Data Engineer

100 Days to Become a Data Engineer

Program Overview

This is an intensive 100-day program designed to train a Data Engineer from fundamentals to advanced projects. The plan includes key technologies, constant practice, and real-world projects.

Program Structure

Phase 1: Fundamentals (Days 1-13)

SQL - Relational Databases (Days 1-4)

  • Day 1: SQL introduction and basic commands (SELECT, FROM, WHERE)
  • Day 2: Aggregation functions (SUM, AVG, COUNT), GROUP BY, HAVING, JOINs
  • Day 3: Subqueries, CTEs, table creation, optimization
  • Day 4: Practice: Database construction and complex queries

Python for Data Engineering (Days 5-9)

  • Day 5: Basic syntax, variables, control structures
  • Day 6: Functions, modules, file manipulation (CSV, TXT)
  • Day 7: Exception handling, libraries (pandas, numpy, json, requests)
  • Day 8: Practice: Data cleaning and transformation with pandas
  • Day 9: Project: API data extraction and CSV storage

Linux and Bash (Days 10-13)

  • Day 10: Linux basics, fundamental commands
  • Day 11: Permissions, users, pipes, redirections, bash scripts, SSH
  • Day 12: Practice: Cron jobs for periodic tasks
  • Day 13: Practice: Bash script for file cleanup and organization
#!/bin/bash
# Example: Data processing bash script

LOG_DIR="/var/log/data_pipeline"
DATA_DIR="/data/raw"
PROCESSED_DIR="/data/processed"

# Create log entry
log_message() {
    echo "$(date): $1" >> "$LOG_DIR/pipeline.log"
}

# Process CSV files
process_files() {
    for file in "$DATA_DIR"/*.csv; do
        if [ -f "$file" ]; then
            filename=$(basename "$file")
            log_message "Processing $filename"
            
            # Run Python processing script
            python3 /scripts/process_data.py "$file" "$PROCESSED_DIR/$filename"
            
            if [ $? -eq 0 ]; then
                log_message "Successfully processed $filename"
                mv "$file" "$DATA_DIR/archive/"
            else
                log_message "Error processing $filename"
            fi
        fi
    done
}

process_files

Phase 2: Big Data and Advanced Tools (Days 14-23)

Big Data and Hadoop (Days 14-16)

  • Day 14: Big Data concepts, Hadoop, HDFS, MapReduce
  • Day 15: Hadoop ecosystem (Pig, Hive, HBase), SQL queries in Big Data
  • Day 16: Data modeling, Data Warehouse, normalization/denormalization

Data Pipelines with Apache Airflow (Days 17-20)

  • Day 17: Pipeline introduction, Apache Airflow, DAGs
  • Day 18: Connections, variables, operators, database integration
  • Day 19: Practice: DAG for daily Python script execution
  • Day 20: Project: Simple ETL flow with Airflow
# Example: Airflow DAG for ETL pipeline
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'data-team',
    'depends_on_past': False,
    'start_date': datetime(2024, 1, 1),
    'email_on_failure': True,
    'email_on_retry': False,
    'retries': 2,
    'retry_delay': timedelta(minutes=5)
}

def extract_data(**context):
    """Extract data from source"""
    # Implementation here
    pass

def transform_data(**context):
    """Transform extracted data"""
    # Implementation here
    pass

def load_data(**context):
    """Load data to destination"""
    # Implementation here
    pass

dag = DAG(
    'daily_etl_pipeline',
    default_args=default_args,
    description='Daily ETL pipeline',
    schedule_interval='@daily',
    catchup=False
)

extract_task = PythonOperator(
    task_id='extract_data',
    python_callable=extract_data,
    dag=dag
)

transform_task = PythonOperator(
    task_id='transform_data',
    python_callable=transform_data,
    dag=dag
)

load_task = PythonOperator(
    task_id='load_data',
    python_callable=load_data,
    dag=dag
)

# Set task dependencies
extract_task >> transform_task >> load_task

Apache Spark (Days 21-23)

  • Day 21: Spark architecture, installation, RDDs, DataFrames
  • Day 22: DataFrame operations, Spark SQL
  • Day 23: Practice: CSV file processing with Spark
# Example: Spark DataFrame operations
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, avg, when, isnan, count

# Initialize Spark Session
spark = SparkSession.builder \
    .appName("DataProcessing") \
    .config("spark.sql.adaptive.enabled", "true") \
    .getOrCreate()

