Borderless analytics use cases – cloud data labs, cloud bursting, cloud disaster recovery

By Rob Armstrong

In my last blog, I looked at how the experience of Teradata integrating data and analytics across the enterprise has evolved from the Centralized Data Warehouse to the world of Borderless Analytics. In this blog I wanted to delve a bit deeper into a few of the borderless analytics use cases that can leverage and exploit the new world.

Today I will focus on the opportunities brought to the table by our Borderless Analytics tools (Unity™ and QueryGrid™) and the cloud platforms running the Teradata Database. These alternatives to on premise, physical hardware significantly change the cost equation as well as the resources necessary to manage and maintain the analytic ecosystem. Of course not all clouds are equal so these few cases will explore both the public cloud as well as the managed private cloud options.

The three borderless analytics use cases I’ll hiborderless analytics use cases illustrationghlight are:

  • Cloud Data Labs – Allowing greater end user self-service and exploration
  • Cloud Bursting – Load and resource balancing to manage peak periods
  • Cloud Disaster Recovery – Providing lower cost, off premises environments for disaster recovery

Cloud Data Labs

The idea of a data lab is not new, but they have evolved over the years. I remember doing “user data spaces” back in the early 90’s where we gave the users some space and let them create tables for data exploration. Back then the tools were limited, the users needed special training, and they shared resources with the production platform. All of these limited the effectiveness of the environment.

Data Labs improved over time with better tools and mixed workload management tools to isolate the exploration from impacting on the production workloads. The big need though was that the data lab needed to be co-located with the production data so users could not only load and explore new data but also integrate the core production and reference data into their query as well.

Today, users can very easily instantiate a public cloud Teradata Database and have control over their resources and processes with little impact to the production warehouse. With Unity and QueryGrid users can also now interact with the core data as necessary without having to get IT resources involved to constantly replicate data between the systems.

Now when the user is done with their data lab trials, they can easily terminate the public cloud and reduce the cost and overall environment processes.

Cloud Bursting

Cloud Bursting is a capability that can help during peak times or during unexpected needs. The idea here is to have a secondary cloud environment that is being kept up to date with a production system. Using Unity, workload can be directed to each system as service level and performance demands. Under normal operations, the workload may be less rigidly controlled but during peak or season demands, you can use the Unity routing rules to ensure the highest priority workloads, i.e. web access, are not interfered with by other workload such as internal reporting.

This has been done in the past with dual on premises systems but with the cloud environment you can lower the cost and overall data center management of the secondary system while maintaining the same high level of responsiveness.

Cloud Disaster Recovery

In a similar vein to the bursting example, companies can now lower the cost and complexity of have accessible disaster recovery. Most companies have moved beyond the “tape drive” mentality of DR and have gone to secondary systems that are kept as warm or active copies of the production data warehouse. But real DR requires geographic separation as well as rigorous methodologies to ensure data is accurately maintain and available.

With Borderless Analytic environments, companies can easily have a cloud solution housing the separate and protected DR copy of data. Using Unity, the data can easily be managed across the two systems and when disaster strikes, workload can quickly, and transparently, be routed to the secondary environment.

Now how real time the data is kept in synch and how much performance or how active the second box needs to be becomes a business discussion that spans a spectrum of solutions.

Borderless Analytics – Agility without Anarchy

It is becoming a complicated world for data management and business analytics. Users are asking for more capability with less interference. But in the rush to be agile, we cannot allow anarchy because that creates more chaos. To be agile you need to have the coordination and cooperation across the total analytic ecosystem, and that is exactly what a Borderless Analytic solution provides.

One thought on “Borderless analytics use cases – cloud data labs, cloud bursting, cloud disaster recovery

  1. avatarTina Corvin Petersen

    These “sue” cases instead of these “few” cases. Marketing needs an editor. The last few articles I’ve read could benefit from another set of eyes, not just for typos, but also expression of content. I’m not sure if you could tempt me back into contract work for Teradata, but maybe. I was just curious to see what the company has been up to since I left and I see there is room for improvement in the marketing materials, although I’m happy to see the Facebook presence.

    Reply

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