Three Step Process for Offloading Data and Workloads to Hadoop

Carole Gunst's picture

Today’s enterprise data warehouse is rapidly filling with rising volumes of data from increasingly varied sources. Some analysts estimate that companies use a third of their structured data for analytics. While some IT departments suspect that this is the case, they don’t know how to identify the two thirds of structured data that’s not getting used for analytics.

Here’s a three-step process that will help you get started identifying data and workloads that can be offloaded to Hadoop:

Step 1 – Identify data and workloads that can be offloaded

Step 2 –  Prioritize unused data and related workloads for offload

Step 3 – Offload identified data and workloads to Hadoop

Attunity Visibility can help. It is the only software solution that provides detailed insight into business activity, data usage and resource consumption so that you can:

  • Measure data usage and user activity by business lines and departments within your organization
  • Identify individual application users and business intelligence (BI) reports that impact system performance on the data warehouse.
  • Rebalance data and workloads to the right platform by identifying unused data and resource-intensive workloads
  • Optimize multiple data delivery platforms with an integrated dashboard to diagnose and assess performance bottlenecks
  • Measure and audit the utilization of information assets to make smarter infrastructure investments

Attunity Visibility ties users and application activity to data usage and performance metrics of data warehouses. It continuously collects, stores, and analyzes all queries and applications against data warehouses. And Attunity Visibility then correlates that with data usage and workload performance metrics in a centralized repository that provides detailed usage and performance metrics for the entire data warehouse.

 To learn more, download A Practical Guide to Data Warehouse Offload and Optimization with Hadoop.