Scale your data science skills to handle massive datasets with modern big data tools
Imagine trying to analyze every tweet ever posted, or Netflix's viewing history for 200 million users! Traditional tools can't handle this scale. Big Data is about processing datasets so large that they don't fit on a single computer - we're talking terabytes to petabytes!
Volume (Size)
Massive amounts of data - terabytes, petabytes, or more. Think Netflix's 200+ million users watching billions of hours of content!
Velocity (Speed)
Data arrives fast and needs processing in real-time. Twitter processes 500 million tweets per day - that's 6,000 tweets per second!
Variety (Types)
Different data formats - structured (databases), semi-structured (JSON, XML), unstructured (text, images, videos).
Distributed Processing
Split work across hundreds of computers
Fault Tolerance
Automatically handle hardware failures
Scalability
Add more machines to handle more data
Cost Effective
Use commodity hardware instead of expensive servers
Apache Spark is the Ferrari of big data processing - fast, powerful, and elegant! It can process data 100x faster than Hadoop MapReduce by keeping data in memory. Used by Netflix, Uber, Airbnb, and thousands of companies.
# Install PySpark
# pip install pyspark
# Create Spark session
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("MyApp") \
.getOrCreate()
# Read data
df = spark.read.csv("data.csv", header=True, inferSchema=True)
df.show(5)
df.printSchema()
# DataFrame operations
df.select("name", "age").show()
df.filter(df["age"] > 30).show()
df.groupBy("department").count().show()
# SQL queries
df.createOrReplaceTempView("employees")
result = spark.sql("SELECT * FROM employees WHERE age > 30")
result.show()
A data warehouse is like a massive, organized library for your company's data! It stores historical data from multiple sources, optimized for analysis and reporting. Think of it as the single source of truth for business intelligence.
Store measurable events (transactions, sales, clicks). Contains metrics and foreign keys to dimensions.
Example: Sales Fact
Store descriptive attributes (who, what, where, when). Provide context for facts.
Example: Customer Dimension
The most common data warehouse design! One central fact table surrounded by dimension tables, forming a star shape. Simple, fast queries, easy to understand.
Example Structure:
• Center: Sales_Fact (order_id, customer_id, product_id, date_id, amount)
• Points: Customer_Dim, Product_Dim, Date_Dim, Store_Dim
ETL (Extract, Transform, Load) is the process of moving data from source systems to a data warehouse. Think of it as a data assembly line - raw materials (source data) go in, finished products (clean, structured data) come out!
1. Extract
Pull data from various sources (databases, APIs, files, logs). Handle different formats and connection types.
2. Transform
Clean, validate, and reshape data. Remove duplicates, handle nulls, apply business rules, aggregate, join tables.
3. Load
Write transformed data to target system (data warehouse, data lake). Can be full load or incremental.
# Simple Airflow DAG (Directed Acyclic Graph)
from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime
# Define tasks
def extract_data():
print("Extracting data from source...")
def transform_data():
print("Transforming data...")
def load_data():
print("Loading data to warehouse...")
# Create DAG
dag = DAG(
'etl_pipeline',
start_date=datetime(2024, 1, 1),
schedule_interval='@daily'
)
# Define task dependencies
extract = PythonOperator(task_id='extract', python_callable=extract_data, dag=dag)
transform = PythonOperator(task_id='transform', python_callable=transform_data, dag=dag)
load = PythonOperator(task_id='load', python_callable=load_data, dag=dag)
extract >> transform >> load # Set execution order
The cloud has revolutionized big data! Instead of buying expensive hardware, you rent computing power and storage on-demand. Scale up for Black Friday, scale down on slow days. Pay only for what you use!
The market leader with the most comprehensive set of services. If you can imagine it, AWS probably has it!
• S3 (Simple Storage Service)
Object storage for data lakes. Store petabytes of data cheaply. Think of it as infinite hard drive space!
• Redshift
Data warehouse for analytics. Fast SQL queries on petabyte-scale data. Columnar storage, massively parallel.
• EMR (Elastic MapReduce)
Managed Hadoop and Spark clusters. Spin up 100 nodes, run your job, shut down. Pay by the hour!
• Glue
Serverless ETL service. Automatically discovers schema, generates ETL code, runs jobs.
• BigQuery
Serverless data warehouse. Query terabytes in seconds! No infrastructure management. Pay per query.
