Heap vs Apache Spark

Automatic analytics that captures everything, so you can stop guessing what users actually do meets the swiss army knife of big data, but good luck not cutting yourself on the complexity. Here's our take.

Heap

Nice Pick

Automatic analytics that captures everything, so you can stop guessing what users actually do.

Pros

• Auto-captures all user events without manual instrumentation
• Retroactive analysis lets you query past data without pre-defining events
• Intuitive visual interface for non-technical team members
• Session replay and heatmaps integrated with analytics

Cons

• Can become expensive quickly as data volume grows
• Data sampling on free and lower-tier plans limits accuracy
• Requires careful data governance to avoid noise from irrelevant events

Apache Spark

The Swiss Army knife of big data, but good luck not cutting yourself on the complexity.

Pros

• Unified engine for batch, streaming, SQL, and ML workloads
• In-memory processing speeds up iterative algorithms dramatically
• Fault-tolerant and scales to petabytes with ease

Cons

• Configuration hell: tuning Spark is a full-time job
• Memory management can be a nightmare in production

The Verdict

Use Heap if: You want auto-captures all user events without manual instrumentation and can live with can become expensive quickly as data volume grows.

Use Apache Spark if: You prioritize unified engine for batch, streaming, sql, and ml workloads over what Heap offers.

The Bottom Line

Heap wins

Automatic analytics that captures everything, so you can stop guessing what users actually do.