In this course, you’ll model, transform, and serve data for both analytics and machine learning use cases. You’ll explore various data modeling techniques for batch analytics, including normalization, star schema, data vault, and one big table, and you’ll use dbt to transform a dataset based on a star schema and one big table. You’ll also compare the Inmon vs Kimball data modeling approaches for data warehouses. You’ll model and transform a tabular dataset for machine learning purposes. You’ll also model and transform unstructured image and textual data. You’ll explore distributed processing frameworks such as Hadoop MapReduce and Spark, and perform stream processing. You’ll identify different ways of serving data for analytics and machine learning, including using views and materialized views, and you’ll describe how a semantic layer built on top of your data model can support the business. In the last week of this course, you’ll complete a capstone project where you’ll build an end-to-end data pipeline that encompasses all of the stages of the data engineering lifecycle to serve data that provides business value.
Data Modeling, Transformation, and Serving
This course is part of DeepLearning.AI Data Engineering Professional Certificate
Instructor: Joe Reis
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What you'll learn
Model and transform data based on stakeholder needs to deliver business value
Choose the appropriate data processing tools for your architecture design
Process data for batch analytics and machine learning data pipelines using distributed and non-distributed processing frameworks
Skills you'll gain
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September 2024
5 assignments
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There are 4 modules in this course
What's included
13 videos6 readings2 assignments2 app items
What's included
10 videos5 readings1 assignment2 app items
What's included
13 videos2 readings1 assignment2 app items
What's included
9 videos4 readings1 assignment3 app items
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