Building robust data pipelines and scalable solutions.
Transforming raw data into actionable insights with precision and efficiency.
# ETL Pipeline Architecture
import pandas as pd
import apache_spark
from airflow import DAG
class DataPipeline:
def __init__(self):
self.source = "raw_data"
self.destination = "warehouse"
def extract(self):
# Extract from multiple sources
return pd.read_sql(query, conn)
def transform(self, data):
# Clean and transform data
return data.pipe(clean).pipe(validate)
def load(self, data):
# Load to data warehouse
data.to_sql('analytics', engine)Designing and building scalable ETL pipelines that process millions of records efficiently.
Extracting valuable data from web sources with precision and automation.
Implementing ML models for data analysis and predictive insights.
Building full-stack applications with modern technologies and best practices.
Available for remote opportunities worldwide and on-site collaboration in the MENA region.
Get to know me →