# Read data
df = spark.read.option("header", "true").csv("path/to/data.csv")

# Data quality checks
def data_quality_report(df):
    """Generate data quality report"""
    total_rows = df.count()
    
    quality_report = df.select([
        count(when(isnan(c) | col(c).isNull(), c)).alias(c + "_nulls")
        for c in df.columns
    ]).collect()[0].asDict()
    
    return {
        'total_rows': total_rows,
        'null_counts': quality_report
    }

# Transformations
processed_df = df \
    .filter(col("amount") > 0) \
    .withColumn("amount_category", 
                when(col("amount") < 100, "low")
                .when(col("amount") < 1000, "medium")
                .otherwise("high")) \
    .groupBy("category", "amount_category") \
    .agg(
        sum("amount").alias("total_amount"),
        avg("amount").alias("avg_amount"),
        count("*").alias("transaction_count")
    )

# Write results
processed_df.write \
    .mode("overwrite") \
    .parquet("path/to/output")

Phase 3: Intermediate Projects (Days 24-32)

Specialized Practical Projects

Day 24: Advanced SQL Project - Complex database design with multiple related tables - Implementation of complex queries with subqueries and CTEs - Query optimization with indexes and execution plan analysis - Creation of views, stored procedures, and triggers

Day 25: Enterprise ETL Pipeline Objective: Create pipeline that extracts sales data from multiple sources - Sources: CSV files, relational databases, REST APIs - Transformations: Cleaning, standardization, data enrichment - Destination: Data Warehouse (Google BigQuery/Amazon Redshift) - Technologies: Python, pandas, SQL, cloud storage - Deliverables: Process documentation, versioned code, quality reports

Day 26: Weather Data Project Use case: Real-time climate analysis system - Extraction: Weather API (OpenWeatherMap/Weather API) - Processing: Apache Spark for distributed transformations - Storage: SQL database optimized for time series - Features: Missing data handling, anomaly detection, temporal aggregations - Deliverables: Visualization dashboard, automatic alerts

Day 27: Big Data Processing Challenge: Process CSV file >1GB with Apache Spark - Dataset: Financial transaction data or server logs - Operations: Complex aggregations, multi-criteria filtering - Optimization: Partitioning, caching, Spark configuration - Storage: Data Warehouse with star schema - Metrics: Processing time, memory usage, throughput

Day 28: Enterprise Report Automation Complete system: Automated daily sales reports - Source: SQL database with transactional data - Processing: Spark for aggregations and complex calculations - Orchestration: Apache Airflow for automatic scheduling - Outputs: PDF reports, web dashboards, email notifications - Features: Error handling, retries, detailed logs

Days 29-32: Development and Refinement - Day 29: Integration of projects into personal portfolio - Day 30: Performance and scalability optimization - Day 31: Implementation of unit and integration tests - Day 32: Technical documentation and project presentations

Phase 4: Advanced Technologies (Days 33-49)

Distributed Systems and NoSQL (Days 33-36)

  • Day 33: Distributed computing, distributed system architectures
  • Day 34: NoSQL databases (MongoDB, Cassandra), Parquet/ORC formats
  • Day 35: Practice: Migration of SQL project to NoSQL
  • Day 36: Practice: Parquet format storage with Spark

Data Governance and Quality (Days 37-40)

  • Day 37: Introduction to Data Governance, frameworks and responsibilities
  • Day 38: Data Governance Framework
  • Day 39: Data quality, Data Profiling, quality dimensions
  • Day 40: Data Catalog, metadata management, taxonomies

Web Scraping (Days 41-49)

Fundamentals and Tools (Days 41-44) - Days 41-42: HTML/CSS, BeautifulSoup, Scrapy, form and session handling - Day 43: Data cleaning, cookie and session management, JavaScript handling - Day 44: Intelligent crawling, detection evasion, legal and ethical considerations

Practical Web Scraping Projects (Days 45-49)

Day 45: E-commerce Price Monitor Project: Product price monitoring system - Objective: Extract prices from multiple e-commerce sites - Technologies: Scrapy, requests, BeautifulSoup, pandas - Features: - Handle different HTML structures - User agent and proxy rotation - Price history storage - Change detection and alerts - Deliverables: Price database, trend dashboard

