Most companies use Hadoop for big data analytical workloads. The problem is, on-premises Hadoop deployments have failed to deliver business value after it is implemented. Over time, the high cost of operations and poor performance places a limitation on an organization’s ability to be agile. As a result, data platform teams are looking to modernize their Hadoop workloads to the data lakehouse.
In this video, learn about:
- Use cases for modernizing Hadoop workloads
- How the data lakehouse solves the inefficiencies of on-premises Hadoop
- Success stories from organizations that have modernized Hadoop with the data lakehouse on Dremio
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