Your social media dashboard updates every 15 minutes. A customer complains on Twitter at 2:15 PM. Your dashboard doesn't show it until 2:30 PM.

By then: The tweet has 500 retweets. It's trending. Your competitor already responded. You look slow and out of touch.

The problem: Batch processing is too slow for scenarios where seconds matter: crisis detection, trading signals, breaking news, live event monitoring.

The solution: Real-time stream processing that analyzes social data the moment it's created, triggering instant alerts and updating live dashboards with zero delay.

This guide shows you how to build production-grade real-time social media processing with Kafka, WebSockets, and stream processors.

Batch vs Real-Time: When Does Speed Matter?

Batch Processing (Good Enough for Most Use Cases)

How it works: Collect data every X minutes/hours, process in bulk
Latency: 5-60 minutes
Cost: Low (simple architecture)

When to use:

Historical analysis and reporting
Daily/weekly trend reports
Non-urgent monitoring
Cost-sensitive projects

Example: "Generate weekly engagement report"

Real-Time Processing (Critical for Time-Sensitive Use Cases)

How it works: Process data instantly as it arrives
Latency: < 1 second
Cost: Higher (complex infrastructure)

When to use:

Crisis detection and alerts
Live event monitoring
Trading/market signals
Real-time dashboards
Competitive intelligence

Example: "Alert me within 10 seconds if brand mentions spike"

Real-World Use Cases for Real-Time Processing

Use Case 1: Brand Crisis Detection

Scenario: United Airlines passenger removal incident

Batch processing (15-minute updates):

2:00 PM: Video posted → Not detected
2:15 PM: First batch → 50 mentions detected, no alert (normal volume)
2:30 PM: Second batch → 2,500 mentions detected, ALERT SENT
Response time: 30 minutes after incident

Real-time processing:

2:00:10 PM: Video posted → Detected in 10 seconds
2:00:45 PM: Mentions doubling every minute → ALERT SENT
2:01:00 PM: PR team reviewing situation
Response time: 1 minute after incident

Impact: 29-minute advantage = Respond before it goes viral

Use Case 2: Live Event Monitoring

Scenario: Super Bowl ad campaign tracking

Requirements:

Track brand mentions during 30-second ad
Compare engagement vs competitors in real-time
Adjust social media response based on sentiment

Batch processing: Can't provide insights fast enough
Real-time processing: Second-by-second tracking during ad

Use Case 3: Stock Trading Signals

Scenario: Social sentiment predicts stock movement

Research shows: Twitter sentiment about stocks predicts price changes 2-6 hours in advance

Batch processing: By the time you get data, opportunity is gone
Real-time processing: Trade based on live sentiment shifts

Use Case 4: Influencer Partnership Opportunities

Scenario: Influencer mentions your brand organically

Real-time detection:

Influencer posts about your product
System detects mention within seconds
Team reaches out while post is trending
Result: Convert organic mention into partnership

Batch processing: Discover mention 6 hours later, engagement already dropped

Architecture: Building a Real-Time Processing System

Component 1: Data Ingestion (Webhooks + API Polling)

Real-time systems need constant data flow:

// webhook-receiver.js
import express from 'express';
import { Kafka } from 'kafkajs';

const app = express();
const kafka = new Kafka({
  clientId: 'webhook-receiver',
  brokers: ['localhost:9092']
});

const producer = kafka.producer();
await producer.connect();

// Receive webhooks from SociaVault
app.post('/webhook/social-mention', async (req, res) => {
  const mention = req.body;
  
  // Push to Kafka immediately
  await producer.send({
    topic: 'social-mentions',
    messages: [{
      key: mention.id,
      value: JSON.stringify({
        platform: mention.platform,
        author: mention.author,
        text: mention.text,
        timestamp: mention.created_at,
        engagement: {
          likes: mention.likes,
          comments: mention.comments,
          shares: mention.shares
        }
      })
    }]
  });
  
  res.status(200).json({ received: true });
});

// Fallback: Poll for data every 10 seconds (for platforms without webhooks)
setInterval(async () => {
  const mentions = await fetchRecentMentions(); // Last 10 seconds
  
  for (const mention of mentions) {
    await producer.send({
      topic: 'social-mentions',
      messages: [{ key: mention.id, value: JSON.stringify(mention) }]
    });
  }
}, 10000);

app.listen(3000, () => {
  console.log('Webhook receiver listening on port 3000');
});

Why Kafka?

