Hi, I'mMayank
About Me
I'm a passionate Data Engineer and Full-stack Developer who loves creating amazing web experiences and building robust data pipelines. With expertise in modern web technologies and data engineering tools, I build scalable applications and data solutions that make a difference.
Featured Projects
Some of my recent work and side projects that showcase my skills
AI Design Tool
A modern AI-powered design tool that generates logos, color palettes, and design suggestions using OpenAI APIs.
E-Commerce Platform
A full-stack e-commerce solution built with Next.js, Stripe, and PostgreSQL.
Task Management App
A collaborative task management tool with real-time updates and team features.
Interactive Data Pipeline
Click "Run Pipeline" to see how data flows through my ETL process
Extract data from PostgreSQL database
Live Code Editor
Try out real data engineering code examples. Click "Run Code" to see the results!
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, when, isnan, isnull
# Initialize Spark session
spark = SparkSession.builder \
.appName("CustomerDataETL") \
.getOrCreate()
# Read data from source
df = spark.read \
.format("jdbc") \
.option("url", "jdbc:postgresql://localhost:5432/customers") \
.option("dbtable", "customer_data") \
.load()
# Data cleaning and transformation
cleaned_df = df \
.filter(col("age").isNotNull()) \
.filter(col("age") > 0) \
.withColumn("age_group",
when(col("age") < 25, "Young")
.when(col("age") < 50, "Middle")
.otherwise("Senior")) \
.withColumn("is_premium", col("purchase_amount") > 1000)
# Write to data warehouse
cleaned_df.write \
.format("parquet") \
.mode("overwrite") \
.save("s3://data-lake/customers/cleaned/")
print(f"Processed {{cleaned_df.count()}} records")Apache Spark ETL Pipeline
Extract, transform, and load data using PySpark
My Projects
Explore my portfolio of data engineering, web development, and ML projects
Category
Status
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Showing 8 of 8 projects
ML Recommendation Engine
Developed a collaborative filtering recommendation system using PySpark MLlib and deployed with MLflow.
ML Feature Store
Built a centralized feature store using Feast for managing ML features across multiple models.
Real-time Stream Processing
Implemented real-time data processing using Apache Flink and Apache Pulsar for event-driven architecture.
A/B Testing Platform
Developed a statistical analysis platform for A/B testing with automated experiment evaluation.
Data Pipeline Monitoring
Built a comprehensive monitoring system using Grafana, Prometheus, and custom alerting for data pipelines.
Cloud Data Warehouse
Designed and implemented a cloud-based data warehouse using Snowflake and dbt for modern analytics.
Technical Blog
Insights, tutorials, and best practices from my data engineering journey
Category
Difficulty
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Showing 8 of 8 articles
Featured Articles
Optimizing Apache Spark for Large-Scale Data Processing
Learn advanced techniques to optimize your Spark jobs for processing terabytes of data efficiently.
ETL Pipeline Design Patterns and Best Practices
Comprehensive guide to building robust, scalable ETL pipelines with real-world examples.
Recent Articles
Optimizing Apache Spark for Large-Scale Data Processing
Learn advanced techniques to optimize your Spark jobs for processing terabytes of data efficiently.
ETL Pipeline Design Patterns and Best Practices
Comprehensive guide to building robust, scalable ETL pipelines with real-world examples.
MLOps with MLflow: Managing Machine Learning Lifecycle
Complete guide to implementing MLOps practices using MLflow for model management and deployment.
Building Interactive Data Visualizations with D3.js
Step-by-step tutorial on creating stunning, interactive data visualizations for your web applications.
SQL Query Optimization: 10 Essential Tips for Data Engineers
Master SQL optimization techniques to write faster, more efficient queries for large datasets.
Modern Data Warehouse Architecture: From Star Schema to Data Vault
Explore different data warehouse modeling techniques and when to use each approach.
Building Real-Time Analytics with Apache Kafka and ClickHouse
Learn how to build a real-time analytics system using Kafka for streaming and ClickHouse for storage.
Essential Python Libraries for Data Engineering
A curated list of must-know Python libraries for data engineers, from Pandas to Airflow.