Master Modern Data Platforms: From Warehouses to Lakehouses for Scalable Analytics
What you'll learn
Understand Modern Data Architectures
Master Data Integration Techniques
Explore Key Technologies and Tools
Understand Lakehouse Architecture
Requirements
No Experience is required!
Description
This course provides a comprehensive exploration of modern data platform architectures, focusing on Data Warehouses, Data Lakes, and Data Lakehouses. Learners will begin by understanding the fundamentals of databases, including OLTP and OLAP systems, and their roles in operational and analytical workloads. The curriculum covers the evolution from traditional data warehouses—centralized, structured repositories for business intelligence—to scalable, flexible data lakes that store structured, semi-structured, and unstructured data for advanced analytics and machine learning.Participants will master the differences between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes, and learn how to select the right approach for cloud-native platforms. The course highlights the advantages and challenges of each architecture, such as governance, scalability, cost efficiency, and integration complexity.A major focus is on the emerging Data Lakehouse paradigm, which unifies the best features of lakes and warehouses. Learners will explore key technologies like Delta Lake, Apache Iceberg, and Hudi, and understand how lakehouses enable ACID transactions, schema enforcement, and interoperability with BI and AI tools.By the end of the course, students will be equipped to design, implement, and optimize data platforms that support real-time analytics, advanced data science, and robust governance for enterprise environments.This is an ideal course for:Data EngineersData ManagersData Architects
Data Engineers,Data Managers,Data Architects

https://rapidgator.net/file/860913fa308cd2cf5604800653683b4a/Data_Warehouse_Data_Lake_Data_Lakehouse_Fundamentals.rar.html