University graduate in Computer Science, Finance, Economics, Mathematics, Business, or related discipline Strong analytical skill and sensitive to figures Highly motivated and committed with strong communication skills, be able to work under pressure Good command of English and Chinese, both written and spoken Conversant with MS Office applications and Chinese Word Processing On-the-job training will be provided. Support implementation of Core Banking System by ensuring business operations readiness and participating in future workflow design and parameters set up. Partner with different departments in bank-wide process improvement projects and streamlining exercises to enhance the overall operational efficiency and customer services Analyze and measure the effectiveness of existing business processes and develop sustainable, repeatable and quantifiable business process improvements Cultivate the mindset of internal customers on continuous improvement through various improvement initiatives Keep abreast of the market, suggest and propose new improvement initiatives Provide support in procedural manuals review and monitor related progress Requirements:
By using data already in the cache it reduces load on the database, the network, and the BI Server. Should you be using it? If the foundations are not not in place? Because the BI Server cache will start to store data from all the queries that you run, and use it to satisfy subsequent queries.
But, what happens when you load new data into your data warehouse? You have to have a cache management strategy.
But it boils down to two things: This gives you lots of useful information particularly when you come to optimising cache usage including the size of each entry, when it was created, when it was last used, and so on. Otherwise we commit the cardinal sin of any analytical system and show the user incorrect data which is a Bad Thing.
The cache is persisted to disk and can hold lots of data stretching back months — to blitz all of that just because one table has some new data is overkill. A more targetted approach is to purge by physical database, physical table, or even logical query.
When would you use these? Purge entire cache — the nuclear option, but also the simplest. If your data model is small and a large proportion of the underlying physical tables may have changed data, then go for this Purge by Physical Database — less brutal that clearing the whole cache, if you have various data sources that are loaded at different points in the batch schedule then targetting a particular physical database makes sense.
Imagine you have a table with sales by day. You load new sales figures daily, so purging the cache by query for recent data is obviously necessary, but data from previous weeks and months may well remain untouched so it makes sense to leave queries against those in the cache.
Note that OBIEE itself will perform cache purges in some situations including if a dynamic repository variable used by a Business Model e.
Performing the Purge There are several ways in which we can purge the cache. Doing this every time your ETL runs is not a sensible idea unless you enjoy watching paint dry or need to manually purge it as part of a deployment of a new RPD etc.
In the Physical table, setting Cache persistence time.
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Because this time period starts from when the data was loaded into the cache, not when the data was loaded into your database.
An easy mistake to make would be to think that with a daily ETL run, setting the Cache persistence time to 1 day might be a good idea. What happens if the ETL batch overruns or underruns? So the two viable options for cache purging are: Many of these are internal and mostly undocumented Christian Berg does a great job of explaining them hereand they do creep into the documentation here and herebut there are some cache management ones that are fully supported and documented.
The syntax is in the documentation, but what I am interested in here is how you can invoke them. They are my preferred method for managing the BI Server cache because they enable you to tightly couple your data load ETL to your cache purge.
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