Handles millions of messages per second
Durable (messages aren't lost if consumer crashes)
Multiple consumers can read same stream
Scales horizontally

Component 2: Stream Processing (Real-Time Analytics)

Process data as it flows through Kafka:

// stream-processor.js
import { Kafka } from 'kafkajs';

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

const consumer = kafka.consumer({ groupId: 'analytics-group' });
await consumer.connect();
await consumer.subscribe({ topic: 'social-mentions', fromBeginning: false });

// In-memory state (last 5 minutes)
const recentMentions = [];
const windowSize = 5 * 60 * 1000; // 5 minutes

await consumer.run({
  eachMessage: async ({ topic, partition, message }) => {
    const mention = JSON.parse(message.value.toString());
    const now = Date.now();
    
    // Add to window
    recentMentions.push({
      ...mention,
      processedAt: now
    });
    
    // Remove mentions outside 5-minute window
    const cutoff = now - windowSize;
    while (recentMentions.length > 0 && recentMentions[0].processedAt < cutoff) {
      recentMentions.shift();
    }
    
    // Real-time analytics
    const metrics = calculateMetrics(recentMentions);
    
    // Check for anomalies
    if (metrics.volumeSpike > 3) {
      await sendAlert({
        type: 'VOLUME_SPIKE',
        severity: 'HIGH',
        message: `Mention volume is ${metrics.volumeSpike}x normal`,
        currentVolume: metrics.currentVolume,
        baseline: metrics.baseline
      });
    }
    
    if (metrics.sentimentDrop < -0.3) {
      await sendAlert({
        type: 'SENTIMENT_DROP',
        severity: 'CRITICAL',
        message: `Sentiment dropped by ${Math.abs(metrics.sentimentDrop)} points`,
        currentSentiment: metrics.currentSentiment,
        baseline: metrics.baselineSentiment
      });
    }
    
    // Publish metrics to dashboard
    await publishMetrics(metrics);
  }
});

function calculateMetrics(mentions) {
  const fiveMinutesAgo = Date.now() - windowSize;
  
  // Volume metrics
  const currentVolume = mentions.length;
  const baseline = 50; // Calculated from historical data
  const volumeSpike = currentVolume / baseline;
  
  // Sentiment metrics
  const sentiments = mentions.map(m => analyzeSentiment(m.text));
  const currentSentiment = sentiments.reduce((a, b) => a + b, 0) / sentiments.length;
  const baselineSentiment = 0.6; // Historical average
  const sentimentDrop = currentSentiment - baselineSentiment;
  
  // Engagement metrics
  const totalEngagement = mentions.reduce((sum, m) => 
    sum + m.engagement.likes + m.engagement.comments + m.engagement.shares, 0
  );
  const avgEngagement = totalEngagement / mentions.length;
  
  return {
    currentVolume,
    baseline,
    volumeSpike,
    currentSentiment,
    baselineSentiment,
    sentimentDrop,
    avgEngagement,
    topMentions: mentions
      .sort((a, b) => 
        (b.engagement.likes + b.engagement.comments + b.engagement.shares) -
        (a.engagement.likes + a.engagement.comments + a.engagement.shares)
      )
      .slice(0, 5)
  };
}

How it works:

Consume messages from Kafka as they arrive
Maintain sliding 5-minute window in memory
Calculate real-time metrics every time a new mention arrives
Detect anomalies and send alerts
Publish metrics to dashboard

Latency: < 100 milliseconds from mention to alert

Component 3: Real-Time Alerts

Send instant notifications when anomalies detected:

// alert-system.js
import { Kafka } from 'kafkajs';
import Twilio from 'twilio';
import nodemailer from 'nodemailer';
import axios from 'axios';

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

const consumer = kafka.consumer({ groupId: 'alert-group' });
await consumer.connect();
await consumer.subscribe({ topic: 'alerts', fromBeginning: false });

// Multi-channel alert delivery
await consumer.run({
  eachMessage: async ({ message }) => {
    const alert = JSON.parse(message.value.toString());
    
    // Route based on severity
    if (alert.severity === 'CRITICAL') {
      await sendSMS(alert);
      await sendSlack(alert);
      await sendEmail(alert);
      await callPhone(alert); // For extreme cases
    } else if (alert.severity === 'HIGH') {
      await sendSlack(alert);
      await sendEmail(alert);
    } else {
      await sendSlack(alert);
    }
    
