5 Steps to Offload Your Data Warehouse With Hadoop
According to Gartner, nearly 70% of all data warehouses are performance and capacity constrained -- so it's no surprise that total cost of ownership is the #1 challenge most organizations face with their data integration tools.
Meanwhile, data volumes continue to grow. With no end in sight to the digital explosion, organizations are looking at Hadoop to collect, process, and distribute the ever-expanding data avalanche.
This guide offers expert advice to help you get started with offloading your EDW to Hadoop. Follow these 5 steps to overcome some of the biggest challenges & learn best practices for freeing up your EDW to do the work it was meant to do: provide the insights you need through high-performance analytics and fast user queries.
- Understand and define business objectives
- Get the right connectivity for Hadoop
- Identify the top 20% of ETL/ELT workloads
- Re-create equivalent transformations in MapReduce
- Make your Hadoop environment enterprise-ready