Friday Feb 19, 2016
Friday Jan 17, 2014
By Klaker-Oracle on Jan 17, 2014
We have released yet another great video customer video, this time with Stubhub.
Many customers are still pulling data out of their data warehouse and shipping it to specialised processing engines so they can mine their data, run spatial analytics and/or built multi-dimensional cubes. The problem with this approach, as the team at Stubhub points out, is that typically when you move the data to these specialised engines you have to work with a subset of the data that is sitting in your data warehouse. When you work with a subset of data you immediately start to impose compromises on your analytical workflows. If you can't work with all your data then you can't be sure that your analytical model is as good as it could be and that could mean losing customers or missing out on additional revenue.
The other problem comes from everyone using their own favourite tool to do their analysis: how do you share your discoveries, how do you develop a high level of corporate-wide analytical skills?
The data warehouse insider is written by the Oracle product management team and sheds lights on all thing data warehousing and big data.
- Big Data SQL Quick Start. Partition Pruning - Part7.
- Big Data SQL Quick Start. Predicate Push Down - Part6.
- SQL Pattern Matching Deep Dive - Part 3, greedy vs. reluctant quantifiers
- Common Distribution Methods in Parallel Execution
- Big Data SQL Quick Start. Joins. Bloom Filter and other features - Part5.
- Is an approximate answer just plain wrong?
- Big Data SQL Quick Start. Security - Part4.
- Oracle OpenWorld 2016 call for papers is OPEN!
- SQL Pattern Matching Deep Dive - Part 2, using MATCH_NUMBER() and CLASSIFIER()
- In-Memory Parallel Query