Data Warehousing Essentials for Analytics and AI Support

External: Coursera Courses ↗ · Coursera

Open Course on External: Coursera

Free to audit · Opens on External: Coursera

Data Warehousing Essentials for Analytics and AI Support

Coursera · Beginner ·🔄 Data Engineering ·3mo ago

Key Takeaways

Designs data warehouses for analytics and AI support using dimensional modeling

Original Description

This course will cover various topics in Data Engineering in support of decision support systems, data analytics, data mining, machine learning, and artificial intelligence. You will study on-premises data warehouse architecture, and dimensional modeling of data warehouses.
Watch on External: Coursera ↗ (saves to browser)
Sign in to unlock AI tutor explanation · ⚡30

Related Reads

📰
I Built My Second ETL Pipeline. This Time, I Started Thinking Like a Data Engineer
Learn how to build a production-ready ETL pipeline with Python, Docker, PostgreSQL, and Kestra by thinking like a data engineer
Towards Data Science
 📰
JuiceFS Sync for PB-Scale Data Transfers: Resumable Sync, Encryption, and Bandwidth Control
Learn how to efficiently transfer large volumes of data using JuiceFS Sync, which offers resumable sync, encryption, and bandwidth control, ideal for PB-scale data transfers.
Dev.to AI
 📰
How Airflow is using AI to make data engineering more resilient, not more complex
Airflow uses AI to make data engineering more resilient by detecting data drift, resuming failed pipelines, and fixing issues automatically, reducing complexity and improving reliability.
Medium · AI
 📰
What Can We Do When Memory Becomes the New Bottleneck in Data Engineering?
Learn how to overcome memory bottlenecks in data engineering using Pandas chunking, Dask, and Polars, and why it matters for processing large datasets
Towards Data Science
Up next
A Moment Frozen in Time | Arnav Iyengar | TEDxJenks Youth
TEDx Talks
Watch →