Building Social Media Data Pipelines: Architecture Guide

Collecting social media data is step one. Building a reliable pipeline to process, store, and analyze that data is where the real value comes from.

This guide covers architecture patterns for social media data pipelines. Need a reliable data source? Our social media scraper provides the extraction layer for your pipeline.

Pipeline Architecture Overview

┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Source    │────▶│   Ingest    │────▶│  Transform  │────▶│    Store    │
│ (API/Scrape)│     │   (Queue)   │     │   (ETL)     │     │ (Database)  │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘
                                                                   │
                                                                   ▼
                    ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
                    │   Alert     │◀────│   Analyze   │◀────│    Serve    │
                    │   System    │     │   (ML/BI)   │     │    (API)    │
                    └─────────────┘     └─────────────┘     └─────────────┘

Component Deep Dive

1. Data Ingestion

Simple: Direct API Calls

// Simple ingestion with SociaVault
async function ingestProfiles(usernames) {
  const results = [];
  
  for (const username of usernames) {
    const profile = await sociavault.instagram.profile({ username });
    results.push({
      ...profile,
      collected_at: new Date().toISOString()
    });
    
    // Rate limiting
    await sleep(200);
  }
  
  return results;
}

Scalable: Message Queue

// Producer: Queue collection jobs
const { Queue } = require('bullmq');

const collectionQueue = new Queue('social-collection', {
  connection: { host: 'localhost', port: 6379 }
});

async function queueCollectionJobs(usernames, platform) {
  const jobs = usernames.map(username => ({
    name: 'collect-profile',
    data: { username, platform },
    opts: {
      attempts: 3,
      backoff: { type: 'exponential', delay: 1000 }
    }
  }));
  
  await collectionQueue.addBulk(jobs);
}

// Consumer: Process jobs
const { Worker } = require('bullmq');

const worker = new Worker('social-collection', async (job) => {
  const { username, platform } = job.data;
  
  const profile = await sociavault[platform].profile({ username });
  
  // Store in database
  await db.profiles.upsert({
    where: { platform_username: { platform, username } },
    update: profile,
    create: { platform, username, ...profile }
  });
  
  return profile;
}, {
  connection: { host: 'localhost', port: 6379 },
  concurrency: 10
});

2. Data Transformation

Cleaning and Normalization

function normalizeProfile(rawProfile, platform) {
  // Different platforms have different field names
  const normalizers = {
    instagram: (p) => ({
      platform: 'instagram',
      username: p.username,
      displayName: p.full_name,
      bio: p.biography,
      followers: p.follower_count,
      following: p.following_count,
      postsCount: p.media_count,
      isVerified: p.is_verified,
      profileUrl: `https://instagram.com/${p.username}`,
      profilePicture: p.profile_pic_url
    }),
    
    tiktok: (p) => ({
      platform: 'tiktok',
      username: p.uniqueId,
      displayName: p.nickname,
      bio: p.signature,
      followers: p.followerCount,
      following: p.followingCount,
      postsCount: p.videoCount,
      isVerified: p.verified,
      profileUrl: `https://tiktok.com/@${p.uniqueId}`,
      profilePicture: p.avatarLarger
    }),
    
    twitter: (p) => ({
      platform: 'twitter',
      username: p.screen_name,
      displayName: p.name,
      bio: p.description,
      followers: p.followers_count,
      following: p.friends_count,
      postsCount: p.statuses_count,
      isVerified: p.verified,
      profileUrl: `https://twitter.com/${p.screen_name}`,
      profilePicture: p.profile_image_url_https
    })
  };
  
  const normalizer = normalizers[platform];
  if (!normalizer) {
    throw new Error(`Unknown platform: ${platform}`);
  }
  
  return normalizer(rawProfile);
}

Enrichment

async function enrichProfile(normalizedProfile) {
  return {
    ...normalizedProfile,
    
    // Calculate engagement rate
    engagementRate: await calculateEngagementRate(normalizedProfile),
    
    // Detect category/niche
    category: await detectCategory(normalizedProfile.bio),
    
    // Sentiment of recent posts
    contentSentiment: await analyzeRecentContent(normalizedProfile),
    
    // Growth metrics (if historical data exists)
    growth: await calculateGrowth(normalizedProfile),
    
    // Data quality score
    dataQuality: assessDataQuality(normalizedProfile),
    
    // Enrichment timestamp
    enrichedAt: new Date().toISOString()
  };
}

async function calculateEngagementRate(profile) {
  if (!profile.followers || profile.followers === 0) return 0;
  
  const posts = await getRecentPosts(profile.platform, profile.username, 12);
  
  if (posts.length === 0) return 0;
  
  const avgEngagement = posts.reduce((sum, post) => 
    sum + post.likes + post.comments, 0
  ) / posts.length;
  
  return (avgEngagement / profile.followers) * 100;
}

3. Data Storage

Schema Design

-- Profiles table (normalized across platforms)
CREATE TABLE profiles (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  platform VARCHAR(50) NOT NULL,
  username VARCHAR(255) NOT NULL,
  display_name VARCHAR(255),
  bio TEXT,
  followers INTEGER,
  following INTEGER,
  posts_count INTEGER,
  is_verified BOOLEAN DEFAULT false,
  profile_url TEXT,
  profile_picture TEXT,
  category VARCHAR(100),
  engagement_rate DECIMAL(5,2),
  created_at TIMESTAMP DEFAULT NOW(),
  updated_at TIMESTAMP DEFAULT NOW(),
  
  UNIQUE(platform, username)
);

-- Historical metrics for tracking changes
CREATE TABLE profile_metrics_history (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  profile_id UUID REFERENCES profiles(id),
  followers INTEGER,
  following INTEGER,
  posts_count INTEGER,
  engagement_rate DECIMAL(5,2),
  recorded_at TIMESTAMP DEFAULT NOW()
);

-- Posts table
CREATE TABLE posts (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  profile_id UUID REFERENCES profiles(id),
  platform_post_id VARCHAR(255),
  content TEXT,
  likes INTEGER,
  comments INTEGER,
  shares INTEGER,
  views INTEGER,
  post_type VARCHAR(50),
  posted_at TIMESTAMP,
  collected_at TIMESTAMP DEFAULT NOW(),
  
  UNIQUE(profile_id, platform_post_id)
);

-- Indexes for common queries
CREATE INDEX idx_profiles_platform ON profiles(platform);
CREATE INDEX idx_profiles_category ON profiles(category);
CREATE INDEX idx_profiles_followers ON profiles(followers DESC);
CREATE INDEX idx_posts_profile ON posts(profile_id);
CREATE INDEX idx_posts_posted_at ON posts(posted_at DESC);

Time-Series Optimization

-- Use TimescaleDB for time-series data
CREATE EXTENSION IF NOT EXISTS timescaledb;

-- Convert metrics history to hypertable
SELECT create_hypertable(
  'profile_metrics_history',
  'recorded_at',
  chunk_time_interval => INTERVAL '1 day'
);

-- Compression policy (compress data older than 7 days)
ALTER TABLE profile_metrics_history SET (
  timescaledb.compress,
  timescaledb.compress_orderby = 'recorded_at DESC'
);

SELECT add_compression_policy(
  'profile_metrics_history',
  INTERVAL '7 days'
);

-- Retention policy (keep only 1 year of history)
SELECT add_retention_policy(
  'profile_metrics_history',
  INTERVAL '1 year'
);

4. Scheduling and Orchestration

Cron-Based (Simple)

const cron = require('node-cron');

// Daily full refresh
cron.schedule('0 2 * * *', async () => {
  console.log('Starting daily profile refresh...');
  
  const profiles = await db.profiles.findAll();
  
  for (const profile of profiles) {
    await queueCollectionJobs([profile.username], profile.platform);
  }
});

// Hourly high-priority refresh
cron.schedule('0 * * * *', async () => {
  const highPriority = await db.profiles.findAll({
    where: { priority: 'high' }
  });
  
  for (const profile of highPriority) {
    await queueCollectionJobs([profile.username], profile.platform);
  }
});

Workflow Orchestration (Apache Airflow)

from airflow import DAG
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

default_args = {
    'owner': 'data-team',
    'depends_on_past': False,
    'email_on_failure': True,
    'retries': 3,
    'retry_delay': timedelta(minutes=5)
}

dag = DAG(
    'social_media_pipeline',
    default_args=default_args,
    schedule_interval='@daily',
    start_date=datetime(2025, 1, 1),
    catchup=False
)

collect_profiles = PythonOperator(
    task_id='collect_profiles',
    python_callable=collect_all_profiles,
    dag=dag
)

collect_posts = PythonOperator(
    task_id='collect_posts',
    python_callable=collect_recent_posts,
    dag=dag
)

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

generate_reports = PythonOperator(
    task_id='generate_reports',
    python_callable=generate_daily_reports,
    dag=dag
)

# Define dependencies
collect_profiles >> collect_posts >> transform_data >> generate_reports

5. Real-Time Processing

For real-time use cases (alerts, dashboards):

// Kafka consumer for real-time processing
const { Kafka } = require('kafkajs');

const kafka = new Kafka({
  clientId: 'social-processor',
  brokers: ['localhost:9092']
});

const consumer = kafka.consumer({ groupId: 'social-group' });

async function runConsumer() {
  await consumer.connect();
  await consumer.subscribe({ topic: 'social-data', fromBeginning: false });
  
  await consumer.run({
    eachMessage: async ({ topic, partition, message }) => {
      const data = JSON.parse(message.value.toString());
      
      // Process in real-time
      await processRealTime(data);
      
      // Check for alerts
      await checkAlerts(data);
      
      // Update real-time dashboard
      await updateDashboard(data);
    }
  });
}

async function checkAlerts(data) {
  // Viral content alert
  if (data.type === 'post' && data.likes > 10000) {
    await sendAlert('viral-content', data);
  }
  
  // Sentiment alert
  if (data.sentiment < -0.5) {
    await sendAlert(&apos;negative-sentiment&apos;, data);
  }
  
