Building Data Pipelines Using Python Tutorial | Data Pipelines Using Python Course
About This Course
This Python 3 course teaches data professionals how to create data pipelines and automate workflows using various frameworks like Airflow and Luigi. Assumes intermediate-level Python experience and covers important workflow concepts. Gain practical experience in parallelizing data analysis and writing data tests using property-based testing.
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Timestamp
Introduction
0:00:00 Welcome To The Course
Automation 101
0:02:23 Introduction To Automation
0:05:11 Adventures With Servers
0:11:48 Being A Good Systems Caretaker
0:17:51 What Is A Queue?
0:20:23 What Is A Consumer? What Is A Producer?
Easy Task Processing With Celery
0:22:23 Why Celery?
0:24:12 Celery Architecture & Set Up
0:29:37 Writing Your First Tasks
0:37:26 Deploying Your Tasks
0:43:34 Scaling Your Workers
0:52:26 Monitoring With Flower
0:57:31 Advanced Celery Features****
Scaling Data Analysis With Dask
1:03:31 Why Dask?
1:06:32 First Steps With Dask
1:16:40 Dask Bags
1:26:58 Dask Distributed
Data Pipelines With Luigi & Airflow
1:36:56 Python Data Pipelines
1:37:06 What Are Data Pipelines? What Is Dag?
1:39:43 Luigi And Airflow: A Comparison
1:45:33 First Steps With Luigi
1:52:45 More Complex Luigi Tasks
2:02:02 Introduction To Hadoop
2:10:23 First Steps With Airflow
2:18:30 Custom Tasks With Airflow
2:27:46 Advanced Airflow: Subdags And Branches
2:39:03 Using Luigi With Hadoop
Other Workflow Frameworks
2:49:18 Apache Spark
2:57:46 Apache Spark Streaming
3:04:18 Django Channels
3:13:57 And Many More
Testing With Pipelines
3:19:56 Testing pipelines
3:20:06 Introduction To Testing With Python
3:27:30 Property-Based Testing With Hypothesis
Conclusion
3:33:39 What's Next?
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