Elementary Cloud Data Observability

Elementary Cloud data testing

Elementary Cloud runs your dbt tests plus Elementary's own tests on SQL tables and DataFrames, compares tables, checks unstructured text with warehouse LLM functions, and shows the rows that fail.

• SQL Table Tests: Supports all native dbt tests — not_null (completeness), unique (uniqueness), accepted_values (validity), relationships (consistency/RI) — plus Cloud-native checks and custom SQL assertions (docs).

• DataFrame Tests: Python tests allow custom test logic as Python scripts executed in the dbt environment (docs).

• Unstructured Data Tests: Same elementary.unstructured_data_validation dbt test (Beta, v0.18+) as OSS — uses warehouse-native LLM functions to validate free-text columns against plain-English expectations (docs).

• Table Comparison Tests: Custom SQL dbt tests can assert aggregate consistency across multiple models (e.g., row counts or sums match between tables) (docs).

• Inspect Failed Rows: Elementary captures and displays the specific rows that triggered each failing test for direct inspection (docs).

• Data Diff: No row-level dataset comparison tool found in documentation (docs).

• Data Contracts: Elementary has a Data Contract Test that validates schema (column names, types, nullability) against a stored baseline, but it is a cloud-based schema enforcement monitor — not a formal producer-consumer contract system defined in YAML/JSON Schema and enforced in CI/CD (docs).

Elementary Cloud data observability

Elementary Cloud learns baselines for volume, freshness, schema, columns and dimensions. Monitors are defined in dbt YAML or in the UI, and it tracks dbt run times and schedules.

Compare: data observability tools

• Data Profiling: Column anomaly monitors compute null rate, distinct count, min, max, avg, and standard deviation per column continuously (docs).

• Anomaly Detection: Anomaly detection uses historical baselines across volume, freshness, schema, column, and dimension metrics (docs).

• Freshness Monitors: Freshness anomaly and Event freshness anomaly tests detect stale tables and ingestion delays (docs).

• Volume Monitors: Volume anomaly test detects drops or spikes in row counts versus historical baselines (docs).

• Schema Monitors: Schema change tests detect added, dropped, or retyped columns, including changes that break downstream BI exposures (docs).

• Field Monitors: Column anomaly monitors track null count, distinct count, min, max, avg, and standard deviation per column as time series (docs).

• Distribution Monitors: Column anomaly monitors track value distributions per column and detect abnormal shifts (docs).

• Segment Monitors: Dimension anomaly test tracks counts per dimension value (e.g., by country or status) and detects distribution shifts (docs).

• Custom SQL Monitors: Custom SQL dbt tests and Cloud Tests allow arbitrary SQL expressions to be monitored continuously (docs).

• UI-Based Monitors: Cloud UI allows configuring monitors and test coverage without writing code; both code and UI config are supported (pricing).

• Monitors-as-Code: All tests and monitors can be defined in dbt YAML files, version-controlled, and deployed via CI/CD (docs).

• Pipeline Monitoring: Tracks dbt model run times, pipeline health, and schedule adherence on all plans (pricing).

• Partition-Based Monitors: Cloud Tests use warehouse metadata only (no data row scanning), so partition-based filtering is not applicable; dbt-based tests inherit whatever time-filter is built into the dbt model but Elementary Cloud does not expose dedicated partition-based monitor configuration (docs).

• Window-Based Monitors: Monitors run per dbt run, not over configurable tumbling or sliding time windows (docs).

Elementary Cloud incident management

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

• Alert Notifications: Sends failure notifications to Slack, email, and other channels on all plans (docs).

• Incident Triage: Incident management workflow with classification, owner assignment, and grouping on Growth and Enterprise plans (pricing).

• Alert Feedback: Elementary Cloud supports anomaly feedback — users can mark anomalies as expected or false positives directly from the incident UI (docs).

• Incident Resolution: Triage & Resolution AI agent supports the resolution lifecycle including marking as resolved or false positive (docs).

• Ticketing Integrations: Integrations with task and incident management tools including Jira available on Growth and Enterprise plans (pricing).

• On-Call Integrations: PagerDuty integration listed under task and incident management tool integrations on Growth and Enterprise plans (pricing).

Elementary Cloud data quality analytics

Elementary Cloud's dashboards and health scores track data quality.

