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The Evolution of Deployment Models in the Big Data Era

With the advent of big data, the deployment models for managing data are changing. The traditional data warehouse is typically implemented on a single, large system within the data center. The costs of this model have led organizations to optimize these warehouses and limit the scope and size of the data being managed.

However, when organizations want to leverage the massive amount of information generated by big data sources, the limitations of the traditional models no longer work. Therefore, the data warehouse appliance has become a practical method of creating an optimized environment to support the transition to new information management.

The big data appliance model

When companies need to combine their data warehouse structure with big data, the appliance model can be one answer to the problem of scaling. Typically, the appliance is an integrated system that incorporates hardware (typically in a rack) that is optimized for data storage and management.

Because they are self-contained, appliances can be relatively easy and quick to implement, as well as offer lower costs to operation and maintain, Therefore, the system will be preloaded with a relational database, the Hadoop framework, MapReduce, and many of the tools that help ingest and organize data from a variety of sources.

It also incorporates analytical engines and tools to simplify the process of analyzing data from multiple sources. The appliance is therefore a single-purpose system that typically includes interfaces to make it easier to connect to an existing data warehouse.

The big data cloud model

The cloud is becoming a compelling platform to manage big data and can be used in a hybrid environment with on-premises environments. Some of the new innovations in loading and transferring data are already changing the potential viability of the cloud as a big data warehousing platform.

For example, Aspera, a company that specializes in fast data transferring between networks, is partnering with Amazon.com to offer cloud data management services. Other vendors such as FileCatalyst and Data Expedition are also focused on this market. In essence, this technology category leverages the network and optimizes it for the purpose of moving files with reduced latency.

As this problem of latency in data transfer continues to evolve, it will be the norm to store big data systems in the cloud that can interact with a data warehouse that is also cloud based or a warehouse that sits in the data center.

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