    // Log alert
    await logAlert(alert);
  }
});

async function sendSMS(alert) {
  const client = new Twilio(
    process.env.TWILIO_ACCOUNT_SID,
    process.env.TWILIO_AUTH_TOKEN
  );
  
  await client.messages.create({
    body: `🚨 ${alert.type}: ${alert.message}`,
    from: process.env.TWILIO_PHONE,
    to: process.env.ALERT_PHONE
  });
  
  console.log('SMS alert sent');
}

async function sendSlack(alert) {
  const severityEmojis = {
    CRITICAL: '🔴',
    HIGH: '🟠',
    MEDIUM: '🟡',
    LOW: '🟢'
  };
  
  await axios.post(process.env.SLACK_WEBHOOK_URL, {
    text: `${severityEmojis[alert.severity]} *${alert.type}*`,
    blocks: [
      {
        type: 'section',
        text: {
          type: 'mrkdwn',
          text: `*Alert: ${alert.type}*\n${alert.message}`
        }
      },
      {
        type: 'section',
        fields: [
          { type: 'mrkdwn', text: `*Severity:*\n${alert.severity}` },
          { type: 'mrkdwn', text: `*Time:*\n${new Date(alert.timestamp).toLocaleString()}` }
        ]
      },
      {
        type: 'actions',
        elements: [
          {
            type: 'button',
            text: { type: 'plain_text', text: 'View Dashboard' },
            url: 'https://dashboard.example.com'
          },
          {
            type: 'button',
            text: { type: 'plain_text', text: 'Acknowledge' },
            action_id: 'acknowledge_alert'
          }
        ]
      }
    ]
  });
  
  console.log('Slack alert sent');
}

async function sendEmail(alert) {
  const transporter = nodemailer.createTransport({
    host: process.env.SMTP_HOST,
    port: 587,
    auth: {
      user: process.env.SMTP_USER,
      pass: process.env.SMTP_PASS
    }
  });
  
  await transporter.sendMail({
    from: 'alerts@example.com',
    to: process.env.ALERT_EMAIL,
    subject: `[${alert.severity}] ${alert.type}`,
    html: `
      <h2>${alert.type}</h2>
      <p>${alert.message}</p>
      <p><strong>Severity:</strong> ${alert.severity}</p>
      <p><strong>Time:</strong> ${new Date(alert.timestamp).toLocaleString()}</p>
      <a href="https://dashboard.example.com">View Dashboard</a>
    `
  });
  
  console.log('Email alert sent');
}

Alert delivery times:

Slack: < 1 second
Email: 1-3 seconds
SMS: 2-5 seconds
Phone call: 5-10 seconds

Component 4: Live Dashboard (WebSockets)

Push real-time metrics to browser:

// websocket-server.js
import { WebSocketServer } from 'ws';
import { Kafka } from 'kafkajs';

const wss = new WebSocketServer({ port: 8080 });
const kafka = new Kafka({
  clientId: 'dashboard-server',
  brokers: ['localhost:9092']
});

// Track connected clients
const clients = new Set();

wss.on('connection', (ws) => {
  console.log('Client connected');
  clients.add(ws);
  
  // Send current metrics immediately
  ws.send(JSON.stringify({
    type: 'INITIAL_STATE',
    data: getCurrentMetrics()
  }));
  
  ws.on('close', () => {
    clients.delete(ws);
    console.log('Client disconnected');
  });
});

// Consume metrics from Kafka
const consumer = kafka.consumer({ groupId: 'dashboard-group' });
await consumer.connect();
await consumer.subscribe({ topic: 'real-time-metrics', fromBeginning: false });

await consumer.run({
  eachMessage: async ({ message }) => {
    const metrics = JSON.parse(message.value.toString());
    
    // Broadcast to all connected clients
    const payload = JSON.stringify({
      type: 'METRICS_UPDATE',
      data: metrics,
      timestamp: Date.now()
    });
    
    clients.forEach(client => {
      if (client.readyState === WebSocket.OPEN) {
        client.send(payload);
      }
    });
  }
});

console.log('WebSocket server running on port 8080');

Frontend (React with WebSocket):

