Data Warehousing (CS). Handouts (pdf) / Powerpoint Slides (PPTs). Handouts / Power Point Slides. Lessons () (pdf format) · Power Point Slides ( ). Lecture Handout. Data Warehousing. Lecture No. Why a DWH? • Data recording and storage is growing. • History is excellent predictor of the future. 20 hours ago Download Data Warehousing - CS Handouts. Data Warehousing - CS billpercompzulbe.ga VUTube. Administrator. Quote post.
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For example, transactions in the detail table need to be updated with the new key, and for a retail warehouse, the detail table could be 30 times larger than the master table, which again is larger then the fact member table.
The conquer part of the technique is about combining the results. Assuming uniform hashing, hash splitting supports even data distribution across all partitions in a pre-defined manner.
However, hash based splitting is not easily reversible to eliminate the split.
Hash partitioning is the most common partitioning strategy. Almost all parallel RDBMS products provide some form of built-in hash partitioning capability mainframe DB2 is the most significant exception to this statement.
In other words, a particular row will always hash to the same partition assuming that the hashing algorithm and number of partitions have not changed , but a Data Warehousing Course Code: CS Cs vu. Since data rows are hash distributed across all partitions for load-balancing purposes , there is not practical way to perform partition elimination unless a very small number e.
If this can not be done, then extra effort or CPU cycles would be required to achieve this objective. As shown in Figure This is further explained when we discuss the issues of horizontal partitioning.
Not pre-defined. Almost impossible to reverse or undo. Range and expression splitting: Can facilitate partition elimination with a smart optimizer. Round-robin spreads data evenly across the partitions, but does not facilitate partition elimination for the same reasons that hashing does not facilitate partition elimination.
Round-robin is typically used only for temporary tables where partition elimination is not important and co-location of the table with other tables is not expected to yield performance benefits hashing allows for co-location, but round-robin does not. The most common use of range partitioning is on date.
This is especially true in data warehouse deployments where large amounts of historical data are often retained. Hot spots typically surface when using date range partitioning because the most recent data tends to be accessed most frequently.
Expression partitioning is usually deployed when expressions can be used to group data together in such a way that access can be targeted to a small set of partitions for a significant portion of the DW workload.
Expression partitioning can lead to hot spots in the same way as described for range partitioning. Consider the case of airline reservations table horizontally split on the basis of year.
Thus the most number of cancellations, actually, probably highest ever occurred during the last quarter of year Thus the corresponding partition would have the largest number of records.
Thus in a parallel processing environment, where partitioning is consciously done to improve performance it not going to work, because as shown in Figure The assignment will not be accepted after due date.
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The assignment file must be an MS Word. Zero marks will be awarded to the assignment if copied from other student or copied from handouts or internet. Zero marks will be awarded to the assignment if the Student ID is not mentioned in the assignment file.
This assignment covers lectures 13 and For any query about the assignment, contact only at CS vu. Course Synopsis. The focal area of this course is to provide awareness of data warehouse basic components, importance of data warehouse in business, important steps and techniques to be considered during data warehouse development, and future trends and usage of data warehouse. Course Learning Outcomes.
After completing this course you should be able to: Design and implement a Data Warehouse Define the basic concepts and importance of data warehouse Identify the business areas where data warehouse is required Use data warehouse for data mining projects.