The assignment contains Use the OLTP logical schema below to build data warehouse consisted of two data marts. You will need to import the final exam data code for the OLTP logical schema and develop ETL process in SSIS . Your tables should meet these requirements:
To build a data warehouse using the provided OLTP logical schema, we need to understand its structure and the requirements for the data marts. The OLTP logical schema represents the database design for online transaction processing systems, which are optimized for transactional data storage and retrieval.
The first step is to import the final exam data code for the OLTP logical schema. This code will define the tables, relationships, and attributes in the database. Once the data code is imported, we can proceed with developing the Extract, Transform, and Load (ETL) process using SSIS (SQL Server Integration Services).
The goal of the ETL process is to extract data from the OLTP schema, transform it into a format suitable for analysis, and load it into the data warehouse. In this case, we are building two data marts, which are subsets of the data warehouse focused on specific subject areas.
When developing the ETL process, it is important to consider the requirements for the tables in the data warehouse. These requirements dictate the structure and content of the tables, ensuring that they meet the needs of the data marts. Let’s examine some common requirements for data warehouse tables:
1. Historical data: Data warehouses typically store historical data, allowing for trend analysis and comparison over time. As such, the ETL process should include mechanisms for capturing and loading historical data from the OLTP schema.
2. Aggregated data: Data marts often require aggregated data for efficient analysis. Aggregation involves summarizing data at different granularity levels (e.g., daily, monthly, yearly). The ETL process should include transformations to aggregate the data and populate the aggregated tables in the data warehouse.
3. Cleansed and standardized data: The OLTP schema may contain incomplete, inconsistent, or redundant data. The ETL process should include data cleansing and standardization steps to ensure the quality and integrity of the data in the data warehouse.
4. Denormalization: In the OLTP schema, data is typically normalized to reduce redundancy and improve transaction performance. However, for analytical purposes, denormalization may be necessary to simplify queries and improve query performance. The ETL process should include denormalization techniques to create denormalized tables in the data warehouse.
5. Security and access control: Data warehouses often contain sensitive and confidential information. The ETL process should consider security measures to ensure that only authorized users can access and analyze the data.
6. Data transformation and integration: The ETL process should employ various transformation techniques to integrate data from different sources, standardize formats, and resolve conflicts or inconsistencies. This ensures that the data in the data warehouse is consistent and ready for analysis.
Once the ETL process is developed, it can be deployed and executed using SSIS. SSIS provides a graphical interface for designing and managing the ETL workflows, making it easier to monitor and maintain the data warehouse.
In conclusion, to build a data warehouse with two data marts using the provided OLTP logical schema, we need to import the data code, develop an ETL process in SSIS, and ensure that the tables in the data warehouse meet the requirements for the data marts. The ETL process should address considerations such as historical data, aggregation, data cleansing and standardization, denormalization, security, and data transformation and integration.