Sifflet Data Observability

Sifflet data testing

Sifflet checks SQL tables with built-in templates for nulls, uniqueness, ranges, accepted values and regex, and shows the rows that fail.

• SQL Table Tests: Built-in check templates cover nulls (completeness), uniqueness, value range and bounds (accuracy), value list and regex format (validity), referential integrity via dedicated template (consistency), and custom SQL assertions (docs).

• Inspect Failed Rows: Failing Rows view on the Monitor Details page shows all rows that didn't meet monitor criteria with no in-UI row limit; users can select which columns to display and export up to 20,000 rows as a CSV (comma or semicolon-separated) directly from the interface (docs).

• DataFrame Tests: SQL-first tool with no support for Python DataFrame libraries (Pandas, Spark, Polars) (docs).

• Unstructured Data Tests: No LLM-based text or semantic validation found in monitor templates or product documentation (docs).

• Table Comparison Tests: No cross-table aggregate assertion template (e.g., assert row counts match between two tables) found in monitor templates (docs).

• Data Diff: No row-level dataset comparison tool (dev vs. prod, pre/post-migration diff) found in documentation (docs).

• Data Contracts: No machine-readable data contract feature (schema + quality SLA agreements between producers and consumers) found in documentation (docs).

Sifflet data observability

Volume and field monitors learn thresholds from history, and you define monitors in the UI or as YAML deployed from a CLI. Pipeline monitoring is on the Enterprise plan.

Compare: data observability tools · data profiling tools

• Data Profiling: Field profiling monitors track null rates, distinct counts, distributions, and duplicates per column as continuous time series (docs).

• Anomaly Detection: Volume (dynamic) and field profiling monitors derive thresholds from historical baselines without requiring fixed expectations (docs).

• Freshness Monitors: Two templates: Freshness (row ingestion pattern) and Freshness (Update time gap) for metadata-based stale data detection (docs).

• Volume Monitors: Volume (dynamic) detects row count anomalies using historical baselines; Volume (static) applies a fixed threshold (docs).

• Schema Monitors: Schema Change monitor detects added, removed, renamed, or retyped columns and alerts owners (docs).

• Field Monitors: Field profiling tracks Nulls, Duplicates, and Unique counts per column as continuous time series with anomaly detection (docs).

• Distribution Monitors: Distribution Change monitor detects abnormal shifts in column value distributions compared to historical runs (docs).

• Segment Monitors: Custom Metrics support an optional third column in the SQL query that groups results by a dataset field, enabling per-segment anomaly detection (e.g., row count by country or status) (docs).

• Custom SQL Monitors: Custom Metrics template monitors any user-defined SQL query as a tracked time series; SQL template enables full custom assertions (docs).

• Partition-Based Monitors: Auto-mode targets BigQuery time partitions (INGESTION TIME and TIME_UNIT_COLUMN) automatically, using the partition key for the initial 365-day training run and a 1-day incremental window on subsequent runs — avoiding full-table scans (docs).

• UI-Based Monitors: No Code Conditions template lets users build custom monitors through a UI with conditional statements, no SQL required (docs).

• Monitors-as-Code: YAML-based monitor and asset configuration deployable via CLI for version-controlled, at-scale setup (docs).

• Pipeline Monitoring: Pipeline Monitoring tracks data pipeline job health and is available on the Enterprise plan (pricing).

• Window-Based Monitors: Scan Modes are documented but tumbling/sliding window-based monitoring is not explicitly confirmed (docs).

Sifflet incident management

Sifflet has a full incident workflow: alerts, triage, feedback, resolution, ticketing and on-call integrations.

• Alert Notifications: Notifications fired on monitor failures via email, Slack, and other configured channels (docs).

• Incident Triage: Automatic Incident Grouping clusters related failures into a single incident with owner assignment and priority (docs).

• Alert Feedback: Feedback Loop lets users qualify individual data points on time-series graphs as false positive, false negative, expected, fixed, or known error — false positive and false negative qualifications directly retrain the ML anomaly detection model, while known error causes those points to be excluded from future predictions (docs).

• Incident Resolution: Resolution lifecycle support: investigation tracking, marking as resolved or false positive, root-cause context surfacing (docs).

• Ticketing Integrations: Sifflet natively integrates with Jira and ServiceNow — tickets are created automatically on monitor failure with bidirectional status sync (In Progress / Done ↔ Sifflet incident status) (docs).