  // Follower spike
  if (data.type === &apos;profile&apos;) {
    const previous = await getPreviousMetrics(data.username);
    if (previous && data.followers > previous.followers * 1.1) {
      await sendAlert(&apos;follower-spike&apos;, data);
    }
  }
}

Complete Pipeline Example

Here's a production-ready pipeline using SociaVault:

// pipeline.js
const { Queue, Worker } = require('bullmq');
const { PrismaClient } = require('@prisma/client');
const cron = require('node-cron');

const prisma = new PrismaClient();
const SOCIAVAULT_API_KEY = process.env.SOCIAVAULT_API_KEY;

// ============ COLLECTION ============

async function collectProfile(platform, username) {
  const response = await fetch(
    `https://api.sociavault.com/${platform}/profile`,
    {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${SOCIAVAULT_API_KEY}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({ username })
    }
  );
  
  return response.json();
}

// ============ TRANSFORMATION ============

function transformProfile(raw, platform) {
  return {
    platform,
    username: raw.username,
    displayName: raw.full_name || raw.nickname || raw.name,
    bio: raw.biography || raw.signature || raw.description,
    followers: raw.follower_count || raw.followerCount,
    following: raw.following_count || raw.followingCount,
    postsCount: raw.media_count || raw.videoCount,
    engagementRate: calculateEngagement(raw),
    collectedAt: new Date()
  };
}

function calculateEngagement(profile) {
  // Simplified - in production, fetch posts
  const followers = profile.follower_count || profile.followerCount || 0;
  if (followers === 0) return 0;
  return Math.min(10, Math.random() * 5); // Placeholder
}

// ============ STORAGE ============

async function storeProfile(data) {
  // Upsert profile
  const profile = await prisma.profile.upsert({
    where: {
      platform_username: {
        platform: data.platform,
        username: data.username
      }
    },
    update: {
      displayName: data.displayName,
      bio: data.bio,
      followers: data.followers,
      following: data.following,
      postsCount: data.postsCount,
      engagementRate: data.engagementRate,
      updatedAt: new Date()
    },
    create: data
  });
  
  // Store historical metrics
  await prisma.metricsHistory.create({
    data: {
      profileId: profile.id,
      followers: data.followers,
      following: data.following,
      postsCount: data.postsCount,
      engagementRate: data.engagementRate
    }
  });
  
  return profile;
}

// ============ WORKER ============

const collectionQueue = new Queue('collection');

const worker = new Worker('collection', async (job) => {
  const { platform, username } = job.data;
  
  // Collect
  const raw = await collectProfile(platform, username);
  
  if (raw.error) {
    throw new Error(raw.error);
  }
  
  // Transform
  const transformed = transformProfile(raw, platform);
  
  // Store
  const stored = await storeProfile(transformed);
  
  console.log(`Processed: ${platform}/${username}`);
  
  return stored;
}, {
  concurrency: 5,
  limiter: {
    max: 10,
    duration: 1000 // 10 requests per second
  }
});

// ============ SCHEDULER ============

// Daily refresh of all profiles
cron.schedule('0 3 * * *', async () => {
  const profiles = await prisma.profile.findMany();
  
  const jobs = profiles.map(p => ({
    name: 'collect',
    data: { platform: p.platform, username: p.username }
  }));
  
  await collectionQueue.addBulk(jobs);
  
  console.log(`Queued ${jobs.length} collection jobs`);
});

// ============ API ============

const express = require('express');
const app = express();

app.get('/api/profiles', async (req, res) => {
  const { platform, category, minFollowers } = req.query;
  
  const profiles = await prisma.profile.findMany({
    where: {
      ...(platform && { platform }),
      ...(category && { category }),
      ...(minFollowers && { followers: { gte: parseInt(minFollowers) } })
    },
    orderBy: { followers: 'desc' },
    take: 100
  });
  
  res.json(profiles);
});

app.get('/api/profiles/:id/history', async (req, res) => {
  const history = await prisma.metricsHistory.findMany({
    where: { profileId: req.params.id },
    orderBy: { recordedAt: 'desc' },
    take: 30
  });
  
  res.json(history);
});

app.listen(3000, () => console.log('Pipeline API running on :3000'));

Infrastructure Recommendations

Small Scale (< 10K profiles)

• Database: PostgreSQL
• Queue: Redis + BullMQ
• Hosting: Single VPS (4GB RAM)
• Cost: ~$50/month

Medium Scale (10K-100K profiles)

• Database: PostgreSQL with read replicas
• Queue: Redis cluster
• Hosting: 2-3 VPS or managed services
• Cost: ~$200-500/month

Large Scale (100K+ profiles)

• Database: PostgreSQL + TimescaleDB (or ClickHouse)
• Queue: Kafka
• Orchestration: Airflow or Prefect
• Hosting: Kubernetes cluster
• Cost: ~$1,000-5,000/month

Get Started

1. Sign up for SociaVault - 50 free credits
2. Choose your stack - Start simple, scale as needed
3. Build incrementally - Collection → Storage → Transformation → Analysis
4. Monitor everything - Log errors, track metrics, alert on failures

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