data-warehouse

Based on my earlier questions , how can I pivot data using Informatica PowerCenter Designer when I have variable amount of Addresses in my data. I would like to Pivot e.g four addresses from my data. This is the structure of the source data file: +---------+--------------+-----------------+ | ADDR_ID | NAME | ADDRESS | +---------+--------------+-----------------+ | 1 | John Smith | JohnsAddress1 | | 1 | John Smith | JohnsAddress2 | | 1 | John Smith | JohnsAddress3 | | 2 | Adrian Smith | AdriansAddress1 | | 2 | Adrian Smith | AdriansAddress2 | | 3 | Ivar Smith | IvarAddress1 | +---------+------

I have read a few articles about moving data out of SAP BW and into SQL Server. I cant find any articles on moving the data from SQL Server to SAP BW, is it even possible and if so what would be the best way to handle this? After searching on this topic, i found many link addressing this issue, in this answer i will try to summarize them all and to provide all links that can help you achieving your goal. There are many way to import data from SQL Server into SAP BW: (1) SAP BW DB Connect With DB Connect, you can load data from a database system that is supported by SAP, by linking a database

I want to handle a date dimension in a MySQL datawarehouse. (I m a newbie in the DW world) I made some searches with google and saw a lot of table structures (most of) date dimension where the Primary Key is a simple UNSIGNED INTEGER . Why don't use a DATE field as primary key since with MySQL it is 3 Bytes VS 4 Bytes for INTEGER ? Ex: CREATE TABLE dimDate id INTEGER UNSIGNED NOT NULL PRIMARY AUTOI_NCREMENT, date DATE NOT NULL, dayOfWeek ... VS CREATE TABLE dimDate date DATE NOT NULL PRIMARY, dayOfWeek ... If you have a table with a column that is of date type and where no two rows will ever

I did a bit R&D on the fact tables, whether they are normalized or de-normalized. I came across some findings which make me confused. According to Kimball : Dimensional models combine normalized and denormalized table structures. The dimension tables of descriptive information are highly denormalized with detailed and hierarchical roll-up attributes in the same table. Meanwhile, the fact tables with performance metrics are typically normalized. While we advise against a fully normalized with snowflaked dimension attributes in separate tables (creating blizzard-like conditions for the business

I have a very complex Oracle view based on other materialized views, regular views as well as some tables (I can't "fast refresh" it). Most of the time, existing records in this view are based on a date and are "stable", with new record sets having new dates. Occasionally, I receive back-dates. I know what those are and how to deal with them if I were maintaining a table, but I would like to keep this a "view". A complete refresh would take around 30 minutes, but it only takes 25 seconds for any given date. Can I specify that only a portion of a materialized view should be updated (i.e. the

with MongoDB, CouchDB and related technologies we can get faster querying so is this still valid? “A copy of transaction data, specially restructured for queries and analyses.” (R. Kimball The Data Warehouse Toolkit, 1996 I mean, do we really need to restructure our data to an OLAP scheme to query it for analysis purposes? More specifically can drill-down, slice and dice and other reporting for analysis purposes be achieved with NoSQL (NOT necessarily with OLAP modelling)? Also could we overcome the "data subset" querying limitation of OLAP and report on the whole data universe with NoSQL? In

Are staging tables used only in Data warehouse project or in any SSIS Project? I would like to know what is a staging table? Can anyone give me some examples on how to use it and in what circumstances it is implemented? Also, may I please know the best practices while using it? staging tables are just database tables containing your business data in some form or other. Staging is the process of preparing your business data, usually taken from some business application. For your average BI system you have to prepare the data before loading it. A staging table is essentially just a temporary

I know what is Data Warehouse & what is Big Data. But I am confused with Data Warehouse Vs Big Data. Both are same with different names or both are different(Conceptually & Physically). I know that this is an older thread but there have been some developments in the last year or so. Comparing the data warehouse to Hadoop is like comparing apples to oranges. The data warehouse is a concept: clean, integrated data of high quality. I don't think the need for a data warehouse will go away anytime soon. Hadoop on the other hand is a technology. It is a distributed compute framework to process large