Data Modeling, Transformation, and Serving
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.
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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.
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 warehouse architectures. You’ll learn about basic machine learning concepts, then model and transform a tabular dataset for machine learning purposes. You’ll also be modeling and transforming unstructured images and textual data. You’ll explore distributed processing frameworks such as Hadoop MapReduce and Spark, and perform stream processing.
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Activities
Review data modeling techniques from previous coursework or resources
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Solidify your understanding of data modeling principles before beginning the course.
Browse courses on
Data Modeling
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Gather notes, assignments, and materials from previous data modeling courses.
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Review key concepts such as normalization, star schema, and data vault.
Complete practice problems on data modeling and normalization
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Reinforce data modeling concepts by applying them to practical exercises.
Browse courses on
Data Modeling
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-
Find practice problems on data modeling and normalization.
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Solve the problems using appropriate techniques.
Create a glossary of key data modeling and transformation terms
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Enhance your understanding by defining and organizing important terms related to the course.
Browse courses on
Data Modeling
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Identify key terms from the course materials.
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Define each term concisely and accurately.
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Organize the terms alphabetically or by category.
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Review DBMS Fundamentals by Ramez Elmasri
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Review core database concepts to strengthen your understanding of data modeling and transformation.
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Read Chapters 1-3: Database Concepts, Data Modeling, and Relational Model.
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Complete the practice exercises at the end of each chapter.
Participate in peer-led review sessions
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Engage in discussions and exchange ideas with peers to enhance understanding.
Browse courses on
Data Modeling
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Form a peer group with classmates.
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Meet regularly to discuss course concepts, assignments, and projects.
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Provide constructive feedback and support to each other.
Follow tutorials on Apache Spark for Distributed Processing
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Gain practical knowledge of Spark's capabilities for data processing at scale.
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Apache Spark
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Find tutorials on Spark's RDD, DataFrame, and SQL interfaces.
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Follow along with the tutorials and complete the exercises.
Create a Data Transformation Plan for a Real-World Dataset
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Develop hands-on experience in transforming a real-world dataset to meet specific requirements.
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Data Transformation
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Identify a raw dataset of interest.
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Define the desired transformed dataset structure.
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Perform data cleaning and transformation using appropriate tools or libraries.
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Document the data transformation steps and rationale.
Contribute to open-source data transformation projects
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Gain hands-on experience and contribute to a real-world project related to data transformation.
Browse courses on
Open Source
Show steps
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Identify open-source data transformation projects.
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Find issues or areas where you can contribute.
-
Follow project guidelines and submit your contributions.
Review data modeling techniques from previous coursework or resources
Show steps
Solidify your understanding of data modeling principles before beginning the course.
Browse courses on
Data Modeling
Show steps
- Gather notes, assignments, and materials from previous data modeling courses.
- Review key concepts such as normalization, star schema, and data vault.
Complete practice problems on data modeling and normalization
Show steps
Reinforce data modeling concepts by applying them to practical exercises.
Browse courses on
Data Modeling
Show steps
- Find practice problems on data modeling and normalization.
- Solve the problems using appropriate techniques.
Create a glossary of key data modeling and transformation terms
Show steps
Enhance your understanding by defining and organizing important terms related to the course.
Browse courses on
Data Modeling
Show steps
- Identify key terms from the course materials.
- Define each term concisely and accurately.
- Organize the terms alphabetically or by category.
Review DBMS Fundamentals by Ramez Elmasri
Show steps
Review core database concepts to strengthen your understanding of data modeling and transformation.
Show steps
- Read Chapters 1-3: Database Concepts, Data Modeling, and Relational Model.
- Complete the practice exercises at the end of each chapter.
Participate in peer-led review sessions
Show steps
Engage in discussions and exchange ideas with peers to enhance understanding.
Browse courses on
Data Modeling
Show steps
- Form a peer group with classmates.
- Meet regularly to discuss course concepts, assignments, and projects.
- Provide constructive feedback and support to each other.
Follow tutorials on Apache Spark for Distributed Processing
Show steps
Gain practical knowledge of Spark's capabilities for data processing at scale.
Browse courses on
Apache Spark
Show steps
- Find tutorials on Spark's RDD, DataFrame, and SQL interfaces.
- Follow along with the tutorials and complete the exercises.
Create a Data Transformation Plan for a Real-World Dataset
Show steps
Develop hands-on experience in transforming a real-world dataset to meet specific requirements.
Browse courses on
Data Transformation
Show steps
- Identify a raw dataset of interest.
- Define the desired transformed dataset structure.
- Perform data cleaning and transformation using appropriate tools or libraries.
- Document the data transformation steps and rationale.
Contribute to open-source data transformation projects
Show steps
Gain hands-on experience and contribute to a real-world project related to data transformation.
Browse courses on
Open Source
Show steps
- Identify open-source data transformation projects.
- Find issues or areas where you can contribute.
- Follow project guidelines and submit your contributions.
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