to real-time ad servers to complex CRM applications. Examples of online Big Data databases include MongoDB and other NoSQL databases.
Offline Big Data encompasses applications that ingest, transform, manage and/or analyze Big Data in a batch context. They typically do not create new data. For these applications, response time can be slow (up to hours or days), which is often acceptable for this type of use case. Since they usually produce a static (vs. operational) output, such as a report or dashboard, they can even go offline temporarily without impacting the overall goal or end product. Examples of offline Big Data applications include Hadoop-based workloads; modern data warehouses; extract, transform, load (ETL) applications; and business intelligence tools.
Organizations evaluating which Big Data technologies to adopt should consider how they intend to use their data. For those looking to build applications that support real-time, operational use cases, they will need an operational data store like MongoDB. For those that need a place to conduct long-running analysis offline, perhaps to inform decision-making processes, offline solutions like Hadoop can be an effective tool. Organizations pursuing both use cases can do so in tandem, and they will sometimes find integrations between online and offline Big Data technologies. For instance, MongoDB provides integration with Hadoop.
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