// Dashboard.jsx
import { useEffect, useState } from 'react';

function RealtimeDashboard() {
  const [metrics, setMetrics] = useState(null);
  const [connected, setConnected] = useState(false);
  
  useEffect(() => {
    const ws = new WebSocket('ws://localhost:8080');
    
    ws.onopen = () => {
      console.log('Connected to real-time stream');
      setConnected(true);
    };
    
    ws.onmessage = (event) => {
      const message = JSON.parse(event.data);
      
      if (message.type === 'INITIAL_STATE') {
        setMetrics(message.data);
      } else if (message.type === 'METRICS_UPDATE') {
        setMetrics(message.data);
      }
    };
    
    ws.onerror = (error) => {
      console.error('WebSocket error:', error);
      setConnected(false);
    };
    
    ws.onclose = () => {
      console.log('Disconnected from real-time stream');
      setConnected(false);
    };
    
    return () => ws.close();
  }, []);
  
  if (!metrics) {
    return <div>Loading real-time data...</div>;
  }
  
  return (
    <div className="dashboard">
      <div className="status">
        {connected ? (
          <span className="connected">🟢 Live</span>
        ) : (
          <span className="disconnected">🔴 Offline</span>
        )}
      </div>
      
      <div className="metrics">
        <MetricCard
          title="Mentions (Last 5 min)"
          value={metrics.currentVolume}
          change={`${metrics.volumeSpike}x baseline`}
          status={metrics.volumeSpike > 3 ? 'alert' : 'normal'}
        />
        
        <MetricCard
          title="Sentiment"
          value={metrics.currentSentiment.toFixed(2)}
          change={`${metrics.sentimentDrop > 0 ? '+' : ''}${metrics.sentimentDrop.toFixed(2)}`}
          status={metrics.sentimentDrop < -0.3 ? 'alert' : 'normal'}
        />
        
        <MetricCard
          title="Avg Engagement"
          value={Math.round(metrics.avgEngagement)}
          change="per mention"
        />
      </div>
      
      <div className="top-mentions">
        <h3>Top Mentions (Live)</h3>
        {metrics.topMentions.map(mention => (
          <MentionCard key={mention.id} mention={mention} />
        ))}
      </div>
    </div>
  );
}

Result: Dashboard updates in real-time (< 1 second latency). Users see live data flow without refreshing.

Advanced: Complex Event Processing

Detect patterns across multiple events:

// pattern-detector.js
class EventPatternDetector {
  constructor() {
    this.events = [];
    this.windowSize = 10 * 60 * 1000; // 10 minutes
  }
  
  addEvent(event) {
    const now = Date.now();
    this.events.push({ ...event, timestamp: now });
    
    // Remove old events
    const cutoff = now - this.windowSize;
    this.events = this.events.filter(e => e.timestamp >= cutoff);
    
    // Detect patterns
    this.detectCrisisPattern();
    this.detectViralPattern();
    this.detectCompetitorMentionPattern();
  }
  
  detectCrisisPattern() {
    // Pattern: 3+ negative keywords + volume spike + sentiment drop
    const crisisKeywords = this.events.filter(e =>
      e.type === 'mention' &&
      ['lawsuit', 'sick', 'poisoning', 'dangerous'].some(kw => 
        e.text.toLowerCase().includes(kw)
      )
    );
    
    if (crisisKeywords.length >= 3) {
      const volumeSpike = this.events.filter(e => e.type === 'mention').length > 100;
      const avgSentiment = this.events
        .filter(e => e.type === 'mention')
        .reduce((sum, e) => sum + e.sentiment, 0) / this.events.length;
      
      if (volumeSpike && avgSentiment < -0.3) {
        this.triggerAlert({
          pattern: 'CRISIS',
          severity: 'CRITICAL',
          message: 'Crisis pattern detected: Multiple negative keywords + volume spike + negative sentiment',
          evidence: {
            crisisKeywordCount: crisisKeywords.length,
            totalMentions: this.events.filter(e => e.type === 'mention').length,
            avgSentiment
          }
        });
      }
    }
  }
  
  detectViralPattern() {
    // Pattern: Single post with exponential share growth
    const postEngagement = {};
    
    this.events.filter(e => e.type === 'post_update').forEach(event => {
      if (!postEngagement[event.postId]) {
        postEngagement[event.postId] = [];
      }
      postEngagement[event.postId].push({
        timestamp: event.timestamp,
        shares: event.shares
      });
    });
    