# Example: Price monitoring scraper
import scrapy
from scrapy.http import Request
import pandas as pd
from datetime import datetime

class PriceSpider(scrapy.Spider):
    name = 'price_monitor'
    
    def __init__(self, products_file=None):
        self.products = pd.read_csv(products_file)
        
    def start_requests(self):
        for _, product in self.products.iterrows():
            yield Request(
                url=product['url'],
                callback=self.parse_price,
                meta={'product_id': product['id'], 'name': product['name']}
            )
    
    def parse_price(self, response):
        # Extract price using CSS selectors
        price_text = response.css('.price::text').get()
        if price_text:
            price = float(price_text.replace('$', '').replace(',', ''))
            
            yield {
                'product_id': response.meta['product_id'],
                'product_name': response.meta['name'],
                'price': price,
                'url': response.url,
                'scraped_at': datetime.now(),
                'availability': response.css('.availability::text').get()
            }

Day 46: News Aggregator System Project: Automatic news aggregator - Objective: Monitor multiple news sites and extract headlines - Functionalities: - Extract headlines, dates, categories - Automatic topic classification - Duplicate news detection - RSS feed integration - Technologies: Scrapy spiders, basic NLP, cron scheduling - Deliverables: News API, trend dashboard

Days 47-49: Advanced Projects - Day 47: Social Media Analytics: Public social media data scraper - Day 48: Real Estate Monitor: Property monitoring system - Day 49: Job Market Analysis: Job posting extraction for market analysis

Phase 5: Cloud Computing (Days 50-57)

Cloud Platforms (Days 50-57)

Services covered: - AWS: S3, Lambda, Redshift, Kinesis, EMR, Glue, RDS - Google Cloud: BigQuery, Dataflow, Dataproc, Cloud Storage, Pub/Sub
- Azure: Data Lake, SQL Database, Stream Analytics, HDInsight

Objectives: - Design scalable infrastructures - Distributed storage - Real-time and batch processing

# Example: AWS S3 data processing with Lambda
import boto3
import pandas as pd
from io import StringIO
import json

def lambda_handler(event, context):
    """Process CSV files uploaded to S3"""
    
    s3_client = boto3.client('s3')
    
    # Get bucket and key from event
    bucket = event['Records'][0]['s3']['bucket']['name']
    key = event['Records'][0]['s3']['object']['key']
    
    try:
        # Read CSV from S3
        response = s3_client.get_object(Bucket=bucket, Key=key)
        csv_content = response['Body'].read().decode('utf-8')
        
        # Process with pandas
        df = pd.read_csv(StringIO(csv_content))
        
        # Data transformations
        processed_df = df.groupby('category').agg({
            'amount': ['sum', 'mean', 'count'],
            'date': ['min', 'max']
        }).round(2)
        
        # Save processed data
        output_key = f"processed/{key.replace('.csv', '_processed.csv')}"
        s3_client.put_object(
            Bucket=bucket,
            Key=output_key,
            Body=processed_df.to_csv(index=False)
        )
        
        return {
            'statusCode': 200,
            'body': json.dumps(f'Successfully processed {key}')
        }
        
    except Exception as e:
        return {
            'statusCode': 500,
            'body': json.dumps(f'Error processing {key}: {str(e)}')
        }

Data Streaming - Specialized Projects (Days 58-71)

Streaming Fundamentals (Days 58-63) Tools: Apache Kafka, Apache Flink, Apache Storm, AWS Kinesis, Google Dataflow

Key concepts: - Real-time vs batch data processing - Event-driven architectures and microservices - Windowing and temporal aggregations - Exactly-once processing and fault tolerance

Days 64-67: Project 1 - Real-time Log Analysis System Use case: Enterprise web application monitoring

Day 64: Architecture and Setup - Apache Kafka configuration as message broker - Apache Flink setup for stream processing - Elasticsearch configuration for storage - Pipeline design: Logs → Kafka → Flink → Elasticsearch

Day 65: Ingestion and Processing - Implementation of producers for sending logs to Kafka - Development of Flink jobs for: - Log filtering by severity - Time window aggregations - Anomaly pattern detection - Data enrichment

# Example: Kafka producer for log streaming
from kafka import KafkaProducer
import json
import logging
from datetime import datetime

class LogProducer:
    def __init__(self, bootstrap_servers=['localhost:9092']):
        self.producer = KafkaProducer(
            bootstrap_servers=bootstrap_servers,
            value_serializer=lambda v: json.dumps(v).encode('utf-8'),
            key_serializer=lambda k: k.encode('utf-8') if k else None
        )
    
    def send_log(self, log_level, message, service_name, user_id=None):
        """Send log message to Kafka topic"""
        log_entry = {
            'timestamp': datetime.utcnow().isoformat(),
            'level': log_level,
            'message': message,
            'service': service_name,
            'user_id': user_id
        }
        
        topic = f"logs-{log_level.lower()}"
        key = service_name
        
        try:
            self.producer.send(topic, value=log_entry, key=key)
            self.producer.flush()
        except Exception as e:
            logging.error(f"Failed to send log: {e}")