• On-Call Integrations: Sifflet officially documents PagerDuty integration — incidents are created automatically on monitor failure by routing Sifflet email alerts to a PagerDuty-generated service email address (docs).

Sifflet data quality analytics

Sifflet's dashboards and health scores track data quality, but not resolution times.

• Data Quality Dashboards: Built-in Dashboard with quality KPIs, monitor status, and trend charts across data assets (docs).

• Data Health Scores: Asset Health Status provides a per-asset quality score derived from monitor pass/fail results (docs).

• Incident Resolution Metrics: Sifflet's native Dashboard shows an incidents-by-status chart, table uptime, and monitor coverage, but documentation confirms it has no MTTD/MTTR tracking or resolution-rate-by-team/owner analytics (docs).

Sifflet data lineage

Sifflet maps field-level lineage end to end, from warehouse query logs to BI dashboards, with impact analysis.

Compare: data lineage tools

• Table-Level Lineage: Table-to-table dependencies tracked via query log parsing and integration manifests (docs).

• Column-Level Lineage: Field-level lineage is mapped automatically by parsing SQL query logs or native lineage metadata on Snowflake, BigQuery, Redshift, and Databricks (docs).

• End-to-End Lineage: Lineage spans ingestion (Fivetran, Airbyte), transformation (dbt), and BI consumption layers (docs).

• Impact Analysis: Field-level lineage shows every downstream dashboard, report, or table a column change affects, and traces issues upstream for root cause analysis (docs).

Sifflet data governance

Sifflet's catalog, data classification and data products overlap with data governance tools.

Compare: data catalog tools

• Data Catalog: Built-in searchable data catalog with asset pages, tags, ownership metadata, and business glossary (docs).

• Data Catalog Integration: Sifflet integrates with Atlan to sync metadata (descriptions, tags, business glossary terms, ownership, custom fields) into Sifflet daily, enriching the built-in data catalog with external governance context (docs).

• Data Classification: Sifflet supports data classification via tags that can be applied to assets and fields to label sensitive data (e.g., PII, confidential), used as filters in the catalog and to scope monitors (docs).

• Data Products: Sifflet lets users group datasets, dashboards, and tasks into "Data Products" serving a common business use case, with underlying asset/monitor health rolling up into the product's overall status (docs).

Sifflet workflows

Sifflet has a REST API, webhooks and a dbt integration. Flow Stopper blocks pipeline runs and pull request merges when data quality checks fail. It has no Python SDK.

• Scheduling: Monitors run on a configurable scan schedule; frequency is set per monitor (docs).

• CI/CD Integrations: Flow Stopper integration blocks pipeline runs and PR merges when data quality checks fail (docs).

• Webhooks: Sifflet webhooks send HTTP POST payloads to custom endpoints on MONITOR_FAILURE, MONITOR_SUCCESS, MONITOR_STATUS_CHANGE, and TRANSFORMATION_RUN_FAILURE events (docs).

• REST / GraphQL API: Full REST API reference available for programmatic access to monitors, incidents, and assets (docs).

• MCP Server: Sifflet MCP server is listed in the developer documentation sidebar (docs).

• dbt Integration: Imports dbt manifest.json for lineage and supports dbt-based CI/CD quality blocking via Flow Stopper (docs).

• Python SDK: Sifflet exposes a REST API but does not provide a dedicated pip-installable Python SDK for programmatic integration (docs).

Sifflet AI agents

Sifflet has an AI chat assistant that answers questions about data health, writes and suggests monitors, and triages issues. Forge, in early access on Enterprise, proposes fixes. An MCP server is also available.

• AI Chat Assistant: Sifflet AI Chat is an in-product conversational assistant that answers questions about data health, generates governance reports, provides platform guidance, and suggests monitors to create (docs).

• Natural Language Test Generation: Sifflet's Contextualized Text-To-SQL feature lets users create custom SQL monitors from a plain-English prompt, with the AI Assistant generating SQL using data-catalog context (docs).

• Automated Test Generation: Sentinel (Auto-Monitoring, beta) recommends which monitors to add based on data patterns and usage (docs).

• AI Issue Triaging: Sage performs Root Cause Analysis to identify likely causes and surface relevant context for failing checks (docs).

• AI Issue Resolution: Forge (early access on Enterprise) proposes fix suggestions for data quality issues (pricing).