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Unity Data Mover (DM) is built to handle multiple requests to move objects concurrently. Understanding how DM processes these requests is benefitial to those looking to get the most out of their DM installations.

The easiest way to explain how DM handles work is to take a user request to move data and follow it through its life cycle. Along the way, we'll see how various user controlled settings affect the request and provide suggestions for how one might improve work throughput.

When it comes time to test your latest database application, Teradata Data Mover (DM) can easily be used to grab real world data from your production system to populate your test system.

One of the big advantages that Teradata Data Mover (DM) provides is built-in parallelism. The underlying utilities that Teradata DM uses such as Teradata Parallel Transporter API do have methods available for users to do parallel work but either are limited to a single client machine or require the user to build their own code framework. Teradata DM takes care of all the hard work and puts the world of multiple client machine parallelism at your finger-tips.

But how to make best use of this parallelism for your big jobs? There’s a lot of power under the hood but it might not be obvious how to put that power to work. Here we’ll talk about what parallelism features are available and provide tips for how to use those to get your big data moving faster.

Teradata Data Mover makes moving tables and other objects between Teradata systems easier than ever. However, the initial release, 13.01, does require manual tuning and configuration in order to get the best performance results. Here are some tips and recommendations to improving Data Mover performance.

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