Reputation: 20620
I am thinking of a simple way to build operational data warehouse leading to (near) real-time BI.
For example, I am using SQL server platform for database applications. One application is for OLTP data entry and the other with data warehousing, eventually used for SSAS and BI reporting.
I know common infrastructure for BI is
OLTP database -> Periodically updated data warehouse -> Analytic cube -> Reporting
,
where OLTP (normalized) database handles transactional operations and data warehouse stores updated data for BI operations.
But I am thinking of two ways to make data warehouse real-time.
The reasons for #1 and #2:
Of course, unknown part for #1 is how slow transaction will be when the analysis cube is being updated. But method #2 solves this problem especially when data update for data warehouse is asynchronously done.
I would like to ask if there is any gotcha I missed or if there is any better method to build operational data warehouse.
Upvotes: 0
Views: 344
Reputation: 9375
icCube is an in-memory OLAP server written in JAVA that can source its data from any relational DB. It supports incremental load for near real-time BI.
The DB servers won't be impacted when the cubes are updated as icCube is building the cubes in a separate process and then all the queries are done to this external process. You'll have to configure icCube to check periodically for updates in the DB servers (e.g., you can have a trigger table for a quick/low-cost check).
It supports MDX/XMLA for Excel access for example and has its own Web Reporting package.
Upvotes: 1