ETL Code Process – Manual vs Low-Code

ETL is one of the most important processes in Data Integration. It is an acronym for Extract, Transform, and Load. Each of these processes in ETL is not so straightforward, which is why some organizations employ developers to build scalable ETL systems to handle their Data Integrations from different sources.

The advent and hype of Low-Code solutions have affected the approach to the ETL process and that gives companies a choice of whether to follow the manual ETL Code approach or the Low-Code approach. In this article, you’ll be taken through the advantages and shortcomings of manual ETL Code and Low-Code ETL processes.  Keep in mind that the process has to be efficient and timely for smooth processing across all workflows in the data processing of an organization.

Components of ETL Code

The ETL process is essential for transforming raw data into actionable insights, but how you build it can vary significantly. While manual coding offers complete control and customization, it can be time-consuming and complex. On the other hand, low-code platforms simplify ETL development, making it faster and more accessible, even for those with limited coding experience. In this article, you’ll be taken through the advantages and shortcomings of manual ETL Code and Low-Code ETL processes.

1. Extract Code:

• Purpose: Writing scripts or code to pull data from various sources.
• Typical Sources:
  • Databases (e.g., MySQL, PostgreSQL)

The below code is a sample code to extract your Data from a database such as PostgreSQL

import pandas as pd
from sqlalchemy import create_engine

# Create a connection to the database
engine = create_engine('postgresql://username:password@hostname:port/database')

# SQL query to extract data
query = "SELECT * FROM source_table"

# Extract data into a DataFrame
df = pd.read_sql(query, engine)

• APIs (e.g., RESTful services)

The below code in Python is a sample code to fetch your data in JSON format.

import requests
import pandas as pd

# API URL
url = "https://api.example.com/data"
# API request
response = requests.get(url)

# Convert the JSON response to a DataFrame
df = pd.json_normalize(response.json())

• Flat files (e.g., CSV, JSON)

The below code uses Pandas to read a CSV file. Similarly, you can also use the read_json function to extract data from your JSON file.

import pandas as pd

# Read data from a CSV file
df = pd.read_csv('path/to/file.csv')

• Tools/Technologies: SQL, Python, custom connectors.

2. Transform Code:

• Purpose: Applying transformation logic to the extracted data.
• Common Operations:
  • Data cleansing (removing duplicates, handling missing values)
  • Data formatting (date conversions, data type changes)
  • Business logic implementation.
• Tools/Technologies: SQL, Python, Pandas, or other scripting languages.

Data Transformation using Pandas

# Data cleansing - Removing duplicates
df = df.drop_duplicates()

# Data formatting - Converting date strings to datetime objects
df['date_column'] = pd.to_datetime(df['date_column'])

# Business logic - Calculating a new column
df['new_column'] = df['existing_column1'] + df['existing_column2']

Data Transformation Logic using SQL

# Assuming 'df' is a DataFrame containing the data
transformed_query = """
SELECT 
    id,
    UPPER(name) AS name_uppercase,
    TO_CHAR(date_column, 'YYYY-MM-DD') AS formatted_date,
    (column1 + column2) AS new_column
FROM df
"""

# Execute the transformation logic
transformed_df = pd.read_sql(transformed_query, engine)

3. Load Code:

• Purpose: Loading the transformed data into the target system.
• Target Systems:
  • Databases (e.g., MySQL, PostgreSQL)

The below code is a sample code for loading your data into a database.
Note: If you have a table in your database with the same name, it will forcefully replace it with this new, current table.

# Assuming 'df' is a DataFrame containing the transformed data

# Load data into a target table in a database
df.to_sql('target_table', engine, if_exists='replace', index=False)

• Data Warehouses (e.g., Amazon Redshift, Google BigQuery)

In this code, we append the values to our target table.

# Assuming 'df' is a DataFrame containing the transformed data

# Using the same SQLAlchemy engine for Redshift
df.to_sql('target_table', engine, schema='public', if_exists='append', index=False)

• Tools/Technologies: SQL, Python, or specific loading utilities.

4. Error Handling and Logging:

• Purpose: Ensuring the ETL process is robust, and errors are managed.
• Techniques:
  • Try/Except blocks for catching exceptions.
  • Logging frameworks (e.g., Python’s logging module) to record successes, failures, and errors.
• Importance: Critical for debugging and ensuring data integrity.

import logging
# Configure logging
logging.basicConfig(filename='etl_process.log', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

def extract_data():
    try:
        # Example of extracting data
        df = pd.read_sql(query, engine)
        logging.info('Data extraction successful')
        return df
    except Exception as e:
        logging.error('Data extraction failed: %s', e)
        raise
def transform_data(df):
    try:
        # Example of transforming data
        df['date_column'] = pd.to_datetime(df['date_column'])
        logging.info('Data transformation successful')
        return df
    except Exception as e:
        logging.error('Data transformation failed: %s', e)
        raise
def load_data(df):
    try:
        # Example of loading data
        df.to_sql('target_table', engine, if_exists='replace', index=False)
        logging.info('Data loading successful')
    except Exception as e:
        logging.error('Data loading failed: %s', e)
        raise

# Example ETL Process
try:
    data = extract_data()
    transformed_data = transform_data(data)
    load_data(transformed_data)
    logging.info('ETL process completed successfully')
except Exception as e:
    logging.critical('ETL process failed: %s', e)

Manual ETL Code Process

The manual ETL Code approach requires a developer to build a system with a Programming Language and perform the three processes of Extract, Transform, and Load (ETL) with this language while ensuring concurrency, parallelism, and scalability.

