Welcome to the Global Cycling Company Data Warehouse by AdventureWorks 2016! From data collection to insightful reporting, our process involves gathering data from various sources, integrating it into a centralized platform, transforming it for analysis, storing it efficiently, applying advanced analytics techniques like Slowly Changing Dimension (SCD), generating insightful reports and visualizations, and continuously improving the warehouse to meet evolving business needs and technological advancements.
The Global Cycling Company Data Warehouse project involves the following key components:
- Data Consolidation: Gathering data from transactional databases, external feeds, spreadsheets, and online sales platforms to centralize information for enhanced reporting and analytics.
- Optimized Utilization: Structuring and organizing data to ensure accessibility and facilitate insightful analysis.
- Purposeful Insights: Leveraging the data warehouse to gain deeper insights into sales trends, customer behavior, inventory management, and market dynamics.
- Decision Support: Empowering decision-makers with data-driven strategies for better forecasting, inventory optimization, targeted marketing, and predictive analytics.
AdventureWorks is a database provided by Microsoft for free on online platforms. It is a product sample database initially published by Microsoft to demonstrate the supposed design of a SQL server database using SQL Server 2019. Here are some key points to know about AdventureWorks:
- AdventureWorks database supports a manufacturing MNC named Adventure Works Cycles.
- It is a sample Online Transaction Processing (or OLTP) database, a type of data processing where multiple transactions occur concurrently. Microsoft ships these with all of their SQL server products.
This project uses the Lightweight (LT) data: a lightweight and pared-down version of the OLTP sample. Download here
Utilizes a star schema design with a central fact table surrounded by dimension tables optimized for query performance and simplicity.
- SQL Server Management Studio 2019 and MySQL (SSMS): For deployment and scheduling.
- ODBC and OLEDB Drivers: For database connections.
- Visual Studio 2019 (ETL): To automate the extraction, transformation, and loading processes.
The purpose of our data warehouse is to:
- Gain deeper insights into sales trends and customer behavior
- Improve inventory management through better forecasting and optimization
- Enhance decision-making processes with data-driven strategies
- Facilitate targeted marketing campaigns based on customer segmentation
- Enable predictive analytics to anticipate market changes and customer preferences
By implementing the Global Cycling Company Data Warehouse, we expect to:
- Make informed, strategic decisions to drive business growth
- Improve operational efficiency through data-driven insights
- Enhance customer satisfaction by understanding their preferences and needs better
While implementing the data warehouse, we may encounter challenges such as:
- Data quality issues leading to inaccurate analysis
- Complex ETL processes requiring careful management
- Integration of data from diverse sources may pose compatibility challenges
- Understand Business Goals: Research our company to grasp our business model, focusing on sales trends, customer demographics, and inventory optimization.
- Identify Data Sources: Enumerate potential sources like transactional databases, external feeds, and spreadsheets.
- Define Data Granularity and Architecture: Determine the detail level for data (e.g., daily sales data) and outline the data warehouse architecture, including ETL workflow and schema design.
- Provide guidelines covering ODBC driver installation, source data extraction, and SQL script for DW creation, including key definitions and data mapping specifications.
- Extract: Identify and prepare data from MySQL and SQL Server 2019.
- Transform: Clean data, ensuring consistency and correct format, especially for keys.
- Load: Populate the Global Cycling Company Data Warehouse (DW), focusing on incremental data loading and critical transformations.
Incremental Load and Full Load.
Look up to DWH to avoid data duplication.
Look up to DWH to avoid data duplication.
Split into three conditions: SCD 0, SCD 1, and SCD 2 based on the incoming data criteria of Dim.Product.
If the criteria meet SCD 1, it will update the existing data.
If the criteria meet SCD 2, it will insert new data and keep history.
Full load and Truncate each time.
Look up for SK key matched with all other SK keys of dim.Table.
Look up for SK key matched with all other SK keys of dim.Table.
- Unit Testing: Test ETL processes, data validation, and reporting.
- Integration Testing: Validate the entire data pipeline for correctness and logic.
- Deployment: Deploy the data warehouse in a production environment, monitoring performance and functionality.
Deploy packages to SQL server (DWH).
Testing deployment of Dimension tables in SQL server (DWH).
Testing deployment of Fact tables in SQL server (DWH).
Create a step task to load all Dimension tables data.
Create a step task to load all Fact tables data.
Create a schedule to execute all two steps to load all data from Dimension and Fact tables based on your preferred time schedule.
With the data warehouse in place, we can expect to:
- Gain deeper insights into sales trends and customer behavior.
- Improve inventory management through better forecasting and optimization.
- Enhance decision-making processes with data-driven strategies.
- Facilitate targeted marketing campaigns based on customer segmentation.
- Enable predictive analytics to anticipate market changes and customer preferences.
For further inquiries or to discuss potential collaborations, please connect with us: