harvard-art-museums-data-pipeline

name	harvard-art-museums-data-pipeline
description	Build end-to-end data engineering pipelines with the Harvard Art Museums API, ETL processes, SQL analytics, and Streamlit visualization
triggers	["build a data pipeline for museum artifacts","create ETL workflow for Harvard Art Museums API","set up artifact data collection and analytics","implement museum data engineering pipeline","build streamlit dashboard for art museum data","create SQL analytics for artifact collections","extract and analyze Harvard museum artifacts","design data warehouse for art collections"]

Harvard Art Museums Data Pipeline

Skill by ara.so — Data Skills collection.

This skill enables you to build production-ready data engineering pipelines using the Harvard Art Museums API. It covers ETL workflows, relational database design, SQL analytics, and interactive Streamlit dashboards for artifact data visualization.

What This Project Does

The Harvard-Artifacts-Collection-Data-Engineering-Analytics-App demonstrates a complete data pipeline:

1. Extract: Fetches artifact data from Harvard Art Museums API with pagination and rate limiting
2. Transform: Converts nested JSON into normalized relational tables
3. Load: Batch inserts data into MySQL/TiDB Cloud databases
4. Analyze: Executes analytical SQL queries for insights
5. Visualize: Renders interactive dashboards with Plotly and Streamlit

The architecture follows: API → ETL → SQL → Analytics → Visualization

Installation

git clone https://github.com/Manali0711/Harvard-Artifacts-Collection-Data-Engineering-Analytics-App.git
cd Harvard-Artifacts-Collection-Data-Engineering-Analytics-App

# Install dependencies
pip install -r requirements.txt

Dependencies

streamlit
pandas
requests
mysql-connector-python
plotly
python-dotenv

Configuration

Environment Variables

Create a .env file in the project root:

# Harvard Art Museums API
HARVARD_API_KEY=your_api_key_here

# Database Configuration
DB_HOST=your_database_host
DB_PORT=3306
DB_USER=your_db_user
DB_PASSWORD=your_db_password
DB_NAME=harvard_artifacts

Get Harvard API Key

1. Visit Harvard Art Museums API
2. Register for a free API key
3. Add to .env file

Database Setup

import mysql.connector
from dotenv import load_dotenv
import os

load_dotenv()

# Database connection
def get_db_connection():
    return mysql.connector.connect(
        host=os.getenv('DB_HOST'),
        port=int(os.getenv('DB_PORT', 3306)),
        user=os.getenv('DB_USER'),
        password=os.getenv('DB_PASSWORD'),
        database=os.getenv('DB_NAME')
    )

# Create tables
def setup_database():
    conn = get_db_connection()
    cursor = conn.cursor()
    
    # Artifact metadata table
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS artifactmetadata (
            id INT PRIMARY KEY,
            title VARCHAR(500),
            culture VARCHAR(255),
            century VARCHAR(100),
            classification VARCHAR(255),
            department VARCHAR(255),
            technique VARCHAR(255),
            medium VARCHAR(500),
            dated VARCHAR(255),
            url TEXT,
            totalpageviews INT,
            totaluniquepageviews INT
        )
    """)
    
    # Artifact media table
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS artifactmedia (
            id INT AUTO_INCREMENT PRIMARY KEY,
            artifact_id INT,
            iiifbaseuri VARCHAR(500),
            baseimageurl TEXT,
            primaryimageurl TEXT,
            FOREIGN KEY (artifact_id) REFERENCES artifactmetadata(id)
        )
    """)
    
    # Artifact colors table
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS artifactcolors (
            id INT AUTO_INCREMENT PRIMARY KEY,
            artifact_id INT,
            color VARCHAR(50),
            spectrum VARCHAR(50),
            hue VARCHAR(50),
            percent DECIMAL(5,2),
            FOREIGN KEY (artifact_id) REFERENCES artifactmetadata(id)
        )
    """)
    
    conn.commit()
    cursor.close()
    conn.close()

API Integration

Basic API Request

import requests
import os
from dotenv import load_dotenv

load_dotenv()

def fetch_artifacts(page=1, size=100):
    """Fetch artifacts from Harvard Art Museums API"""
    api_key = os.getenv('HARVARD_API_KEY')
    base_url = "https://api.harvardartmuseums.org/object"
    
    params = {
        'apikey': api_key,
        'page': page,
        'size': size,
        'hasimage': 1  # Only artifacts with images
    }
    
    response = requests.get(base_url, params=params)
    response.raise_for_status()
    return response.json()

Paginated Data Collection

def collect_all_artifacts(max_records=1000):
    """Collect artifacts with pagination handling"""
    all_artifacts = []
    page = 1
    size = 100
    
    while len(all_artifacts) < max_records:
        try:
            data = fetch_artifacts(page=page, size=size)
            records = data.get('records', [])
            
            if not records:
                break
                
            all_artifacts.extend(records)
            
