Wait, You Can't Analyze That Data Yet!
Before you can start analyzing data, you have to join and sort your sources, profile your data, find and fix problems and duplicates, calculate means and averages, and a hundred other little transformations that turn raw data into something you can get real value from.
Preparing big data for analysis takes 60% to 80% of the total time allotted for the project.
Do any of these common frustrations sound familiar?
- Initial data preparation setup takes weeks or months before you can even begin digging into the data.
- When you need to add a dataset, or select a different set of columns, or any other change to the data, you have to wait for IT to make that change.
- You have miscommunications between the two teams and end up with the wrong data, and then have to wait again.
What if you could run quick tests, realize you didn’t have quite the right data, tweak the data preparation, and run the test again, in minutes?
What if a single platform could do all of that, without coding?
Actian's ParAccel Hadoop ETL/DQ can help you:
- Vastly reduce time spent preparing data.
- Audit all data, not just samples. (Don’t miss unusual problems that can crash the system.)
- Speed deployment with automatic scaling.
- Reduce cost and complexity of deployment by using inexpensive industry standard servers.
- Improve green efficiency and save energy dollars by making optimum use of available hardware.
- Easily integrate with existing analytics: R, SAS, SPSS, etc.
- Rapidly feed prepared data into high performance analytics databases: Actian Vectorwise, ParAccel Database, Actian Versant, etc.