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This program provide participants with a comprehensive understanding of data warehousing concepts and practices in the context of data engineering.
Difficulty
Hours per week
Live Sessions
Next Date
Objective
The objetive of this program is to provide participants with a comprehensive understanding of data warehousing concepts and practices in the context of data engineering. The course aims to equip participants with the knowledge and skills to design, develop, and optimize data warehousing solutions to support decision-making processes. By the end of the course, participants should be able to design and implement data warehousing architectures, perform ETL (Extract, Transform, Load) processes, and create efficient data models for analytics and reporting.
• Familiarity with databases, SQL, and data management concepts.
• Understanding of data modeling principles.
• Proficiency in a programming language commonly used in data engineering, such as Python
• Access to a computer with a database management system (DBMS) installed (e.g., MySQL, PostgreSQL, Oracle).
Discover data warehousing from both business and technical perspectives. Explore data warehousing architecture, components, and key concepts such as OLAP, data marts, and data integration.
Revisit data modeling principles and learn how dimensional modeling plays a crucial role in data warehousing. Dive into star schema and snowflake schema design principles, fact and dimension tables, hierarchies, and attributes.
Understand the essential Extract, Transform, Load (ETL) processes in data warehousing. Learn how to extract data from various sources, including databases, files, and APIs.
Master data integration techniques for consolidating data from multiple sources. Perform data quality assessment through data profiling, identifying and addressing data inconsistencies and anomalies.
Explore different data warehouse architectures, including Kimball, Inmon, and Data Marts. Consider performance considerations like scalability and availability to build robust data warehouses.
Get familiar with Databricks SQL, a serverless data warehouse on the Databricks Lakehouse Platform. Discover visualization and reporting tools for data exploration and analysis within Databricks.
Learn about data governance, metadata management, and ensuring data security and privacy in data warehousing. Stay compliant with relevant regulations and follow best practices for data governance.
Improve your data warehousing design and maintenance with best practices. Explore real-world case studies showcasing successful data warehousing implementations in data engineering. Keep up-to-date with emerging trends and advancements in data warehousing.
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