• Dataflow
Managed Apache Beam for stream and batch processing. Auto-scaling, no ops required.
• Cloud Storage
Object storage like S3. Multiple storage classes for different access patterns.
• Synapse Analytics
Unified analytics platform. Combines data warehousing, big data, and data integration.
• Data Factory
Cloud ETL service. Visual interface for building data pipelines. 90+ connectors.
• Blob Storage
Object storage for unstructured data. Hot, cool, and archive tiers for cost optimization.
Start with AWS (most popular) or GCP (best for data/ML). The concepts transfer! Once you know one cloud, learning others is easy. Focus on understanding distributed computing concepts, not memorizing service names.
Batch processing is like doing laundry once a week. Stream processing is like having a washing machine that cleans clothes as soon as they're dirty! Real-time processing analyzes data as it arrives - crucial for fraud detection, recommendations, monitoring.
Batch Processing
• Process large volumes at once
• Run on schedule (hourly, daily)
• Higher latency, higher throughput
• Example: Daily sales reports
Stream Processing
• Process events as they arrive
• Continuous, real-time
• Low latency, lower throughput
• Example: Fraud detection
Let's build a complete big data pipeline! We'll process large-scale e-commerce data using PySpark, store it in a data warehouse structure, and create analytics-ready datasets.
# Step 1: Initialize Spark
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, avg, count, when
from pyspark.sql.window import Window
spark = SparkSession.builder \
.appName("EcommercePipeline") \
.config("spark.sql.adaptive.enabled", "true") \
.getOrCreate()
# Step 2: Extract - Read from multiple sources
orders_df = spark.read.parquet("s3://data-lake/orders/")
customers_df = spark.read.json("s3://data-lake/customers/")
products_df = spark.read.csv("s3://data-lake/products/", header=True)
# Step 3: Transform - Clean and enrich data
# Remove duplicates
orders_clean = orders_df.dropDuplicates(["order_id"])
# Handle nulls
orders_clean = orders_clean.fillna({"discount": 0, "notes": ""})
# Add calculated columns
orders_enriched = orders_clean.withColumn(
"total_with_tax",
col("total_amount") * 1.1
)
# Step 4: Join tables (create fact table)
fact_sales = orders_enriched \
.join(customers_df, "customer_id", "left") \
.join(products_df, "product_id", "left") \
.select(
"order_id",
"customer_id",
"product_id",
"order_date",
"quantity",
"total_amount",
"total_with_tax"
)
# Step 5: Aggregate analytics
customer_metrics = fact_sales.groupBy("customer_id") \
.agg(
count("order_id").alias("order_count"),
sum("total_amount").alias("lifetime_value"),
avg("total_amount").alias("avg_order_value")
)
# Step 6: Window functions for rankings
window_spec = Window.partitionBy("category").orderBy(col("revenue").desc())
product_rankings = products_df \
.withColumn("rank", row_number().over(window_spec)) \
.filter(col("rank") <= 10)
# Step 7: Load - Write to data warehouse
# Partition by date for efficient queries
fact_sales.write \
.mode("overwrite") \
.partitionBy("order_date") \
.parquet("s3://warehouse/fact_sales/")
customer_metrics.write \
.mode("overwrite") \
.parquet("s3://warehouse/customer_metrics/")
# Step 8: Data quality checks
print(f"Total orders processed: {fact_sales.count()}")
print(f"Null check: {fact_sales.filter(col('customer_id').isNull()).count()}")
# Stop Spark session
spark.stop()
You've completed the Data Science learning path! You now have the skills to work with data at any scale - from Pandas on your laptop to Spark on cloud clusters processing petabytes. You've learned Python, statistics, machine learning, SQL, and big data tools. You're ready to tackle real-world data science challenges!
Module 1: Python for Data Science
NumPy, Pandas, data cleaning
Module 2: Statistics & Probability
Hypothesis testing, distributions, A/B testing
Module 3: Data Visualization
Matplotlib, Seaborn, storytelling with data
Module 4: Machine Learning
Supervised/unsupervised learning, model evaluation
Module 5: Advanced ML
XGBoost, feature engineering, hyperparameter tuning
Module 6: SQL & Databases
JOINs, aggregations, window functions
Module 7: Big Data & Tools
Spark, data warehousing, ETL, cloud platforms