Framework de calidad de datos con Trust Score automático de 0-100. Integración con Great Expectations y detección de anomalías.
Every dataset gets an automatic Trust Score from 0 to 100. Based on Monte Carlo's 5 Pillars of Data Observability, combining 4 key dimensions:
Nexion provides complementary quality validation approaches for maximum coverage. Block bad data during ingestion AND monitor what's already stored.
Monitoring & Compliance
Independent quality rules that run on-demand, scheduled, or via API. Perfect for compliance reporting and continuous monitoring.
Best For
Real-time Validation
Inline quality checks in your data pipelines. Block bad data before it reaches your lakehouse. Automatic context from upstream nodes.
Best For
Enterprise data quality requires both approaches. Pipeline Gates block bad data at ingestion. Standalone Rules monitor what's already stored. Together, they provide complete coverage for compliance and operational excellence.
Comprehensive data quality measurement across all dimensions that matter.
Measures missing or null values across your datasets
Validates data values against expected patterns and ranges
Ensures data matches across related tables and systems
Tracks data freshness and update frequency
Identifies and prevents duplicate records
Confirms data conforms to defined business rules
Built on the industry-standard Great Expectations framework. Define expectations through the UI with 100+ rule types across 16 categories. No YAML required.
Null & Uniqueness
expect_column_values_to_not_be_nullexpect_column_values_to_be_uniqueexpect_compound_columns_to_be_uniqueSet Membership
expect_column_values_to_be_in_setexpect_column_distinct_values_to_equal_setexpect_column_distinct_values_to_contain_setNumeric & Statistical
expect_column_values_to_be_betweenexpect_column_mean_to_be_betweenexpect_column_stdev_to_be_betweenString & Pattern
expect_column_values_to_match_regexexpect_column_value_lengths_to_be_betweenexpect_column_values_to_match_like_patternTable Level
expect_table_row_count_to_be_betweenexpect_table_columns_to_match_setexpect_table_column_count_to_equalMulti-Column
expect_column_pair_values_to_be_equalexpect_column_pair_values_A_to_be_greater_than_Bexpect_multicolumn_sum_to_equalNexion goes beyond traditional quality frameworks with AI-powered rules, data contracts, ML anomaly detection, and integrated observability.
Create quality rules in plain English. AI translates your intent into executable checks automatically.
Example:
"Check that email is never null and matches email format"
expect_column_values_to_match_regexAutomatic baseline learning without manual thresholds. Detects anomalies using statistical and machine learning methods.
Define producer/consumer contracts in YAML. Automatic validation and enforcement in your pipelines.
Quality, Profiling, Lineage, and Schema work together. When quality fails, see downstream impact instantly.
Observability agents automatically investigate quality failures and identify root causes.
Get alerted on quality issues through your preferred channels. Configurable thresholds and routing.
The only composite data health score in the market. Four pillars combined into one actionable number.
How recent is the data?
25%
Pass rate of checks
25%
Expected row counts
25%
Structure stability
25%
Unique in the market: No other platform combines these four dimensions into a single, actionable Trust Score. Know your data health at a glance.
Every column is automatically profiled with statistics, patterns, and anomaly detection.
Row Count
1,245,678
Distinct
1,198,432
96.2% unique
Null %
0.3%
Excellent
Pattern
99.7% match
Top Domains
Anomalies Detected
Every pipeline run captures detailed quality metrics. Track quality over time.
Quality Score
92
Checks Passed
47/50
Rows Validated
1.2M
Rows Rejected
3,721
Failed Checks (3)
expect_column_values_to_match_regex(email)3,721 failuresexpect_column_values_to_be_unique(customer_id)12 duplicatesexpect_column_values_to_not_be_null(phone)156 nullsStart measuring and improving data quality with Nexion Trust Score.