            # Check if more pages available
            if data['info']['next'] is None:
                break
                
            page += 1
            
            # Rate limiting
            import time
            time.sleep(0.5)
            
        except Exception as e:
            print(f"Error fetching page {page}: {e}")
            break
    
    return all_artifacts[:max_records]

ETL Pipeline

Extract and Transform

import pandas as pd

def transform_artifact_metadata(artifacts):
    """Transform artifacts into metadata DataFrame"""
    metadata = []
    
    for artifact in artifacts:
        metadata.append({
            'id': artifact.get('id'),
            'title': artifact.get('title', 'Unknown')[:500],
            'culture': artifact.get('culture', 'Unknown')[:255],
            'century': artifact.get('century', 'Unknown')[:100],
            'classification': artifact.get('classification', 'Unknown')[:255],
            'department': artifact.get('department', 'Unknown')[:255],
            'technique': artifact.get('technique', 'Unknown')[:255],
            'medium': artifact.get('medium', 'Unknown')[:500],
            'dated': artifact.get('dated', 'Unknown')[:255],
            'url': artifact.get('url', ''),
            'totalpageviews': artifact.get('totalpageviews', 0),
            : artifact.get(, )
        })
    
     pd.DataFrame(metadata)

 ():
    
    media = []
    
     artifact  artifacts:
        artifact_id = artifact.get()
        images = artifact.get(, [])
        
         images:
            primary_image = images[]
            media.append({
                : artifact_id,
                : primary_image.get(, ),
                : primary_image.get(, ),
                : artifact.get(, )
            })
    
     pd.DataFrame(media)

 ():
    
    colors = []
    
     artifact  artifacts:
        artifact_id = artifact.get()
        color_list = artifact.get(, [])
        
         color  color_list:
            colors.append({
                : artifact_id,
                : color.get(, ),
                : color.get(, ),
                : color.get(, ),
                : color.get(, )
            })
    
     pd.DataFrame(colors)

Load into Database

def load_metadata(df, conn):
    """Batch insert metadata into database"""
    cursor = conn.cursor()
    
    insert_query = """
        INSERT INTO artifactmetadata 
        (id, title, culture, century, classification, department, 
         technique, medium, dated, url, totalpageviews, totaluniquepageviews)
        VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
        ON DUPLICATE KEY UPDATE
        title=VALUES(title), culture=VALUES(culture)
    """
    
    data = [tuple(row) for row in df.values]
    cursor.executemany(insert_query, data)
    conn.commit()
    cursor.close()

def run_etl_pipeline(max_records=1000):
    """Execute complete ETL pipeline"""
    # Extract
    print("Extracting artifacts from API...")
    artifacts = collect_all_artifacts(max_records)
    
    # Transform
    print("Transforming data...")
    df_metadata = transform_artifact_metadata(artifacts)
    df_media = transform_artifact_media(artifacts)
    df_colors = transform_artifact_colors(artifacts)
    
    # Load
    print("Loading into database...")
    conn = get_db_connection()
    load_metadata(df_metadata, conn)
    load_metadata(df_media, conn)  # Similar function for media
    load_metadata(df_colors, conn)  # Similar function for colors
    conn.close()
    
    print(f"ETL complete: {len(artifacts)} artifacts processed")

SQL Analytics

Sample Analytical Queries

# Query 1: Artifacts by culture
QUERY_BY_CULTURE = """
    SELECT culture, COUNT(*) as count
    FROM artifactmetadata
    WHERE culture != 'Unknown'
    GROUP BY culture
    ORDER BY count DESC
    LIMIT 20
"""

# Query 2: Most viewed artifacts
QUERY_TOP_VIEWED = """
    SELECT title, culture, totalpageviews
    FROM artifactmetadata
    ORDER BY totalpageviews DESC
    LIMIT 10
"""

# Query 3: Artifacts by century
QUERY_BY_CENTURY = """
    SELECT century, COUNT(*) as count
    FROM artifactmetadata
    WHERE century != 'Unknown'
    GROUP BY century
    ORDER BY count DESC
"""

# Query 4: Color distribution
QUERY_COLOR_DISTRIBUTION = """
    SELECT color, COUNT(*) as count, AVG(percent) as avg_percent
    FROM artifactcolors
    GROUP BY color
    ORDER BY count DESC
    LIMIT 15
"""

# Query 5: Department statistics
QUERY_DEPARTMENT_STATS = """
    SELECT 
        department,
        COUNT(*) as total_artifacts,
        AVG(totalpageviews) as avg_views
    FROM artifactmetadata
    WHERE department != 'Unknown'
    GROUP BY department
    ORDER BY total_artifacts DESC
"""

def execute_query(query):
    """Execute SQL query and return DataFrame"""
    conn = get_db_connection()
    df = pd.read_sql(query, conn)
    conn.close()
    return df