Day 66: Alerts and Monitoring - Real-time alert system for critical errors - Real-time dashboard with Grafana - Performance metrics implementation - Automatic notification configuration

Day 67: Optimization and Testing - Load testing with massive log generation - Throughput and latency optimization - Backpressure handling implementation - System documentation

Days 68-71: Project 2 - Real-time Trading Platform Use case: Financial analysis system for algorithmic trading

Day 68: Financial Data Ingestion - Integration with financial data APIs (Alpha Vantage, Yahoo Finance) - Kafka configuration for price streaming - Schema registry implementation for data evolution - Multiple topics setup per financial instrument

Day 69: Trading Signal Processing - Real-time technical indicators implementation: - Moving averages (SMA, EMA) - RSI, MACD, Bollinger Bands - Trading pattern detection - Real-time risk calculation

Day 70: Alert and Execution System - Buy/sell signal generation - Push alert system for opportunities - Order execution simulation - Portfolio performance tracking

Day 71: Dashboard and Analysis - Interactive dashboard with real-time visualizations - Trading system performance metrics - Latency and throughput analysis - Simulated profitability reports

Technologies used in both projects: - Message Brokers: Apache Kafka, AWS Kinesis - Stream Processing: Apache Flink, Apache Storm - Storage: Elasticsearch, InfluxDB, AWS S3 - Visualization: Grafana, custom dashboards - Monitoring: Prometheus, custom metrics

Phase 7: Final Project (Days 72-100)

Capstone Project - Complete ETL Pipeline (Days 72-100)

Objective: Develop an end-to-end data pipeline that solves a real business problem.

Strategic Planning (Days 72-75)

Day 72: Problem Identification and Objective Definition - Select a real use case (sales optimization, log analysis, real-time processing) - Define SMART project objectives - Identify success metrics and KPIs - Document the business problem to solve

Day 73: Pipeline Architecture Design - Create high-level architecture diagram - Define data flow: sources → transformation → storage → consumption - Select technology stack (Python, Spark, Airflow, Cloud) - Design data schema and storage structure

Day 74: Data Source Definition - Identify and catalog all data sources - Evaluate APIs, databases, CSV files, logs - Search for public datasets or generate simulated data if necessary - Document structure and format of each source

Day 75: Infrastructure Configuration - Cloud: Configure AWS/GCP/Azure (S3/GCS, EMR/Dataproc, Redshift/BigQuery) - Local: Configure virtual environment with Python, Spark, Airflow - Establish connections between services - Configure security and permissions

Environment Setup (Days 76-79)

Day 76: Orchestration Configuration - Install and configure Apache Airflow - Create base DAG for task sequence - Configure connections and environment variables - Establish execution scheduling

Day 77: Database Configuration - Configure SQL/NoSQL databases for storage - Create necessary schemas and tables - Configure indexes for optimization - Establish backup and recovery policies

Day 78: Version Control and Documentation - Configure Git repository with proper structure - Create .gitignore and configuration files - Establish code and documentation conventions - Configure basic CI/CD

Day 79: Monitoring Configuration - Implement detailed logging for each stage - Configure monitoring tools (Prometheus, Grafana) - Establish alerts and notifications - Create pipeline monitoring dashboards

ETL Development (Days 80-90)

Days 80-82: Data Extraction (Extract) - Day 80: Python extraction script development - Day 81: Real-time API consumption implementation - Day 82: Initial cleaning and validation of extracted data

Days 83-87: Data Transformation (Transform) - Day 83: Transformation rules definition (aggregations, filtering, normalization) - Day 84: Spark/pandas transformation development - Day 85: Data quality checks implementation - Day 86: Transformation testing and optimization - Day 87: Validation with large datasets

Days 88-90: Data Loading (Load) - Day 88: Data Warehouse loading process configuration - Day 89: Complete ETL pipeline integration - Day 90: Airflow automation and execution scheduling

Optimization and Finalization (Days 91-100)

Days 91-93: Optimization and Security - Day 91: SQL query and Spark process optimization - Day 92: Security implementation (encryption, RBAC) - Day 93: Scalability and performance testing