The Extraction process gets data from different data sources such as Excel files, CSV files, Databases like SQL Server, etc., and validates this data to make sure it meets the required benchmark. 

The Transform process starts with Data Cleansing, which is just as important as Data Validation. The process of Data Cleansing is necessary to make sure the required data is loaded to the target data store. The target datastore will receive data in a specified format and this makes the transformation process a particularly tedious one as it has to convert data from different sources and communication protocols into a single supported format by the data store. 

Developers invest a good chunk of their Engineering expertise, time, and resources to maintain the data quality, and make sure all the transformation takes place outside of the data store or Data Warehouse.

The Load process, which is the final stage, lodges the cleaned and processed data in a Data Warehouse (usually on the Cloud). Cloud Data Warehouses like Google BigQuery, Amazon Redshift, and Snowflake provide in-house tools to query and process data coming from any source and make sense of them by extracting actionable insights.

With the manual ETL Code approach, a company has to employ a developer to build and manage the ETL system for them. According to ZipRecruiter, the average salary of a full-time ETL Developer is about $110k ($109, 881 to be exact).

Pros of the Manual ETL Code Approach

• Customization: The biggest advantages of the manual ETL approach are centered around customizing to the organization’s unique needs. Because there’s a developer on standby, the Data Analysts can specify how they want data to come in based on their preference and how best it serves the company’s interest. This is not the case with Low-Code tools.

Cons of the Manual ETL Code Approach

• Cost: The cost of hiring an ETL Developer might not always be favorable for a company in terms of expenses. The data to be processed might not be as bogus, and they might need a cost-effective option.
  
• Maintenance: Maintenance is just as important for any data processing system. Your ETL Code needs to be updated regularly as development tools upgrade their dependencies, industry standards change, and processing capacity gets closer. Maintaining this system requires you to read the old ETL Codes (which you might not always want to do) and refactor.
  
• Scalability: The scalability of an ETL system is paramount for successful data processing. ETL systems can fail over time if conditions for processing fails. What if incoming data increases 10X, will it still be at the same speed? Questions like this require serious thinking while opting for the manual ETL Code approach.

Low-Code ETL Process

Low-Code ETL processes might not need a developer to oversee them as they usually exhibit intuitive user interfaces for non-technical personnel to understand. Some come with simple Drag-and-Drop functionalities to select Data Sources and much more.

One of the finest examples of such a Low-Code or a No-Code solution is Hevo Data. Hevo Data relieves the technical stress off developers with a zero-maintenance system that is completely automated.

Pros of Low-Code ETL Tools

• Cost: You don’t need to pay a developer to handle your ETL processes anymore. All you have to do is pick a subscription plan offered by your ETL tool provider and follow up with the features provided. The cost of incorporating a Low-Code ETL tool is a mere fraction of what it takes to hire a developer that can hit the ground running.

• Maintenance: This is a key aspect for most Low-Code ETL tools – maintenance. Maintenance in almost all aspects, maintaining your Codebase, Data Source integrations, etc., is taken care of by the provider. A simple drag-and-drop user interface that doesn’t require programming knowledge to oversee is the unique selling point for most Low-Code ETL tools. Also, who will have to look after the Codebase for security bugs? Absolutely no one! This takes off so much stress from the developer(s). They can focus on other core aspects of the data processing cycle.

• Scalability and Performance: Scalability in this context would encompass Schema (Database table) Management. In all honesty, an expert ETL Developer will do just as much to develop a scalable and reliant ETL system, but the cost might not always be in favor of hiring or training one.
  Low-Code ETL tools do not require you to add more processing nodes and clusters as input increases. This means scaling out is not an issue for most. In terms of performance, it is usually directly proportional to scalability – as processing input increases, more processing nodes are added automatically meaning the speed of the system does not lag. All of these with no developer to oversee.

4. Code Workflows: Workflows are an important aspect of any development process. Since ETL tools are usually providing output, a developer can keep the processes in line as the integration goes. Low-Code ETL tools do not need you to manage any framework, all they need is an input with a drag-and-drop interface. Nothing breaks the development workflow.

As development and integration go, ETL tools are critical for a successful push to production.

Conclusion

This article has given you a detailed understanding of manual ETL Code and Low-Code ETL approaches by comparing the pros and cons of both solutions. This comparison will probably help you to zero in on one of these solutions for your company.

If your organization decides to go with a Low-Code ETL tool, it is also important to know if they allow customization – this is where the manual ETL approach trumps the latter. You can check out Hevo Data features and decide to not opt for another tool.

visit our website to explore hevo

Hevo Data with its strong integration with 150+ Sources & BI tools allows you to not only export data from sources & load data in the destinations, but also transform & enrich your data, & make it analysis-ready so that you can focus only on your key business needs and perform insightful analysis using BI tools.

Give Hevo Data a try by sign up for a 14-day free trial today. Hevo offers plans & pricing for different use cases and business needs, check them out!

FAQ on ETL Code Process