harvard-artifacts-etl-streamlit-analytics

name	harvard-artifacts-etl-streamlit-analytics
description	Build ETL pipelines and analytics dashboards using Harvard Art Museums API data with Python, SQL, and Streamlit
triggers	["how do I build an ETL pipeline with Harvard Art Museums API","create a Streamlit dashboard for Harvard artifacts data","set up SQL database for Harvard museum collections","extract and transform Harvard Art Museums API data","build analytics app with museum artifact data","query and visualize Harvard museum collection data","implement data pipeline for art museum API","create artifact analytics with Python and SQL"]

Harvard Artifacts ETL & Analytics Skill

Skill by ara.so — Data Skills collection.

This skill enables AI coding agents to help developers build end-to-end data engineering and analytics applications using the Harvard Art Museums API. The project demonstrates ETL pipelines, SQL database design, analytical queries, and interactive Streamlit visualizations for museum artifact data.

What This Project Does

The Harvard Artifacts Collection Data Engineering & Analytics App provides:

• API Integration: Fetch artifact data from Harvard Art Museums API with pagination and rate limiting
• ETL Pipeline: Extract, transform, and load artifact metadata, media, and color data into relational SQL tables
• SQL Analytics: Pre-built analytical queries for insights on culture, century, media availability, and color patterns
• Interactive Dashboard: Streamlit-based UI with Plotly visualizations for query results

Installation

Prerequisites

• Python 3.8+
• MySQL or TiDB Cloud database
• Harvard Art Museums API key (get from https://docs.harvardartmuseums.org/api-docs/)

Setup Steps

# Clone the repository
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

# Set up environment variables
export HARVARD_API_KEY="your_api_key_here"
export DB_HOST="your_db_host"
export DB_USER="your_db_user"
export DB_PASSWORD="your_db_password"
export DB_NAME="harvard_artifacts"

# Run the Streamlit app
streamlit run app.py

Dependencies (requirements.txt)

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

Configuration

Database Connection

import mysql.connector
import os

def get_db_connection():
    """Create database connection using environment variables"""
    return mysql.connector.connect(
        host=os.getenv('DB_HOST', 'localhost'),
        user=os.getenv('DB_USER', 'root'),
        password=os.getenv('DB_PASSWORD'),
        database=os.getenv('DB_NAME', 'harvard_artifacts'),
        port=int(os.getenv('DB_PORT', 3306))
    )

API Configuration

import os

API_KEY = os.getenv('HARVARD_API_KEY')
BASE_URL = "https://api.harvardartmuseums.org/object"

Database Schema

Create Tables

-- Artifact Metadata Table
CREATE TABLE artifactmetadata (
    id INT PRIMARY KEY,
    title VARCHAR(500),
    culture VARCHAR(255),
    century VARCHAR(100),
    classification VARCHAR(255),
    division VARCHAR(255),
    department VARCHAR(255),
    dated VARCHAR(255),
    accessionyear INT,
    url VARCHAR(500)
);

-- Artifact Media Table
CREATE TABLE artifactmedia (
    media_id INT AUTO_INCREMENT PRIMARY KEY,
    artifact_id INT,
    baseimageurl VARCHAR(500),
    iiifbaseuri VARCHAR(500),
    primaryimageurl VARCHAR(500),
    FOREIGN KEY (artifact_id) REFERENCES artifactmetadata(id)
);

-- Artifact Colors Table
CREATE TABLE artifactcolors (
    color_id INT AUTO_INCREMENT PRIMARY KEY,
    artifact_id INT,
    color VARCHAR(50),
    spectrum VARCHAR(50),
    hue VARCHAR(50),
    percent FLOAT,
    FOREIGN KEY (artifact_id) REFERENCES artifactmetadata(id)
);

ETL Pipeline Implementation

Extract: Fetch Data from API

import requests
import time

def fetch_artifacts(api_key, num_pages=5, page_size=100):
    """
    Fetch artifact data from Harvard Art Museums API with pagination
    
    Args:
        api_key: Harvard API key
        num_pages: Number of pages to fetch
        page_size: Records per page (max 100)
    
    Returns:
        List of artifact records
    """
    artifacts = []
    
    for page in range(1, num_pages + 1):
        params = {
            'apikey': api_key,
            'size': page_size,
            'page': page
        }
        
        response = requests.get(BASE_URL, params=params)
        
        if response.status_code == 200:
            data = response.json()
            artifacts.extend(data.get('records', []))
            print(f"Fetched page {page}/{num_pages}")
            
            # Rate limiting: API allows 2500 requests/day
            time.sleep(0.5)
        else:
            print(f"Error fetching page {page}: {response.status_code}")
            break
    
    return artifacts

Transform: Clean and Structure Data

import pandas as pd

def transform_artifacts(raw_data):
    """
    Transform raw API data into structured dataframes
    
    Returns:
        Tuple of (metadata_df, media_df, colors_df)
    """
    metadata_list = []
    media_list = []
    colors_list = []
    
    for artifact in raw_data:
        # Extract metadata
        metadata = {
            'id': artifact.get('id'),
            'title': artifact.get('title', '')[:500],
            'culture': artifact.get('culture', '')[:255],
            'century': artifact.get('century', '')[:100],
            'classification': artifact.get('classification', '')[:255],
            'division': artifact.get('division', '')[:255],
            'department': artifact.get('department', '')[:255],
            'dated': artifact.get('dated', '')[:255],
            'accessionyear': artifact.get('accessionyear'),
            'url': artifact.get('url', '')[:500]
        }
        metadata_list.append(metadata)
        
        # Extract media information
         artifact.get()  artifact.get():
            media = {
                : artifact.get(),
                : artifact.get(, )[:],
                : artifact.get(, )[:],
                : artifact.get(, )[:]
            }
            media_list.append(media)
        
        
         color_obj  artifact.get(, []):
            color = {
                : artifact.get(),
                : color_obj.get(, )[:],
                : color_obj.get(, )[:],
                : color_obj.get(, )[:],
                : color_obj.get()
            }
            colors_list.append(color)
    
     (
        pd.DataFrame(metadata_list),
        pd.DataFrame(media_list),
        pd.DataFrame(colors_list)
    )

Load: Insert into SQL Database

def load_to_database(metadata_df, media_df, colors_df, connection):
    """
    Load transformed data into SQL database using batch inserts
    """
    cursor = connection.cursor()
    
    # Insert metadata
    metadata_query = """
        INSERT INTO artifactmetadata 
        (id, title, culture, century, classification, division, department, dated, accessionyear, url)
        VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
        ON DUPLICATE KEY UPDATE title=VALUES(title)
    """
    cursor.executemany(metadata_query, metadata_df.values.tolist())
    
    # Insert media
    media_query = """
        INSERT INTO artifactmedia (artifact_id, baseimageurl, iiifbaseuri, primaryimageurl)
        VALUES (%s, %s, %s, %s)
    """
    cursor.executemany(media_query, media_df.values.tolist())
    
    # Insert colors
    colors_query = """
        INSERT INTO artifactcolors (artifact_id, color, spectrum, hue, percent)
        VALUES (%s, %s, %s, %s, %s)
    """
    cursor.executemany(colors_query, colors_df.values.tolist())
    
    connection.commit()
    cursor.close()
    print(f"Loaded {len(metadata_df)} artifacts, {len(media_df)} media records, {len(colors_df)} color records")

Analytics Queries

Sample Analytical Queries

ANALYTICS_QUERIES = {
    "Artifacts by Culture": """
        SELECT culture, COUNT(*) as count
        FROM artifactmetadata
        WHERE culture IS NOT NULL AND culture != ''
        GROUP BY culture
        ORDER BY count DESC
        LIMIT 20
    """,
    
    "Artifacts by Century": """
        SELECT century, COUNT(*) as count
        FROM artifactmetadata
        WHERE century IS NOT NULL AND century != ''
        GROUP BY century
        ORDER BY count DESC
        LIMIT 15
    """,
    
    "Media Availability": """
        SELECT 
            COUNT(DISTINCT m.artifact_id) as with_media,
            (SELECT COUNT(*) FROM artifactmetadata) as total_artifacts,
            ROUND(COUNT(DISTINCT m.artifact_id) * 100.0 / 
                  (SELECT COUNT(*) FROM artifactmetadata), 2) as percentage
        FROM artifactmedia m
    """,
    
    "Top Colors Used": """
        SELECT color, COUNT(*) as count, AVG(percent) as avg_percent
        FROM artifactcolors
        WHERE color IS NOT NULL
        GROUP BY color
        ORDER BY count DESC
        LIMIT 20
    """,
    
    "Department Distribution": """
        SELECT department, COUNT(*) as count
        FROM artifactmetadata
        WHERE department IS NOT NULL AND department != ''
        GROUP BY department
        ORDER BY count DESC
    """,
    
    "Artifacts with Images by Century": """
        SELECT am.century, COUNT(DISTINCT am.id) as artifact_count
        FROM artifactmetadata am
        JOIN artifactmedia media ON am.id = media.artifact_id
        WHERE am.century IS NOT NULL AND media.primaryimageurl IS NOT NULL
        GROUP BY am.century
        ORDER BY artifact_count DESC
        LIMIT 15
    """
}

def execute_query(query, connection):
    """Execute SQL query and return DataFrame"""
    return pd.read_sql(query, connection)

Streamlit Dashboard Implementation

Basic App Structure

import streamlit as st
import plotly.express as px

def main():
    st.title("🏛️ Harvard Art Museums Analytics Dashboard")
    
    # Sidebar for navigation
    page = st.sidebar.selectbox(
        "Select Page",
        ["Data Collection", "Analytics", "Visualizations"]
    )
    
    if page == "Data Collection":
        show_data_collection_page()
    elif page == "Analytics":
        show_analytics_page()
    else:
        show_visualizations_page()

def show_data_collection_page():
    """Page for ETL operations"""
    st.header("Data Collection & ETL")
    
    api_key = st.text_input("Harvard API Key", type="password", 
                            value=os.getenv('HARVARD_API_KEY', ''))
    num_pages = st.slider("Number of pages to fetch", 1, 10, 5)
    
    if st.button("Run ETL Pipeline"):
        with st.spinner("Fetching data from API..."):
            raw_data = fetch_artifacts(api_key, num_pages)
            st.success(f"Fetched {len(raw_data)} artifacts")
        
        with st.spinner("Transforming data..."):
            metadata_df, media_df, colors_df = transform_artifacts(raw_data)
            st.success("Data transformed")
        
        with st.spinner():
            conn = get_db_connection()
            load_to_database(metadata_df, media_df, colors_df, conn)
            conn.close()
            st.success()

 ():
    
    st.header()
    
    query_name = st.selectbox(, (ANALYTICS_QUERIES.keys()))
    
     st.button():
        conn = get_db_connection()
        df = execute_query(ANALYTICS_QUERIES[query_name], conn)
        conn.close()
        
        st.dataframe(df)
        
        
         (df.columns) ==     df.columns..lower():
            fig = px.bar(df, x=df.columns[], y=, 
                        title=query_name)
            st.plotly_chart(fig)

 __name__ == :
    main()

Common Patterns

Pattern: Incremental Data Loading

def get_last_artifact_id(connection):
    """Get the highest artifact ID already in database"""
    cursor = connection.cursor()
    cursor.execute("SELECT MAX(id) FROM artifactmetadata")
    result = cursor.fetchone()
    cursor.close()
    return result[0] if result[0] else 0

def fetch_new_artifacts_only(api_key, last_id):
    """Fetch only artifacts newer than last_id"""
    params = {
        'apikey': api_key,
        'size': 100,
        'sort': 'id',
        'sortorder': 'asc',
        'q': f'id:>{last_id}'
    }
    response = requests.get(BASE_URL, params=params)
    return response.json().get('records', [])

Pattern: Data Validation

def validate_artifact_data(df):
    """Validate artifact metadata before loading"""
    required_cols = ['id', 'title']
    
    # Check required columns exist
    missing = [col for col in required_cols if col not in df.columns]
    if missing:
        raise ValueError(f"Missing required columns: {missing}")
    
    # Check for null IDs
    if df['id'].isnull().any():
        raise ValueError("Found null artifact IDs")
    
    # Check for duplicates
    duplicates = df[df.duplicated(subset=['id'], keep=False)]
    if not duplicates.empty:
        st.warning(f"Found {len(duplicates)} duplicate artifact IDs")
    
    return True

Troubleshooting

API Rate Limiting

# Problem: HTTP 429 errors from API
# Solution: Add exponential backoff

import time
from requests.exceptions import RequestException

def fetch_with_retry(url, params, max_retries=3):
    """Fetch with exponential backoff on rate limit"""
    for attempt in range(max_retries):
        try:
            response = requests.get(url, params=params)
            if response.status_code == 429:
                wait_time = 2 ** attempt
                print(f"Rate limited. Waiting {wait_time}s...")
                time.sleep(wait_time)
                continue
            return response
        except RequestException as e:
            if attempt == max_retries - 1:
                raise
            time.sleep(2 ** attempt)
    return None

Database Connection Issues

# Problem: Lost database connections
# Solution: Use connection pooling

from mysql.connector import pooling

db_pool = pooling.MySQLConnectionPool(
    pool_name="harvard_pool",
    pool_size=5,
    host=os.getenv('DB_HOST'),
    user=os.getenv('DB_USER'),
    password=os.getenv('DB_PASSWORD'),
    database=os.getenv('DB_NAME')
)

def get_pooled_connection():
    """Get connection from pool"""
    return db_pool.get_connection()

Memory Issues with Large Datasets

# Problem: Out of memory with large API responses
# Solution: Process in chunks

def load_artifacts_in_chunks(metadata_df, chunk_size=1000):
    """Load data in chunks to avoid memory issues"""
    conn = get_db_connection()
    
    for start_idx in range(0, len(metadata_df), chunk_size):
        end_idx = min(start_idx + chunk_size, len(metadata_df))
        chunk = metadata_df.iloc[start_idx:end_idx]
        
        cursor = conn.cursor()
        cursor.executemany(metadata_query, chunk.values.tolist())
        conn.commit()
        cursor.close()
        
        print(f"Loaded chunk {start_idx}-{end_idx}")
    
    conn.close()

Handling Missing Data

# Problem: NULL values breaking queries
# Solution: Use COALESCE and proper NULL handling

def clean_dataframe(df):
    """Clean dataframe before loading"""
    # Replace empty strings with None for SQL NULL
    df = df.replace('', None)
    
    # Fill NaN values appropriately
    if 'accessionyear' in df.columns:
        df['accessionyear'] = df['accessionyear'].fillna(0).astype(int)
    
    # Truncate long strings
    for col in df.select_dtypes(include=['object']).columns:
        df[col] = df[col].astype(str).str[:500]
    
    return df

This skill provides comprehensive guidance for building ETL pipelines and analytics dashboards with the Harvard Art Museums API, enabling AI agents to assist developers in data engineering projects.