barcode printing in c#.net A simple SSIS package that empties the warehouse and reloads it from scratch in Microsoft Office

Making Quick Response Code in Microsoft Office A simple SSIS package that empties the warehouse and reloads it from scratch

A simple SSIS package that empties the warehouse and reloads it from scratch
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Unfortunately, no company has completely clean data The old saying is that as soon as you make something idiot proof, the universe creates a bigger idiot While this is likely true, it s also a fact that many applications allow bad data into the system in the first place Having bad data then makes analysis much more difficult Here is a real world example: A company had a large presence in the city of Ann Arbor, Michigan One of the systems asked customers for their address and the input box for the city was a free-form text field People living in the city of Ann Arbor found (at last count) fourteen different ways to spell Ann Arbor These included Anne Arbor, AnnArbor, AnneArbor, Ann Arbour, and so forth The easy solution going forward would be to fix the application so that the city was chosen from a drop-down box Unfortunately, the owners of that application were busy and didn t see it as a priority Even if it had been fixed, however, this did nothing to handle the existing data that was incorrect At this point, the business could have gone in and cleaned up all the source data Instead, they chose to do the cleanup in their ETL process The source system could continue to hold misspellings of Ann Arbor, but the data in the warehouse would be consistent This was important because they wanted to analyze sales by city and when someone chose Ann Arbor, they wanted all sales for that area to come up without having to know to add in many misspellings of the city name The ETL process was written to handle all known misspellings of Ann Arbor Periodically, a new one would appear and the ETL would have to be updated Note that today, this would have been much easier thanks to the Fuzzy Lookup transformation in SSIS, but at the time the customer had to do everything manually Given that there are such data quality issues inside any organization, it s important for the business to come up with the rules for handling such issues It s appealing to be able to say that everything will be fixed in the warehouse, but sometimes the effort to do so outweighs the benefits Imagine a system in which bad records are flagged as unknown If the estimate to truly fix the data is 2000 man hours, and less than one-tenth of one percent of the data is bad, is it worth the cost to fix On the other hand, if 40 percent of the data is bad, fixing the data probably has a much higher priority Articles, whitepapers, and books can be written about data quality and the issues around it The fact is that most organizations have bad data and still allow bad data to flow into the system There are ways to ameliorate this but eliminating it entirely is often more costly than just living with some level of dirty data It s important for business users to understand why bad data is in the system and what options exist to fix it, and why it might be necessary to see some percentage of data listed as unknown
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3: Data Warehousing and Business Intelligence
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When data warehousing was originally born, the star schema was the data warehouse In fact, it s still often called the relational data warehouse There s nothing wrong with this at all, and many companies query directly against the relational warehouse The star schema is designed for the speed of retrieval, so running reports against it is certainly acceptable One thing that star schemas may not have, however, are summary levels Imagine that a particular schema has a daily grain, and that there are two billion fact records in the fact table, representing 4 years of sales If a customer wants to see yearly sales totals, this requires performing a Sum across all two billion records, summing approximately 500 million records at a time in order to get the values for each year This can be an expensive operation on large warehouses, especially when there are many users of the system One solution was to preaggregate the data in the relational system Remember that in a star schema the concern with normalization is gone, so adding calculated data is acceptable Adding preaggregated data could be accomplished by setting some dummy values For example, if the Year was 2007 but the Quarter was 5, the Month was Z and the Day was 99, that record represented the year total for 2007 If the Year was 2007, the Quarter was 1, the Month was Z and the Day was 99, that record represented the first quarter totals for 2007 This magic number approach could get messy quickly, so many people opted to build summary tables There was a year table, a quarter table, a month table, and then the real fact table at the daily grain This had the advantage of not having messy codes, but queries had to be much smarter and know when to get different values from different tables In addition, if there were monthly totals for each individual product and customer, even the summary tables could be quite large and require a significant amount of extra disk space This is where cube building engines come in Cubes are simply a different way of storing data Different engines work differently, but the focus here is on SQL Server Analysis Services, also called just Analysis Services, SSAS, or AS Analysis Services does several things when creating a cube First, it reads the data from the dimension tables and the fact table It stores the dimension and fact records in a binary format, completely independent of the relational database engine As it stores records, it compresses them so that less disk storage is required How much it compresses them depends on many factors, but a general rule of thumb is that a cube will normally take up about 30 40 percent of the size of the relational warehouse Therefore, a 1TB relational data warehouse would translate to a cube of approximately 300 400GB
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