Como Usar a Orquestração em Python?

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Python Orchestration and ETL Tools

Introduction to Data Pipelines

• Chapter 1: Introduction to Data Pipelines

  • Learn about the process of collecting, processing, and moving data using data pipelines.
  • Understand the qualities of the best data pipelines.
  • Prepare to design and build your own data pipelines.

• Chapter 2: Building ETL Pipelines

  • Dive into leveraging pandas to extract, transform, and load data in your pipelines.
  • Build your first data pipelines.
  • Make your ETL logic reusable.
  • Apply logging and exception handling to your pipelines.

• Chapter 3: Advanced ETL Techniques

  • Supercharge your workflow with advanced data pipelining techniques.
  • Work with non-tabular data and persist DataFrames to SQL databases.
  • Discover tooling to tackle advanced transformations with pandas.
  • Uncover best practices for working with complex data.

• Chapter 4: Deploying and Maintaining a Data Pipeline

  • Create frameworks to validate and test your data pipelines before production.
  • Test your pipeline manually and at “checkpoints”.
  • Perform end-to-end testing of your data pipelines.
  • Unit-test your data pipeline.
  • Validate a data pipeline using assert and isinstance.
  • Write unit tests with pytest.
  • Create fixtures with pytest.
  • Unit test a data pipeline using fixtures.
  • Run a data pipeline in production.
  • Explore different data pipeline architecture patterns.
  • Run a data pipeline end-to-end.

Congratulations on completing the course!

Sample Code

# Importing the required modules
import pandas as pd
import logging

# ETL function that takes data as input and returns processed data
def etl_process(data):
    try:
        # Perform data transformations using pandas
        processed_data = data.apply(lambda x: x*2)

        return processed_data

    except Exception as e:
        # Exception handling with logging
        logging.error(f"ETL process failed: {e}")
        raise

# Load data using pandas
data = pd.read_csv('data.csv')

# Execute the ETL process
processed_data = etl_process(data)

# Save processed data to a SQL database
processed_data.to_sql('processed_data', 'sqlite:///data.db')

# Validating the data pipeline using assert
assert len(processed_data) > 0
assert isinstance(processed_data, pd.DataFrame)

# Unit tests using pytest
import pytest

def test_etl_process():
    # Generate dummy data for testing
    test_data = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

    # Test the ETL process
    processed_test_data = etl_process(test_data)

    # Verify the output
    assert len(processed_test_data) == 3
    assert isinstance(processed_test_data, pd.DataFrame)
    assert all(processed_test_data.iloc[0] == [2, 8])
    assert all(processed_test_data.iloc[1] == [4, 10])
    assert all(processed_test_data.iloc[2] == [6, 12])

Conclusion

In this Python tutorial, you have learned about orchestration and ETL tools for data pipelines. You explored various chapters that covered the introduction to data pipelines, building ETL pipelines using pandas, advanced ETL techniques, and deploying and maintaining a data pipeline. You also saw step-by-step sample codes with explanations and executable examples. By mastering these concepts and techniques, you are now well-equipped to design and build your own data pipelines using Python. Happy coding!