    for (const [postId, updates] of Object.entries(postEngagement)) {
      if (updates.length >= 3) {
        const sorted = updates.sort((a, b) => a.timestamp - b.timestamp);
        const growthRates = [];
        
        for (let i = 1; i < sorted.length; i++) {
          const prev = sorted[i - 1];
          const curr = sorted[i];
          const growthRate = (curr.shares - prev.shares) / prev.shares;
          growthRates.push(growthRate);
        }
        
        const avgGrowth = growthRates.reduce((a, b) => a + b, 0) / growthRates.length;
        
        if (avgGrowth > 2.0) { // 200% growth per update
          this.triggerAlert({
            pattern: 'VIRAL_CONTENT',
            severity: 'HIGH',
            message: `Post going viral: ${(avgGrowth * 100).toFixed(0)}% growth rate`,
            postId,
            currentShares: sorted[sorted.length - 1].shares
          });
        }
      }
    }
  }
  
  detectCompetitorMentionPattern() {
    // Pattern: User mentions competitor after mentioning us (switching consideration)
    const userMentions = {};
    
    this.events.filter(e => e.type === 'mention').forEach(event => {
      if (!userMentions[event.author]) {
        userMentions[event.author] = [];
      }
      userMentions[event.author].push({
        timestamp: event.timestamp,
        text: event.text,
        sentiment: event.sentiment
      });
    });
    
    for (const [author, mentions] of Object.entries(userMentions)) {
      if (mentions.length >= 2) {
        const competitors = ['Competitor1', 'Competitor2', 'Competitor3'];
        const mentionedUs = mentions.some(m => m.text.includes('OurBrand'));
        const mentionedCompetitor = mentions.some(m =>
          competitors.some(comp => m.text.includes(comp))
        );
        
        if (mentionedUs && mentionedCompetitor) {
          // Check if competitor mention came after ours
          const ourMention = mentions.find(m => m.text.includes('OurBrand'));
          const competitorMention = mentions.find(m =>
            competitors.some(comp => m.text.includes(comp))
          );
          
          if (competitorMention.timestamp > ourMention.timestamp) {
            this.triggerAlert({
              pattern: 'COMPETITOR_SWITCH',
              severity: 'MEDIUM',
              message: `User considered us, then mentioned competitor`,
              author,
              ourSentiment: ourMention.sentiment,
              competitorSentiment: competitorMention.sentiment
            });
          }
        }
      }
    }
  }
  
  triggerAlert(alert) {
    console.log('Pattern detected:', alert);
    // Send to alert system
  }
}

// Usage in stream processor
const patternDetector = new EventPatternDetector();

await consumer.run({
  eachMessage: async ({ message }) => {
    const event = JSON.parse(message.value.toString());
    patternDetector.addEvent(event);
  }
});

Patterns detected:

Crisis signals (multiple negative keywords + volume spike)
Viral content (exponential share growth)
Competitor switching (user mentions competitor after us)
Coordinated campaigns (similar messages from multiple accounts)

Scaling Real-Time Systems

Challenge 1: Handle Millions of Messages

Solution: Kafka partitioning

// Partition by account or brand
const producer = kafka.producer();

await producer.send({
  topic: 'social-mentions',
  messages: [{
    key: mention.accountId, // Partition key
    value: JSON.stringify(mention)
  }]
});

// Messages with same key go to same partition
// Allows parallel processing while maintaining order per account

Scaling:

1 partition: 10k messages/sec
10 partitions: 100k messages/sec
100 partitions: 1M messages/sec

Challenge 2: State Management

Problem: In-memory state is lost if consumer restarts

Solution: RocksDB state store

import { Level } from 'level';

const db = new Level('./stream-state', { valueEncoding: 'json' });

// Persist state
await db.put(`baseline:mentions:${accountId}`, {
  avgVolume: 50,
  avgSentiment: 0.6,
  calculatedAt: Date.now()
});

// Restore state after restart
const baseline = await db.get(`baseline:mentions:${accountId}`);

Challenge 3: Exactly-Once Processing

Problem: Consumer crashes mid-processing, message processed twice

Solution: Kafka transactions

const consumer = kafka.consumer({
  groupId: 'analytics-group',
  isolation Level: 'read_committed' // Only read committed transactions
});

const producer = kafka.producer({
  transactionalId: 'analytics-processor',
  idempotent: true
});

await producer.transaction(async (transaction) => {
  // Process message
  const alert = processMessage(message);
  