Days 94-96: Documentation and Visualization - Day 94: Complete pipeline and process documentation - Day 95: Dashboard creation with Tableau/Power BI - Day 96: Final results report preparation

Days 97-100: Review and Presentation - Day 97: Complete review and error correction - Day 98: End-to-end testing - Day 99: Final presentation preparation - Day 100: Project presentation and final delivery

Technologies and Tools Covered

Programming Languages

  • SQL - Queries, optimization, database management
  • Python - Data manipulation, automation, APIs
  • Bash - Automation, system administration

Big Data Tools

  • Apache Hadoop - HDFS, MapReduce, complete ecosystem
  • Apache Spark - Distributed processing, DataFrames, SQL
  • Apache Airflow - Pipeline orchestration, DAGs

Databases

  • Relational - MySQL, PostgreSQL, optimization
  • NoSQL - MongoDB, Cassandra
  • Data Warehouses - BigQuery, Redshift

Cloud Platforms

  • AWS - S3, EMR, Redshift, Kinesis, Lambda, Glue
  • Google Cloud - BigQuery, Dataflow, Dataproc
  • Microsoft Azure - Data Lake, Stream Analytics

Streaming and Real-time

  • Apache Kafka - Message streaming
  • Apache Flink - Stream processing
  • AWS Kinesis - Real-time data streaming

Learning Methodology

Daily Structure

  1. Theory - Fundamental concepts
  2. Guided Practice - Structured exercises
  3. Projects - Practical application
  4. Review - Knowledge consolidation

Progressive Approach

  • Weeks 1-2: Solid fundamentals
  • Weeks 3-7: Intermediate tools
  • Weeks 8-11: Advanced technologies
  • Weeks 12-14: Independent project

Key Components

  • 40% Theory - Concepts and best practices
  • 35% Practice - Exercises and labs
  • 25% Projects - Real application

Expected Results

Upon completing the 100 days, you will have:

Technical Skills

  • SQL mastery for complex data analysis
  • Python competency for data engineering
  • Practical experience with Big Data tools
  • Knowledge of scalable cloud architectures
  • Ability to design robust ETL pipelines

Portfolio Projects

Upon completing the program, you will have a robust portfolio with diverse projects:

Fundamental Projects

  • Enterprise ETL Pipeline: Complete system for extracting, transforming, and loading sales data
  • Advanced SQL Project: Optimized database with complex queries and stored procedures
  • Climate Monitoring System: API integration with distributed Spark processing

Big Data Projects

  • Massive File Processor: Handling datasets >1GB with Spark optimizations
  • Automated Reporting System: Airflow orchestration for enterprise reports
  • SQL to NoSQL Migration: Performance comparison between relational and NoSQL databases

Web Scraping Projects

  • E-commerce Price Monitor: Price monitoring system with automatic alerts
  • News Aggregator: News aggregation platform with automatic classification
  • Real Estate Analytics: Real estate market analysis with extracted data

Cloud Projects

  • Multi-Cloud Architecture: Implementation on AWS, GCP, and Azure with service comparison
  • Data Lake Implementation: Distributed storage with serverless processing
  • Streaming Analytics: Real-time processing with cloud-native services

Streaming Projects

  • Log Analysis System: Real-time monitoring of enterprise applications
  • Trading Platform: Financial analysis with real-time technical indicators
  • IoT Data Pipeline: Sensor data processing with Apache Kafka and Flink

Capstone Project

  • End-to-End ETL Pipeline: Complete solution integrating all learned technologies
  • Professional Documentation: Architecture, code, tests, and performance metrics
  • Executive Presentation: Demonstration of business value and project ROI

Professional Preparation

  • Knowledge of industry best practices
  • Experience with standard enterprise tools
  • Ability to solve real data problems
  • Solid foundation for Data Engineer roles

Success Recommendations

Consistency

  • Dedicate daily time to the program
  • Maintain a sustainable pace
  • Don’t skip practice sessions

Active Practice

  • Implement all exercises
  • Experiment beyond requirements
  • Document your progress

Community

  • Share your projects
  • Seek feedback from other professionals
  • Participate in Data Engineering communities

Adaptation

  • Adjust pace according to your availability
  • Deepen areas of greater interest
  • Maintain focus on practical objectives

Total Duration: 100 days
Level: Beginner to Intermediate-Advanced
Mode: Self-study with practical projects
Result: Complete preparation for Data Engineer roles