4 Common Mistakes to Avoid When Optimizing Your Data Warehouse

Data warehouse optimization can be done — but it has to be done right.

What does it take to breathe new life into a data warehouse?

In an ideal world, all data warehouses would be future-proofed, always fast and simple to use.

Unfortunately, reality gets in the way.

Technologies grow obsolete, businesses apply ad-hoc solutions, IT professionals lack the time to perform maintenance — soon, a once state-of-the-art technology becomes a slow, complex and confusing data warehouse.

Many businesses choose to walk away from their legacy data warehouse altogether, but for many organizations, their warehouse is too embedded in their operations. These businesses must instead engage in a data warehouse optimization project in order to get as much out of their solution as possible.

Data warehouse optimization can be done successfully, but it can be a delicate task. In our guide, 4 Common Mistakes to Avoid When Optimizing Your Data Warehouse, we discuss four common pitfalls that we’ve seen businesses fall into when optimizing their data warehouse. Specifically:

  • Assuming a data warehouse appliance is the answer. 
  • Believing that open-source, Big Data technologies will get you where you need to be.
  • Thinking that the cloud’s low price and “elastically scaling data stores” will solve everything. 
  • Believing that continual data model and Extract Transform Load (ETL) tuning will give you the performance gains you need.

In the guide, we discuss why these four mistakes can be so dangerous in a data warehouse optimization project, and what approach businesses should take to successfully breathe new life into their data. Just fill out the form and click “Submit” to access your free copy.

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