A comprehensive data warehouse built using Microsoft SQL Server and SSIS for the AdventureWorks OLTP database.
A data warehouse is a system used for reporting and data analysis and is considered a core component of Business Intelligence.
BI Relationship with Data Warehouses
Data Warehouses play a crucial role in enabling effective Business Intelligence. They serve as the central repository for structured, historical data collected from various operational systems within an organization.
- Data Consolidation & Organization: Data warehouses consolidate data from disparate sources, cleaning and transforming it into a consistent format. This allows BI tools to easily access and analyze the information.
- Historical Context: Data warehouses store historical data, enabling BI users to track trends, compare performance over time, and gain insights into long-term patterns.
- Performance Optimization: Data warehouses are designed for analytical queries, providing optimized performance for complex BI analysis.
- Data Accessibility: Data warehouses provide a single, unified source of truth, ensuring everyone within the organization is working with the same consistent data.
- Centralized Data: Eliminates silos, provides a single source of truth
- Historical Context: Enables trend analysis & forecasting
- BI Optimized: Structured for complex queries & insights
- Performance & Scalability: Handles large analytical workloads
- Advanced Analytics: Supports data mining & predictive modeling
- Cost-Effective: Streamlines data management
- Data Governance: Enhances security & compliance
The following diagram illustrates the overall architecture of the Business Intelligence solution, showcasing the flow of data from various sources to the final presentation layer:
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- ETL (Extract, Transform, Load): Traditional approach where data is extracted from sources, transformed in a staging area, and then loaded into the data warehouse
- ELT (Extract, Load, Transform): Modern approach leveraging the power of the data warehouse to perform transformations after loading the raw data
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- Staging Area: Temporary storage for data during the ETL/ELT process, used for cleansing, validation, and transformation
- ODS (Operational Data Store): Integrated data repository serving near-real-time reporting and operational needs, often a source for the data warehouse
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- OLTP (Online Transaction Processing): Systems designed for day-to-day business transactions, focused on data integrity and speed
- OLAP (Online Analytical Processing): Systems optimized for complex queries and analysis, often utilizing multidimensional data structures
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- Fact Tables: Store the measurements or metrics of the business process, typically containing numeric values and foreign keys to dimension tables
- Dimension Tables: Provide context to the facts, describing the "who, what, where, when, and how" of the business events
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- Star Schema: Simple dimensional model with a central fact table connected to multiple dimension tables, ideal for performance and ease of use
- Snowflake Schema: Extension of the star schema where some dimension tables are further normalized, offering flexibility but potentially impacting query performance
- Multinational manufacturing company
- Territory
- 3 Group 6 Region 10 Territory
- Product
- 4 Category 30+ Sub Category 500+ Products
- Customers
- 15k+
- Department
- Purchase and Production
- Human Resource
- Sales
Designing a data warehouse is a multi-faceted process that involves careful planning and execution. Here's a breakdown of the key phases:
This phase focuses on understanding the business needs and requirements that will drive the data warehouse design.
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Business Objectives:
- Key questions to answer: Clearly define the business questions the data warehouse needs to help answer. This will guide the data modeling and ensure the warehouse delivers actionable insights.
- Processes/areas to benefit: Identify the specific business processes or areas that will leverage the data warehouse for decision-making and analysis.
- Reporting needs: Determine the types of reports and dashboards that users will need, including the required metrics, dimensions, and levels of detail.
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Data Requirements:
- Data types needed: Identify the specific data elements (e.g., sales data, customer information, product details) necessary to support the business objectives.
- Granularity level: Determine the level of detail at which data should be stored (e.g., daily, weekly, transaction-level). This impacts storage requirements and query performance.
- Metrics & dimensions: Define the key performance indicators (KPIs) and the dimensions (e.g., time, product, customer) that will be used for analysis.
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Data Governance:
- Ownership & responsibilities: Establish clear ownership and responsibilities for managing and maintaining the data warehouse, ensuring data quality and consistency.
- Quality standards: Define data quality rules and processes to ensure the accuracy, completeness, and timeliness of the data in the warehouse.
- Privacy & security: Implement measures to protect sensitive data and comply with relevant privacy and security regulations.
This phase involves evaluating the technical aspects of the data warehouse implementation.
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Technology Stack:
- Data warehouse platform: Choose the appropriate data warehouse platform (e.g., Microsoft SQL Server, Azure Synapse Analytics, Snowflake) based on requirements and budget.
- ETL/ELT tools: Select the tools for extracting, transforming, and loading data into the warehouse. Consider factors like performance, ease of use, and integration capabilities.
- BI & reporting tools: Choose the tools for visualizing and analyzing the data in the warehouse (e.g., Power BI, Tableau, QlikView).
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Hardware & Infrastructure:
- Storage & processing power: Estimate the storage and processing requirements based on data volume, growth projections, and query complexity.
- Scalability: Design the infrastructure to accommodate future growth and increasing data volumes.
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Data Modeling:
- Modeling approach: Decide on the data modeling approach (e.g., dimensional modeling, data vault) that best suits the business requirements and analytical needs.
- Dimensional design: Design the dimension and fact tables, ensuring proper normalization and relationships.
- Data types & indexes: Choose appropriate data types and create indexes to optimize query performance.
This phase involves understanding the source systems from which data will be extracted.
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Identify Sources:
- Inventory of relevant systems: Compile a list of all the source systems that contain data relevant to the data warehouse.
- Data structures & formats: Understand the data structures, formats, and relationships within each source system.
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Data Extraction & Integration:
- Extraction feasibility: Assess the feasibility of extracting data from each source system, considering technical constraints and data access limitations.
- Extraction methods: Determine the most appropriate extraction methods (e.g., database replication, API calls, file transfers) for each source system.
- Data cleansing & transformation: Plan for data cleansing and transformation activities to ensure data quality and consistency before loading into the warehouse.
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Data Quality:
- Quality analysis: Perform data profiling and analysis to identify data quality issues in the source systems.
- Potential issues: Document potential data quality problems, such as missing values, inconsistencies, and duplicates.
- Addressing strategies: Develop strategies to address data quality issues, including data cleansing, validation, and enrichment techniques.
- Creating Database Objects: Design and create the necessary database objects in SQL Server, including tables, views, stored procedures, and functions.
- ETL Source to Staging: Extract data from various source systems and load it into the staging area using SSIS packages. Perform initial data cleansing and validation in the staging area.
- ETL Staging to DWH: Transform and load data from the staging area into the data warehouse, ensuring data integrity and applying business rules.
- Creating SSAS Tabular: Develop a Tabular model in SSAS to provide a semantic layer for business users, enabling them to create reports and perform analysis easily.
- Creating Power BI Reports: Design and develop interactive reports and dashboards in Power BI, connecting to the SSAS Tabular model or the data warehouse.
