Big Data Scale & Data Quality

Why Big Data APM Is Essential

Modern enterprise applications generate massive volumes of transaction data at high speed and across many tiers, a truly big data challenge for Application Performance Monitoring (APM).
Most APM vendors attempt to cope by sampling or snapshotting, capturing only a fraction of transactions. This creates an incomplete, patchy data set that can lead to missed root causes, inaccurate analysis, and unresolved performance issues.

Big data APM solves this problem. It delivers deep diagnostics at full scale, capturing, storing, and indexing billions of transactions per day without sacrificing data completeness or granularity. This gives you the evidence to reconstruct incidents in detail, rapidly investigate even rare or intermittent problems, and resolve issues before users are impacted.

How Big Data APM Maintains Data Quality at Scale

Big data APM platforms are designed for both scale and fidelity:

• End-to-End Transaction Tracing: Follow every transaction across all tiers, no blind spots
• Deep Call-Stack Visibility: Auto-tunes instrumentation overhead to maintain performance
• Massive Clustered Architecture: Handles tens of thousands of application components
• Efficient Data Pipeline: High-performance data transfer and storage
• High-Frequency Metrics: 1-second resolution for real-time insights
• Full Metadata Capture: No compromise on depth or detail