Streamlit Dashboard

Main Application

import streamlit as st
import plotly.express as px

def main():
    st.set_page_config(
        page_title="Harvard Artifacts Analytics",
        page_icon="🏛️",
        layout="wide"
    )
    
    st.title("🏛️ Harvard Art Museums Analytics Dashboard")
    
    # Sidebar
    st.sidebar.header("Navigation")
    page = st.sidebar.selectbox(
        "Choose a page",
        ["Data Collection", "SQL Analytics", "Visualizations"]
    )
    
    if page == "Data Collection":
        show_data_collection()
    elif page == "SQL Analytics":
        show_sql_analytics()
    elif page == "Visualizations":
        show_visualizations()

def show_data_collection():
    st.header("📥 Data Collection")
    
    num_records = st.number_input(
        "Number of records to collect",
        min_value=100,
        max_value=10000,
        value=1000,
        step=100
    )
    
    if st.button("Start ETL Pipeline"):
        with st.spinner("Running ETL pipeline..."):
            run_etl_pipeline(num_records)
        st.success(f"Successfully collected {num_records} artifacts!")

def show_sql_analytics():
    st.header("📊 SQL Analytics")
    
    queries = {
        "Artifacts by Culture": QUERY_BY_CULTURE,
        "Most Viewed Artifacts": QUERY_TOP_VIEWED,
        : QUERY_BY_CENTURY,
        : QUERY_COLOR_DISTRIBUTION,
        : QUERY_DEPARTMENT_STATS
    }
    
    selected_query = st.selectbox(, (queries.keys()))
    
     st.button():
        df = execute_query(queries[selected_query])
        st.dataframe(df, use_container_width=)
        
        
         (df.columns) >= :
            fig = px.bar(
                df, 
                x=df.columns[], 
                y=df.columns[],
                title=selected_query
            )
            st.plotly_chart(fig, use_container_width=)

 __name__ == :
    main()

Run the Dashboard

streamlit run app.py

Common Patterns

Rate Limiting API Requests

import time
from functools import wraps

def rate_limit(calls_per_second=2):
    """Decorator to rate limit API calls"""
    min_interval = 1.0 / calls_per_second
    last_called = [0.0]
    
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            elapsed = time.time() - last_called[0]
            left_to_wait = min_interval - elapsed
            if left_to_wait > 0:
                time.sleep(left_to_wait)
            ret = func(*args, **kwargs)
            last_called[0] = time.time()
            return ret
        return wrapper
    return decorator

@rate_limit(calls_per_second=2)
def fetch_artifact_by_id(artifact_id):
    """Fetch single artifact with rate limiting"""
    api_key = os.getenv('HARVARD_API_KEY')
    url = f"https://api.harvardartmuseums.org/object/{artifact_id}"
    response = requests.get(url, params={'apikey': api_key})
    return response.json()

Error Handling and Retries

from requests.exceptions import RequestException

def fetch_with_retry(url, params, max_retries=3):
    """Fetch with exponential backoff retry"""
    for attempt in range(max_retries):
        try:
            response = requests.get(url, params=params, timeout=10)
            response.raise_for_status()
            return response.json()
        except RequestException as e:
            if attempt == max_retries - 1:
                raise
            wait_time = 2 ** attempt
            print(f"Retry {attempt + 1}/{max_retries} after {wait_time}s")
            time.sleep(wait_time)

Troubleshooting

API Key Issues

# Verify API key is loaded
import os
from dotenv import load_dotenv

load_dotenv()
api_key = os.getenv('HARVARD_API_KEY')

if not api_key:
    raise ValueError("HARVARD_API_KEY not found in environment variables")

# Test API connection
response = requests.get(
    "https://api.harvardartmuseums.org/object",
    params={'apikey': api_key, 'size': 1}
)
print(f"API Status: {response.status_code}")

Database Connection Errors

def test_database_connection():
    """Test database connectivity"""
    try:
        conn = get_db_connection()
        cursor = conn.cursor()
        cursor.execute("SELECT 1")
        result = cursor.fetchone()
        cursor.close()
        conn.close()
        print("Database connection successful")
        return True
    except Exception as e:
        print(f"Database connection failed: {e}")
        return False

Memory Issues with Large Datasets

def batch_process_artifacts(total_records, batch_size=100):
    """Process artifacts in batches to manage memory"""
    num_batches = (total_records + batch_size - 1) // batch_size
    
    for batch_num in range(num_batches):
        page = batch_num + 1
        artifacts = fetch_artifacts(page=page, size=batch_size)
        
        # Process batch
        df = transform_artifact_metadata(artifacts['records'])
        conn = get_db_connection()
        load_metadata(df, conn)
        conn.close()
        
        print(f"Processed batch {batch_num + 1}/{num_batches}")

Streamlit Caching for Performance

@st.cache_data(ttl=3600)
def cached_query_execution(query):
    """Cache query results for 1 hour"""
    return execute_query(query)

# Use in dashboard
df = cached_query_execution(QUERY_BY_CULTURE)
st.dataframe(df)