Discover a no-coding approach to shift SQL ELT workloads to Hadoop
Several organizations are reaping the benefits of offloading ELT/ETL workloads to Hadoop.
But there’s a catch: Re-writing heavy ELT workloads, like complex data warehouse SQL processes to run in Hadoop usually takes highly-skilled Java, Hive or Pig programmers (if you can find them at all).
Not anymore. Learn a simple, graphical approach to offload ELT processing & data from your data warehouse to Hadoop – without writing a single line of Java, Hive or Pig! Our new how-to guide, complete with screen shots, walks you through:
- Extracting source data from the data warehouse
- Joining & sorting the source datasets using a MapReduce ETL job
- Loading the final dataset into the data warehouse
- Executing & scheduling your Hadoop ETL jobs
- Monitoring Your Hadoop ETL Jobs