Implement and analyse a data warehouse

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Reference no: EM132756101

COMP810 Data Warehousing and Big Data - Auckland University of Technology

Assessment - Data Warehousing Project: Building and Analysing a DW for Electronics For You in NZ

Assessment task - To design, implement and analyse a Data Warehouse (DW) for Electronics For You, one of the biggest electronics store chains in NZ.

Project overview - ElectronicsForYou is one of the biggest electronics store chains in NZ. The stores are located all over the country. ElectronicsForYou has thousands of customers and therefore it is important for the organisation to analyse the shopping behaviour of their customers. Based on such analysis the organisation can optimise their selling techniques e.g. by having relevant promotions on different products.

There is a need to build a DW to make the analysis of shopping behaviour practical; customers' transactions from Data Sources (DSs) are required to be reflected in the DW on a daily basis. This process of reflecting the customer data into DW is called Data Integration (DI) as shown in Figure 1. To implement DI we usually need ETL (Extraction, Transformation, and Loading) tools. Since the data generated by customers is not in the format required by DW, it needs to be processed in the transformation layer of ETL. This processing will involve the enriching of transactions data with information from Master Data (MD) as shown in Figure 2.

To implement this enrichment feature in the transformation phase of ETL we need a join operator typically called Semi -Stream Join (SSJ). There are a number of algorithms available to implement this join operation however, the simplest one is Index Nested Loop Join (INLJ) which is explained in the next section and you are required to implement it in this project.

Tasks break-up - Following is a list of tasks that you need to complete in this project.

1. Identification of appropriate dimension tables, fact table, and their attributes for the sales scenario presented in Figure 2. Based on this, the creation of star schema tables for DW with appropriate primary and foreign keys. Consult the structure of tables TRANSACTIONS and MASTERDATA provided in Figure 4 in order to keep the attribute names and their data types consistent with DW.

2. Implementation of the INLJ algorithm and loading of transactional data into DW after joining it with MD.

3. Applying different analyses (described in Section 7) on DW using slicing, dicing, drill down, and materialising view concepts.

4. A Project report that includes an overiew of the project, INLJ algorithm, ER Diagram of schema for DW and an explanation of the same, OLAP queries with outputs and a summary (1 -2 pages) of what you have learned from the project.

Attachment:- Data Warehousing and Big Data Assignment Files.zip

Reference no: EM132756101

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