• Data Quality Dashboards: Cloud dashboard shows test results, anomaly trends, model health, and pipeline run history across all dbt models (docs).

• Data Health Scores: Data health scores per table/model available on Growth and Enterprise plans (pricing).

• Incident Resolution Metrics: Elementary Cloud has incident triage/prioritization tooling and a Triage & Resolution agent, but no documented MTTD/MTTR dashboard, incident volume trending, or resolution-rate-by-team analytics (website).

Elementary Cloud data lineage

Elementary Cloud builds column-level lineage automatically from warehouse metadata, dbt and BI tools, with impact analysis.

Compare: data lineage tools

• Table-Level Lineage: Table-to-table lineage derived from the dbt manifest on all plans (pricing).

• Column-Level Lineage: Column-level lineage available on all plans through the dbt manifest and Elementary package (pricing).

• End-to-End Lineage: End-to-end lineage spanning ingestion, dbt models, and BI tool consumption available on Growth and Enterprise plans (pricing).

• Impact Analysis: Lineage graph enables downstream impact analysis — which models and BI reports are affected by an upstream failure (docs).

Elementary Cloud data governance

Elementary Cloud's catalog of dbt models and columns syncs edits back to dbt.

Compare: data catalog tools

• Data Catalog: Cloud catalog of dbt models, sources, columns, tests, and BI dashboards with test results, freshness, owners, and tags; metadata edits sync back to the dbt project (docs).

• Data Catalog Integration: External Catalog integrations available on Growth and Enterprise plans for pushing metadata to external catalog tools (pricing).

• Data Classification: The Governance Agent detects sensitive fields and enforces that appropriate tags are applied, but it is a metadata tagging-enforcement mechanism — it does not scan raw column values to assign PII/PHI/PCI type labels the way a data classification scanner does (docs).

• Data Products: Governance features focus on metadata, catalog, and lineage exploration; no data-product or domain construct for grouping monitors/tests/quality scores by business capability is documented (website).

Elementary Cloud workflows

Elementary Cloud has a REST API, a Python SDK, webhooks and an MCP server. It runs in dbt CI jobs and can block pipelines when tests fail.

• Scheduling: Cloud monitors run on a configurable schedule; dbt-based tests follow the dbt run schedule (docs).

• CI/CD Integrations: Runs natively in dbt CI jobs on Growth and Enterprise plans; Prevention feature explicitly blocks pipelines on quality failures within dbt CI (docs).

• Webhooks: Elementary Cloud supports webhooks as a dedicated alert channel — route any incident or test failure notification to a custom HTTP endpoint (docs).

• REST / GraphQL API: Elementary Cloud exposes a REST API with bearer-token authentication to list tests, test executions, asset health scores, assets and lineage (docs).

• MCP Server: Elementary MCP Server exposes models, tests, incidents, lineage, and catalog assets to AI coding agents (docs).

• Python SDK: The Elementary Python SDK (pip-installable) captures test results from any Python framework and reports them to Elementary Cloud, enabling quality monitoring outside dbt (docs).

• dbt Integration: Elementary IS a dbt package — tests, lineage, and scheduling are all dbt-native; the deepest possible dbt integration (docs).

Elementary Cloud AI agents

Elementary Cloud has AI agents that recommend tests, answer questions in chat, triage issues and open pull requests with proposed fixes. An MCP server is also available.

• AI Chat Assistant: The Test Recommendation AI agent accepts conversational questions (e.g., "What tests can ensure my customer data is up to date?") and generates ready-to-merge dbt test YAML via a pull request (docs).

• Natural Language Test Generation: The Test Recommendation ('Ella') Agent lets a user ask a plain-English question (e.g. "What tests can ensure all of my customer data is up to date?") from the test configuration screen; the agent analyzes assets/lineage, proposes specific tests, and opens a PR once approved (docs).

• Automated Test Generation: Test Recommendation AI agent identifies coverage gaps and generates tests for uncovered models (docs).

• AI Issue Triaging: Triage & Resolution AI agent classifies incidents, mutes noise clusters, and surfaces root causes (docs).

• AI Issue Resolution: Triage & Resolution AI agent opens pull requests with proposed fixes for data quality issues (docs).