  // Send alert (transactional)
  await transaction.send({
    topic: 'alerts',
    messages: [{ value: JSON.stringify(alert) }]
  });
  
  // Commit offset (transactional)
  await transaction.sendOffsets({
    consumerGroupId: 'analytics-group',
    topics: [{ topic: 'social-mentions', partitions: [{ partition: 0, offset: '100' }] }]
  });
});

// If transaction fails, everything rolls back
// Message will be processed again (no duplicate alerts)

Monitoring Real-Time Systems

Track system health:

// metrics-collector.js
import { Kafka } from 'kafkajs';
import prometheus from 'prom-client';

const register = new prometheus.Registry();

// Metrics
const messageProcessingDuration = new prometheus.Histogram({
  name: 'message_processing_duration_seconds',
  help: 'Time to process each message',
  registers: [register]
});

const messagesProcessed = new prometheus.Counter({
  name: 'messages_processed_total',
  help: 'Total messages processed',
  labelNames: ['topic'],
  registers: [register]
});

const consumerLag = new prometheus.Gauge({
  name: 'consumer_lag',
  help: 'Consumer lag in messages',
  labelNames: ['topic', 'partition'],
  registers: [register]
});

// Track during processing
await consumer.run({
  eachMessage: async ({ topic, partition, message }) => {
    const start = Date.now();
    
    try {
      await processMessage(message);
      
      messagesProcessed.inc({ topic });
      messageProcessingDuration.observe((Date.now() - start) / 1000);
      
    } catch (error) {
      console.error('Processing error:', error);
    }
  }
});

// Update lag metrics every 10 seconds
setInterval(async () => {
  const admin = kafka.admin();
  await admin.connect();
  
  const offsets = await admin.fetchOffsets({
    groupId: 'analytics-group',
    topics: ['social-mentions']
  });
  
  for (const topic of offsets) {
    for (const partition of topic.partitions) {
      const lag = partition.high - partition.offset;
      consumerLag.set({ topic: topic.topic, partition: partition.partition }, lag);
    }
  }
  
  await admin.disconnect();
}, 10000);

// Expose metrics endpoint
import express from 'express';
const app = express();

app.get('/metrics', async (req, res) => {
  res.set('Content-Type', register.contentType);
  res.end(await register.metrics());
});

app.listen(9090);

Key metrics to monitor:

Consumer lag: How far behind real-time (should be < 100)
Processing duration: Time per message (should be < 100ms)
Throughput: Messages per second
Error rate: Failed messages (should be < 0.1%)

Cost Considerations

Real-time systems are more expensive than batch:

Component	Batch (Daily)	Real-Time	Cost Increase
Compute	$50/month	$300/month	6x
Infrastructure (Kafka)	$0	$200/month	N/A
Database (writes)	$20/month	$100/month	5x
Total	$70/month	$600/month	8.5x

When the cost is worth it:

Crisis detection (avoiding PR disasters worth millions)
Live event monitoring (time-sensitive campaigns)
Trading signals (profits > costs)
Competitive advantage (being first matters)

When it's not worth it:

Historical reporting (batch is fine)
Non-urgent monitoring (15-minute delay acceptable)
Cost-sensitive projects (optimize for $ not speed)

Best Practices

Do's

✅ Use Kafka for message streaming - Industry standard, scales well
✅ Implement windowed aggregations - Sliding windows for real-time metrics
✅ Set up proper monitoring - Track lag, throughput, errors
✅ Use WebSockets for dashboards - Push updates, don't poll
✅ Implement circuit breakers - Prevent cascading failures

Don'ts

❌ Don't process synchronously - Use async/parallel processing
❌ Don't store everything in memory - Use persistent state stores
❌ Don't ignore backpressure - Slow down if consumers can't keep up
❌ Don't skip error handling - One bad message shouldn't crash system

Conclusion

Real-time processing transforms how you respond to social media:

Before (batch processing):

15-60 minute delays
Miss time-sensitive opportunities
Slow crisis response
Stale dashboards

After (real-time processing):

< 1 second latency
Instant alerts and responses
Catch crises early
Live dashboards

The investment: 40-60 hours to build, $300-1,000/month to run. The return: Milliseconds matter when reputation, revenue, or competitive advantage is at stake.

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Real-Time Social Media Processing: Stream & Analyze Live Data (Kafka + WebSockets)