Master BigQuery for data warehouse development. Learn ETL pipelines, advanced querying, and optimization techniques.
Master BigQuery for data warehouse development. Learn ETL pipelines, advanced querying, and optimization techniques.
This comprehensive course teaches data professionals how to harness Google BigQuery's powerful capabilities for data warehouse development. Students learn to create efficient data structures, implement ETL pipelines using Python, and perform advanced queries. The curriculum covers essential topics from platform setup to advanced features like partitioning, clustering, and custom transformations, preparing learners for real-world data engineering challenges.
Instructors:
English
What you'll learn
Create efficient data warehouses using BigQuery UI and SQL
Build dynamic ETL pipelines with Python
Implement partitioning and clustering strategies
Perform advanced queries with aggregate functions
Master data security and governance practices
Optimize warehouse performance and cost efficiency
Skills you'll gain
This course includes:
111 Minutes PreRecorded video
6 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There is 1 module in this course
This comprehensive course focuses on building and managing data warehouses using Google BigQuery. The curriculum covers essential concepts including platform setup, data organization, and advanced querying techniques. Students learn both UI-based and programmatic approaches using Python, including ETL pipeline development, data partitioning, and clustering strategies. The course emphasizes practical applications and best practices for data security and governance.
Build a Data Warehouse Using BigQuery
Module 1 · 8 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Data Science Expert and Business Intelligence Innovator
Caio Avelino brings extensive expertise in Data Science, Business Intelligence, and Artificial Intelligence, combining academic excellence with practical industry experience. His educational background includes a degree in Electrical Engineering from Unicamp, international experience at Monash University in Melbourne, and specialized training in Data Science from Data Science Academy. As an instructor and professional, he specializes in complex analytics, dashboard development, and Machine Learning model creation. His teaching portfolio includes courses in Big Data Technologies, Data Warehousing, Business Intelligence, and Advanced Statistical Analysis using Seaborn. His approach emphasizes practical problem-solving and continuous learning, particularly in startup environments where he has contributed to developing innovative data solutions. His passion for knowledge sharing is evident through his training programs, helping colleagues and students master data science concepts. Currently pursuing further studies in Artificial Intelligence, he maintains a commitment to staying at the forefront of technological advancement while making complex technical concepts accessible to learners.
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