Advanced AWS Glue Tutorial Series: Part 2- Exploring Types of ETL Jobs|

AWS Glue Jobs

Advanced AWS Glue Tutorial Series: Part 2- Exploring Types of ETL Jobs

Welcome back to our advanced AWS Glue tutorial series. In this blog , we’ll delve into AWS Glue’s powerful ETL (Extract, Transform, Load) job types, providing real-world code examples that demonstrate the platform’s adaptability and capacity.

1. Python Shell Jobs

Python Shell Jobs in AWS Glue are essential for simplifying and precision data processing processes. Below is a brief example of a Python Shell Job that modifies a CSV file by adding a new column and applying data filtering:

import pandas as pd

# Load data from an S3 source
input_bucket = 'input-bucket'
input_key = 'data/input.csv'
df = pd.read_csv(f's3://{input_bucket}/{input_key}')

# Perform data transformation
df['new_column'] = df['old_column'] * 2
filtered_df = df[df['some_condition'] == True]

# Export the result to S3
output_bucket = 'output-bucket'
output_key = 'data/output.csv'
filtered_df.to_csv(f's3://{output_bucket}/{output_key}', index=False)

2. Apache Spark Jobs

Complex data transformations become simple when using the vast power of Apache Spark within AWS Glue. Consider the word count on a text file as an example.

Want to learn Apache Spark follow this link: here
from pyspark.context import SparkContext
from pyspark.sql import SparkSession

# Initialize Spark
sc = SparkContext()
spark = SparkSession(sc)

# Load data from an S3 source
input_bucket = 'input-bucket'
input_key = 'data/input.txt'
lines ='s3://{input_bucket}/{input_key}')

# Perform word count
word_counts = lines.rdd.flatMap(lambda line: line[0].split(' ')).countByValue()

# Store the results back in S3
output_bucket = 'output-bucket'
output_key = 'data/word_counts.txt'
with open(f's3://{output_bucket}/{output_key}', 'w') as f:
    for word, count in word_counts.items():
        f.write(f'{word}: {count}\n')

3. Ray Jobs

Ray Jobs in AWS Glue come to top of the list for compute-intensive operations that require distribution and parallelization. Consider processing a series of photos in parallel:

import ray

def process_image(image_path):
    # Some processing logic
    return processed_image

# List of image paths
image_paths = ['image1.jpg', 'image2.jpg', 'image3.jpg']

# Process images in parallel
results = ray.get([process_image.remote(path) for path in image_paths])

# Save processed images back to S3
output_bucket = 'output-bucket'
for i, processed_image in enumerate(results):
    output_key = f'processed/image_{i}.jpg''s3://{output_bucket}/{output_key}')

4. Jupyter Notebook Jobs

AWS Glue Jupyter Notebook Jobs provide an interactive and collaborative environment for data exploration and analysis. Here’s an example of how to use Jupyter Notebook for data exploration:

# Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt

# Load and explore data
data = pd.read_csv('data.csv')

# Visualize data
plt.scatter(data['X'], data['Y'])
plt.title('Scatter Plot')

Choosing the Right Job Type

Selecting the right job type depends on the specific requirements of your ETL tasks:

Best Practices for ETL Jobs

Regardless of the job type you choose, it’s essential to follow best practices:

  1. Data Validation: Ensure your data is clean and valid before processing it.
  2. Error Handling: Implement robust error handling to handle unexpected issues gracefully.
  3. Monitoring: Keep an eye on job execution with AWS Glue’s monitoring features.
  4. Testing: Test your ETL jobs thoroughly in development environments before deploying them to production.
  5. Documentation: Document your code, transformations, and job dependencies for future reference.
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