Implementing a SQL Data Warehouse


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Length: 5.0 day (40 hours)

 

This course delves into the practical aspects of designing, implementing, and maintaining a SQL data warehouse. You'll gain hands-on experience in data modeling, ETL processes, performance tuning, and data security

  • Data analysts
  • Data engineers
  • Database administrators
  • Business intelligence professionals
  • Anyone interested in building and managing data warehouses
  • Basic SQL knowledge
  • Understanding of database concepts
  • Familiarity with data warehousing concepts (preferred)

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Course objectivesCourse outlines

Course objectives

After completing this course, students will be able to:

    • Understand data warehousing architecture and concepts
    • Design efficient data warehouse schemas
    • Implement ETL processes to extract, transform, and load data
    • Optimize data warehouse performance
    • Secure data warehouse environments
    • Monitor and maintain data warehouse systems

Course outlines

  • Module 1: Introduction to Data Warehousing
    • What is a data warehouse?
    • Key components of a data warehouse
    • Data warehouse architecture (star schema, snowflake schema)
    • Data warehouse vs. data mart
    • Data warehouse benefits and challenges
  • Module 2: Data Modeling
    • Dimensional modeling techniques
    • Fact and dimension tables
    • Snowflakes and starschemas
    • ER diagrams for data warehouses
    • Data modeling tools and techniques
  • Module 3: ETL Processes
    • ETL process overview
    • Extraction techniques (SQL queries, APIs, file systems)
    • Transformation techniques (data cleaning, data integration, data validation)
    • Loading techniques (bulk load, incremental load)
    • ETL tools (SSIS, Informatica, Talend)
  • Module 4: Data Warehouse Implementation
    • Database design and implementation
    • Indexing and partitioning strategies
    • Performance tuning techniques
    • Data quality and integrity
    • Data security and privacy
  • Module 5: Data Warehouse Administration and Maintenance
    • Backup and recovery strategies
    • Monitoring and performance tuning
    • Capacity planning
    • Disaster recovery planning
    • Data warehouse administration tools
  • Module 6: Data Warehouse and Business Intelligence
    • Integration with BI tools (Power BI, Tableau, QlikView)
    • Data visualization techniques
    • Creating dashboards and reports
    • Data-driven decision making

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