PostHog vs Apache Spark

Open-source analytics that doesn't spy on your users, but might make you question your own product decisions meets the swiss army knife of big data, but good luck tuning it without a phd in distributed systems. Here's our take.

PostHog

Nice Pick

Open-source analytics that doesn't spy on your users, but might make you question your own product decisions.

Pros

• Feature-rich
• Self-hostable
• Session replay
• Feature flags
• Self-hosted option keeps data in-house and avoids third-party cookie drama
• Feature flags and A/B testing built-in, so you can iterate without deploying new code
• Session recordings let you watch users struggle in real-time, which is both terrifying and enlightening

Cons

• Complex
• Resource-heavy
• Overkill for simple sites
• Self-hosting can turn into a DevOps nightmare if you're not prepared for the infrastructure
• The UI can feel cluttered when you're drowning in event data, making simple insights harder to find

Apache Spark

The Swiss Army knife of big data, but good luck tuning it without a PhD in distributed systems.

Pros

• In-memory processing makes it blazing fast for iterative algorithms
• Unified API for batch, streaming, ML, and graph workloads
• Built-in fault tolerance and scalability across clusters

Cons

• Memory management can be a nightmare to optimize
• Steep learning curve for tuning and debugging in production

The Verdict

Use PostHog if: You want feature-rich and can live with complex.

Use Apache Spark if: You prioritize in-memory processing makes it blazing fast for iterative algorithms over what PostHog offers.

The Bottom Line

PostHog wins

Open-source analytics that doesn't spy on your users, but might make you question your own product decisions.