Real-Time Analytics Without the Data Engineering

The Infrastructure Myth

Want real-time analytics? Here’s what the industry says you need:

• Kafka for event streaming ($50K+/year)
• Spark for processing ($30K+/year)
• Data warehouse for storage ($40K+/year)
• Data engineers to maintain it all ($300K+/year)
• 6-12 months to build

Total: $400K+ and a year of your life.

What “Real-Time” Actually Means

Let’s be honest about requirements. When you say “real-time,” you usually mean:

• See the impact of a feature within hours of launch
• Catch anomalies before they become crises
• Answer questions without waiting for overnight batch jobs

You don’t need sub-millisecond latency. You need fast-enough answers to make decisions.

The MCP Approach

Clayva’s MCP integration gives you real-time analytics through conversation:

“Show me signups in the last hour, broken down by source”

Last 60 minutes: 127 signups
- Organic: 45 (35%)
- Paid: 38 (30%)
- Referral: 44 (35%) ← Unusual spike

Alert: Referral signups are 3x normal rate.
Source: ProductHunt feature (detected via referrer analysis)

This query ran against live data. No batch jobs. No waiting until tomorrow.

How It Works

Clayva’s architecture is designed for this:

1. Events stream in real-time through our lightweight SDK
2. Indexes update continuously (not hourly batch jobs)
3. AI queries optimize automatically for speed
4. Results cache intelligently for repeated questions

You get real-time capabilities without building real-time infrastructure.

The Migration Path

Already have a data stack? Clayva works alongside it:

1. Start with Clayva for ad-hoc real-time queries
2. Keep your warehouse for historical analysis
3. Gradually shift more workflows to conversation
4. Eventually, simplify your infrastructure

Start Simple

You don’t need to rip and replace. Just connect Clayva and start asking questions. The real-time answers will speak for themselves.