Hash keys are often used as primary keys in Data Vault environments. When loading dimensional data marts from a Data Vault schema, it seems to be obvious to use them as dimension keys, too. At least in combination with Oracle Database In-Memory, this is not a good idea.
With Oracle Database In-Memory, it is possible to populate individual columns of a table into the In-Memory Column Store. This is very useful for large tables, if only the frequently used columns should be populated to safe memory. The SQL syntax to define this seems to be straight-forward, but does not always work as expected.
Analytic Views, in combination with Attribute Dimensions and Hierarchies, are very useful for ad-hoc queries in a Star Schema. But how about the performance of this Oracle 12.2 feature? I wanted to know it and analyzed the execution plans of some simple queries.
In summer time, the nights are very short. For some Data Warehouses, this is the case all year round, but not because of late sunset and early sunrise. The night is not long enough to finish all the ETL jobs. Long-running load jobs that run for several hours are not a seldom situation. Here some tips how this can be avoided.
On 13 and 14 September 2017, the Trivadis Performance Days 2017 will take place in Zurich. As in previous years, it will be an exciting event for everybody interested in Oracle performance features. For me, it will be a very special adventure this year.
Analytic Views are one of the main features for Business Intelligence introduced with Oracle 12c Release 2. They provide a lot of new possibilities for analytical queries on star schemas. This blog post gives an overview of the new functionality.
Oracle 12c Release 2 introduced Analytic Views, a new set of metadata objects that are very useful for Data Warehouses and Business Intelligence applications. In the first blog post about this new feature I will have a detailed look at two of the new object types: Attribute Dimensions and Hierarchies.