Analytics with no warehouse.
Analytics is a SQL notebook plus a dashboard, sitting on the same work graph your apps already run on. No DAG, no ETL, no warehouse bill. Operational reporting in seconds, board reporting in minutes, with permissions intact.
Five things, no warehouse.
Most analytics tools assume you have a warehouse. Analytics runs SQL directly over the work graph and the live application database. The warehouse is optional, not required.
ANSI-SQL plus pandas-style cells. Notebooks are first-class objects, versioned, reviewable, schedulable.
Promote a chart from a notebook to a dashboard with one click. Filters, drilldowns, and parameters carry over.
"Tell me when pipeline coverage drops below 3.0x." The alert ships with the chart and the top-3 drivers of the change.
Rows respect the workspace ACLs. A regional VP sees their region, not the global pipeline. Dashboards inherit the asker's view.
Every dashboard has a one-paragraph narrative that updates when the data does, grounded in the actual deltas, not in a template.
Three layers, one query path.
Analytics queries the live work graph by default and reaches into the warehouse only when the question requires deep history.
Live read replica
Every module writes through a single Postgres-compatible work graph. Analytics reads a hot replica with row-level ACLs applied. No warehouse, no glue.
Write SQL, see results
Notebooks accept ANSI SQL plus pandas cells. Query cost is shown. Cache is automatic per query plus per parameter. Real-time refresh is opt-in.
Publish, alert, narrate
Promote charts to a dashboard. Bind alerts to expressions. The narrative writes itself from the data, with drivers ranked.
SQL + cells, versioned.
Notebooks are first-class. Reviewable, schedulable, with parameter binding. The output below is real.
-- pipeline coverage by week, last 6 weeks SELECT date_trunc('week', updated_at) AS wk, SUM(amount) FILTER (WHERE stage IN ('Q','Eval','Proc')) AS open_pipe, AVG(closed_won_week_amount(wk)) AS avg_quota FROM deals WHERE updated_at > NOW() - '42 days' GROUP BY wk ORDER BY wk;
-- alert when coverage drops below 3.0x for any week. ALERT low_coverage WHEN coverage_ratio < 3.0 SEND slack('#revops', with_drivers=TRUE);
Nine more, all shipping.
Each capability below is on by default for every workspace and every tier.
15 chart types
Line, bar, area, cohort, sankey, funnel, donut, heatmap, scatter, histogram, table, plus 4 typeahead-defined custom types.
Auto-narrative
Plain-English explanation per chart, grounded in the deltas. Updates with the data.
Parameter binding
Dashboards take parameters: region, segment, owner. URL params carry. Embed links inherit caller's ACL.
Row-level ACL
Same ACL as the rest of the platform. A VP sees their region. Groups roll up naturally.
Warehouse passthrough
Snowflake, BigQuery, Redshift, Databricks. Federated query across work graph and warehouse in one notebook.
Inline in Docs
Embed a live chart inside any doc. Refresh on read. Permission-checked per viewer.
Anomaly detection
Per-metric baseline with seasonality. Alerts fire on deviation, with a one-paragraph why.
SQL lineage
Track which columns and tables feed each chart. Refactor with confidence; the breakage shows up in CI.
Scheduled exports
Email, Slack, S3, or your warehouse. CSV, Parquet, XLSX. Signed URLs, watermarked exports.
Reads from every module.
Because Analytics queries the same graph as the apps, joins between deals, tasks, contracts, and meetings are trivial. No glue, no copy.
One graph, six readable sources
Every module writes the same Postgres-compatible work graph, so joins are trivial. Want closed-won deals where the procurement step took over 14 days and the customer had at least one P0 ticket? One SQL query.
Analytics vs. Mode.
Mode is a SQL notebook over a warehouse. wrxstack Analytics is a SQL notebook over the live work graph, with permissions intact.
wrxstack Analytics vs. Mode
| Capability | Mode | wrxstack Analytics |
|---|---|---|
| Source of truth | Warehouse | Live work graph + warehouse |
| ETL / DAG | Required | Not required |
| Row-level ACL | Warehouse role only | Inherited from platform |
| Auto-narrative | Not available | Grounded in deltas |
| Alerts with reasons | Thresholds only | Top-3 drivers per alert |
| Inline in apps | Embed iframe | Native blocks in Docs, etc. |
| Cost | Seat + warehouse compute | No warehouse compute fees |
Common questions.
Six things data and ops leaders ask in the first hour.
Can we still keep our warehouse?
Yes. The warehouse passthrough lets you join the work graph with Snowflake, BigQuery, Redshift, or Databricks in one SQL statement. We push down where we can, and we cache where we cannot.
How fresh is the data?
The read replica lags the primary by 300 to 800ms in normal operation. For dashboards bound to a metric, refresh is configurable per chart. Real-time mode is available for ops dashboards that need tighter freshness.
Do we lose anything by not having a warehouse?
For analytics on the workspace data, no. For deep historical analytics across non-wrxstack systems, the warehouse passthrough is the right answer. We do not store wrxstack data redundantly. Your warehouse and our work graph stay in sync because joins happen at query time.
Can analysts share dashboards externally?
Yes. Public dashboards, password-protected dashboards, and per-recipient watermarked dashboards. Per-row ACL is enforced on the recipient's view. SSO can be required for external sharing.
How does the auto-narrative actually work?
The narrative is generated from the chart's delta facts, not from a prompt template. The model is constrained to attributable changes (a region's contribution to the move, a deal's contribution to the cohort). Sentences that cannot be attributed are dropped. The result is short, accurate, and updatable.
Can data engineers still use dbt?
Yes. We support dbt models that materialize into the work graph. The lineage graph picks up the dbt DAG, and downstream charts surface broken models in CI. We never require dbt, but we do not punish you for keeping it.
Pairs well with.
Analytics is sharpest with CRM, Tasks, and Projects live in the same workspace.
Who this is not for.
Analytics reports on your live workspace with no warehouse to maintain. If you need a full BI warehouse with SQL modeling, a semantic layer, and joins across dozens of external sources, pair Atlas with a dedicated BI tool. Analytics answers questions about the work in Atlas, not your entire data estate.
Stop maintaining the analytics stack.
Free to start, no credit card. Notebooks, dashboards, and alerts in the box. Warehouse passthrough available.