Data Quality Testing Unleashed : Theory to Implementation

Focused View

3:20:20

0 View

1 - Introduction

1 -Who is this course for.mp4

01:45

2 -Prerequisite for the course.mp4

02:01

3 -Course structure.mp4

03:20

4 - Practice Along with Me Resources.html

2 - Fundamentals of Data Quality & Measurement Dimensions

1 -What is Data Quality.mp4

03:22

2 -Importance of Data Quality.mp4

04:04

3 -Data Quality Measurement Dimensions.mp4

02:16

4 -Accuracy.mp4

01:44

5 -Completeness.mp4

03:13

6 -Consistency.mp4

02:00

7 -Timeliness.mp4

01:56

8 -Validity.mp4

01:43

9 -Uniqueness.mp4

02:11

10 -Summary.mp4

01:02

3 - Introduction to Data Quality Testing

1 -What is Data Quality Testing.mp4

02:16

2 -Importance of Data Quality Testing.mp4

04:17

3 -Difference Data Testing vs Data Quality Testing.mp4

02:23

4 -Data Quality Testing Tools.mp4

04:38

5 -Typical Data Pipeline with Quality Testing Integration.mp4

04:21

6 -Best Practices Data Quality Testing.mp4

06:21

7 -Summary.mp4

02:30

4 - Getting Started with Great Expectations (GX Core)

1 -Why Great Expectations (GX).mp4

02:12

2 -Introduction to the Great Expectations Library.mp4

01:21

3 -GX Core Building Blocks.mp4

07:11

4 -Installing and Setting Up Great Expectations.mp4

01:20

4 - Python & Jupyter Notebook Installation.html

5 -Creating First GX Core workflow Part 1.mp4

06:05

6 -Creating First GX Core workflow Part 2.mp4

07:08

6 -creating first workflow.zip

7 -Interpreting Validation Result.mp4

04:25

7 -interpreting result.zip

8 -Customising Validation Result Part 1.mp4

02:40

9 -Customising Validation Result Part 2.mp4

05:43

9 -customise result format.zip

10 -Summary.mp4

01:15

5 - Deep Dive into Expectations

1 -Creating Parameterised Expectations Part 1.mp4

03:06

2 -Creating Parameterised Expectations Part 2.mp4

04:03

2 -parameterised expectations.zip

3 -Conditional Expectations Part 1.mp4

02:11

4 -Conditional Expectations Part 2.mp4

03:22

4 -conditional expectations.zip

5 -Custom Expectations Part 1.mp4

02:39

6 -Custom Expectations Part 2.mp4

06:21

6 -custom expectations.zip

7 -Exploring Expectations Gallery - Table Level.mp4

02:16

8 -Exploring Expectations Gallery - Column Level.mp4

01:43

9 -Exploring Expectations Gallery - Column Aggregate.mp4

01:48

10 -Exploring Expectations Gallery - Column Distribution.mp4

01:22

11 -Exploring Expectations Gallery - Set Based.mp4

01:27

12 -Summary.mp4

02:18

6 - Exploring Actions

1 -Introduction to Actions.mp4

02:55

2 -Integrating SendEmail Action in Workflow.mp4

06:13

2 -send email action.zip

3 -Built in Actions available in GX Core.mp4

03:31

4 -Summary.mp4

01:20

7 - Enhancing Data Quality Visibility with Data Docs

1 -Introduction to Data Docs.mp4

03:36

2 -Integrating Data Docs in Workflow.mp4

04:59

2 -generate data docs.zip

3 -Reviewing generated Data Docs.mp4

04:20

4 -Summary.mp4

01:33

8 - Advanced Concepts

1 -Scaling Testing using Multiple Batches & Sqlite Database.mp4

06:18

1 -sqlite and batches.zip

2 -Multiple Datasources in a Single Workflow.mp4

03:34

2 -multiple data sources.zip

3 -Customised SQL Expectation.mp4

04:12

3 -customised SQL expectation.zip

4 -Persistant Data Context.mp4

02:23

5 -Persistant Data Context - File Structure Walkthrough.mp4

08:12

5 -file data context.zip

5 - Note Notebook for next two lectures.html

6 -Reloading Data Context.mp4

06:44

7 -Manage Sensitive Data.mp4

03:11

8 -Best Practices.mp4

04:08

8 - Code Snippet - Managing Secrets.html

9 -Summary.mp4

02:31

9 - Conclusion and Next Steps

1 -Thank You!.mp4

01:21

1 - Next Steps.html

Description

Learn Essential Data Quality Principles, Implement Testing with Python and Great Expectations Framework

What You'll Learn?

Gain a clear understanding of the essential principles of Data Quality and Data Quality Testing, equipping you with the knowledge to delivering Quality Data.
Build robust Data Quality Testing workflows using the Great Expectations, mastering the design and automation of tests to ensure outstanding data quality.
Explore the Great Expectations testing framework, gaining insights into its foundational components and how they work together to ensure robust data validation.
Develop thorough data documentation and automate actions that respond to published data quality test results, ensuring proactive management of data quality.

Who is this for?

Data Engineers / Testers : Implement effective data quality practices.

Data Project/Product Managers: Essential knowledge to oversee data quality initiatives effectively.

Anyone who is interested to learn more about data quality and testing

What You Need to Know?

Experience with Python: A basic understanding of Python programming is required, as the course involves hands-on implementations using Python with Great Expectations.

Basic Understanding of Data Concepts: Familiarity with fundamental data concepts like database, data pipeline, etc

Interest in Data Quality Testing : A willingness to learn about data validation and testing processes will be beneficial for maximising the course outcomes.

More details

Description
Data Quality Testing Unleashed: From Theory to Implementation is your comprehensive roadmap to mastering Data Quality Testing using Python and the powerful Great Expectations framework. It is designed for those who want to elevate their data projects by ensuring high-quality and reliable data. This course takes you from foundational principles to hands-on implementation.
In this course, we'll explore:
Fundamentals of Data Quality & Testing: Discover the core principles that underpin data quality and testing, with a focus on critical dimensions like accuracy, completeness, and consistency. Youâ€™ll understand how these elements contribute to trustworthy, dependable data.
Introduction to the Great Expectations Framework: Gain proficiency with Great Expectations, the leading open-source tool for data validation, documentation, and profiling. This framework is crafted to set and enforce data standards, ensuring that data meets the highest quality benchmarks.
The Building Blocks of Great Expectations: Uncover the core components of Great Expectations, learning how to structure workflows that bring them to life. Youâ€™ll dive into the extensive expectations library, equipping yourself with versatile tools to meet diverse data validation needs.
Hands-On Data Quality Testing: With a focus on practical application, this course will guide you through creating multiple testing workflows. Youâ€™ll learn how to publish results, automate actions based on test outcomes, and build experience in efficiently managing data quality testing in real-world scenarios.
By the end of this course, youâ€™ll have a thorough understanding of data quality testing principles and hands-on skills in applying the Great Expectations framework. Youâ€™ll be ready to deliver data that meets rigorous quality standards and confidently contribute to any data project with best-in-class testing practices.
Who this course is for:
Data Engineers / Testers : Implement effective data quality practices.
Data Project/Product Managers: Essential knowledge to oversee data quality initiatives effectively.
Anyone who is interested to learn more about data quality and testing

User Reviews

Rating

average 0

Total votes0

Focused display

Python

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.