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Understand Your Data with Continuous, Scalable Profiling

Analyze data distributions, detect patterns, and surface quality issues across your datasets — with profiling that integrates directly into data observability workflows.

TRUSTED BY ENTERPRISE DATA TEAMS WORLDWIDE

Every Data Platform Team Knows This Too Well

Schema Changes Without Notice

Upstream sources change schemas constantly. Your pipelines break. Engineers spend hours debugging what changed and when.

PII Discovered During Audits

Sensitive data slips into tables it shouldn't touch. You find out during a compliance audit — not at ingestion.

Numbers That Don't Match

Analysts file tickets. Engineers debug. CDOs field questions about why the board report numbers don't align. The root cause: no one profiled the data.

The root cause is almost always the same:no one profiled the data. You manage hundreds of pipelines, data lands from dozens of sources, and quality degradation is invisible until it hits downstream consumers.

Key Capabilities

What You Can Do with Data Profiling in Acceldata

Analyze distributions, completeness, uniqueness, and patterns across datasets.

Row counts, null %, cardinality, min/max

Column-level and row-level pattern detection

Duplicate detection and schema validation

Build historical understanding of how data should behave.

Learn trends over time

Establish expected ranges and patterns

Detect drift and anomalies automatically

Turn profiling insights into real-time actions.

Set thresholds directly on profiling metrics

Trigger alerts without creating separate data quality rules

Detect issues earlier in the pipeline (“shift left”)

Use profiling signals to automatically classify data.

Detect patterns like emails, phone numbers, PII/PHI

Auto-tag columns based on content

Enable policy-driven governance at scale

Built for modern data platforms.

Spark-native engine for large-scale processing

Pushdown capabilities for Snowflake, BigQuery, etc.

Incremental and selective profiling to optimize cost

What Data Teams Gain from Data Profiling

Better visibility into data characteristics before defining rules

More informed data quality checks based on actual data patterns

Faster identification of inconsistencies such as duplicates or schema issues

Improved governance workflows through better understanding of data

Why Acceldata

Before

One-time scans

Separate DQ rules for alerts

Manual tagging

SQL-limited

Static insights

After

One-time scans

Alerts directly from profiling

Automated classification

Spark-based, petabyte scale

Baseline-driven anomaly detection

Ready to get started

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