In this technical presentation, we will delve into the fundamental concepts of Data Engineering, focusing on two pivotal components of modern data architecture - Data Lakes and Data Warehouses. We will explore their roles, differences, and how they collectively empower organizations to harness the true potential of their data.
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Presentation
YouTube Video
1. Introduction to Data Engineering:
- Brief overview of the data engineering landscape and its critical role in modern data-driven organizations.
2. Understanding Data Lakes:
- Explanation of what a data lake is and its purpose in storing vast amounts of raw and unstructured data.
3. Exploring Data Warehouses:
- Definition of data warehouses and their role in storing structured, processed, and business-ready data.
4. Comparing Data Lakes and Data Warehouses:
- Comparative analysis of data lakes and data warehouses, highlighting their strengths and weaknesses.
- Discussing when to use each based on specific use cases and business needs.
5. Integration and Data Pipelines:
- Insight into the seamless integration of data lakes and data warehouses within a data engineering pipeline.
- Code walkthrough showcasing data movement and transformation between these two crucial components.
6. Real-world Use Cases:
- Presentation of real-world use cases where effective use of data lakes and data warehouses led to actionable insights and business success.
- Hands-on demonstration using Python, Jupyter Notebook and SQL to solidify the concepts discussed, providing attendees with practical insights and skills.
Conclusion:
This session aims to equip attendees with a strong foundation in data engineering, focusing on the pivotal role of data lakes and data warehouses. By the end of this presentation, participants will grasp how to effectively utilize these tools, enabling them to design efficient data solutions and drive informed business decisions.
This presentation will be accompanied by live code demonstrations and interactive discussions, ensuring attendees gain practical knowledge and valuable insights into the dynamic world of data engineering.
Some of the technologies that we will be covering:
- Data Lakes
- Data Warehouse
- Data Analysis and Visualization
- Python
- Jupyter Notebook
- SQL
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