Difference between ETL and ELT pipeline in Data Engineering

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two common data integration processes used in data engineering. Both involve moving data from source systems to a target system, but they differ in the sequence of operations.

ETL (Extract, Transform, Load)

1. Extract: Data is extracted from various source systems.

2. Transform: Data is transformed into a suitable format. This can include cleaning, aggregating, and enriching the data.

3. Load: Transformed data is loaded into the target system, such as a data warehouse.

Use Case: ETL is ideal when data transformations are complex and need to be done before loading into the target system to ensure data quality and consistency.

ELT (Extract, Load, Transform)

1. Extract: Data is extracted from various source systems.

2. Load: Raw data is loaded directly into the target system.

3. Transform: Data is transformed within the target system, leveraging its processing power.

Use Case: ELT is suitable for large volumes of data where the target system (e.g., a cloud data warehouse) can handle the transformation efficiently.

Example: Decrypting an Encrypted CSV File in AWS S3, Enriching Data, and Storing in MongoDB

Step-by-Step Implementation

1. Store Encrypted CSV File in AWS S3:

   • Upload an encrypted CSV file to an S3 bucket.

2. Decrypt the CSV File Using AWS Lambda:

   • Create a Lambda function to decrypt the file using Python.

3. Enrich the Data:

   • Process the decrypted data to add additional information or clean it.

4. Store the Data in MongoDB:

   • Insert the enriched data into a MongoDB collection.

Step 1: Store Encrypted CSV File in AWS S3

• Use the AWS Management Console or AWS CLI to upload the encrypted CSV file to an S3 bucket.

Step 2: Decrypt the CSV File Using AWS Lambda

1. Create a Lambda Function:

   • Set up a Lambda function with the necessary permissions to access the S3 bucket and decrypt the file.

2. Lambda Function Code:

   Python

   import boto3
   import csv
   import base64
   from Crypto.Cipher import AES
   from pymongo import MongoClient
   
   s3 = boto3.client('s3')
   kms = boto3.client('kms')
   
   def decrypt_data(encrypted_data, key):
       cipher = AES.new(key, AES.MODE_EAX)
       decrypted_data = cipher.decrypt(base64.b64decode(encrypted_data))
       return decrypted_data
   
   def lambda_handler(event, context):
       bucket = event['Records'][0]['s3']['bucket']['name']
       key = event['Records'][0]['s3']['object']['key']
   
       # Download the encrypted file from S3
       response = s3.get_object(Bucket=bucket, Key=key)
       encrypted_data = response['Body'].read()
   
       # Decrypt the data
       decryption_key = kms.decrypt(CiphertextBlob=base64.b64decode(encrypted_data))['Plaintext']
       decrypted_data = decrypt_data(encrypted_data, decryption_key)
   
       # Process the CSV data
       csv_data = csv.reader(decrypted_data.decode('utf-8').splitlines())
       enriched_data = []
       for row in csv_data:
           # Enrich the data (example: add a new column)
           row.append('enriched_value')
           enriched_data.append(row)
   
       # Store the enriched data in MongoDB
       client = MongoClient('mongodb://your_mongodb_uri')
       db = client.your_database
       collection = db.your_collection
       for row in enriched_data:
           document = { 'column1': row[0], 'column2': row[1], 'enriched_column': row[2] }
           collection.insert_one(document)
   
       return {
           'statusCode': 200,
           'body': 'Data processed and stored successfully'
       }

Step 3: Enrich the Data

In the Lambda function, you can enrich the data by adding new columns or cleaning existing data. Here’s an example:

Python

import boto3
import csv
import base64
from Crypto.Cipher import AES
from pymongo import MongoClient

s3 = boto3.client('s3')
kms = boto3.client('kms')

def decrypt_data(encrypted_data, key):
    cipher = AES.new(key, AES.MODE_EAX)
    decrypted_data = cipher.decrypt(base64.b64decode(encrypted_data))
    return decrypted_data

def lambda_handler(event, context):
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = event['Records'][0]['s3']['object']['key']

    # Download the encrypted file from S3
    response = s3.get_object(Bucket=bucket, Key=key)
    encrypted_data = response['Body'].read()

    # Decrypt the data
    decryption_key = kms.decrypt(CiphertextBlob=base64.b64decode(encrypted_data))['Plaintext']
    decrypted_data = decrypt_data(encrypted_data, decryption_key)

    # Process the CSV data
    csv_data = csv.reader(decrypted_data.decode('utf-8').splitlines())
    enriched_data = []
    for row in csv_data:
        # Enrich the data (example: add a new column)
        row.append('enriched_value')
        enriched_data.append(row)

    # Store the enriched data in MongoDB
    client = MongoClient('mongodb://your_mongodb_uri')
    db = client.your_database
    collection = db.your_collection
    for row in enriched_data:
        document = { 'column1': row[0], 'column2': row[1], 'enriched_column': row[2] }
        collection.insert_one(document)

    return {
        'statusCode': 200,
        'body': 'Data processed and stored successfully'
    }

Step 4: Store the Data in MongoDB

Using the pymongo library, you can connect to MongoDB and insert the enriched data as documents. Here’s how you can do it:

1. Install pymongo:

   pip install pymongo

2. Connect to MongoDB and Insert Data:

   Python

   from pymongo import MongoClient
   
   # Connect to MongoDB
   client = MongoClient('mongodb://your_mongodb_uri')
   db = client.your_database
   collection = db.your_collection
   
   # Example enriched data
   enriched_data = [
       {'column1': 'value1', 'column2': 'value2', 'enriched_column': 'enriched_value1'},
       {'column1': 'value3', 'column2': 'value4', 'enriched_column': 'enriched_value2'}
   ]
   
   # Insert data into MongoDB
   collection.insert_many(enriched_data)

Summary

This example demonstrates how to implement an ETL pipeline using AWS Lambda to decrypt an encrypted CSV file stored in S3, enrich the data, and store it in MongoDB. This approach leverages the power of AWS services and Python to handle data securely and efficiently