Freshness. Volume. Schema. Quality. All watched. All alerted. All before your stakeholders notice.
Most data teams find out about broken pipelines the same way: a Slack message from the CMO. Logiciel's data monitoring tools watch every pipeline, every model, and every dashboard so issues get caught at ingestion - not when the C-suite reviews the wrong number.
Symptoms most data leads quietly accept:
Teams searching this term typically already have:
Monitoring that understands data - not just the box it runs on.
Freshness, volume, and schema monitoring out of the box — no rule writing required. Out-of-the-box monitoring means teams get value in hours, not weeks — the typical onboarding time is 24 hours from connection to first surfaced anomaly.
Anomaly detection trained on your historical patterns — fewer false positives, more real signal. Anomaly detection trained on historical patterns catches the issues nobody anticipated, which are the ones that actually move stakeholder trust.
Lineage-aware alerts — when something breaks, you see exactly what's downstream and who to tell. Lineage-aware alerts eliminate the typical 'who owns this' fire drill; the alert routes to the upstream owner and downstream consumers as one threaded conversation.
Native integration with dbt, Airflow, Snowflake, Databricks — no agents, no sidecars. Native dbt, Airflow, Snowflake, Databricks integration means there's nothing to instrument and no agents to deploy.
Trading data, risk models, regulatory reporting — sub-second SLAs and audit-ready governance.
Listing data, transaction pipelines, geospatial analytics — multi-source consolidation.
EHR integration, claims pipelines, clinical analytics — HIPAA-aware infrastructure.
Product analytics, customer 360, usage-based billing — embedded and operational data.
Inventory, pricing, order, and customer pipelines — real-time and high-throughput.
IoT, project, and supply-chain data — operational analytics on hybrid stacks.
| Dedicated Pod | Staff Augmentation | Project-Based Delivery |
|---|---|---|
| Embedded data engineering pod aligned to your sprint cadence — typically 3–6 engineers + a US lead. | Senior data engineers, architects, and SMEs slotted into your team to unblock specific work. | Fixed-scope, milestone-driven engagements with clear deliverables and outcomes. |
We map your stack, workloads, team, and constraints in a working session - not an RFP response.
Reference architecture grounded in your reality, with capacity, cost, and migration plans.
Iterative implementation with weekly demos, code reviews, and your team in the loop.
Managed operations or knowledge transfer - your choice. Both with US-aligned coverage.
Continuous tuning of cost, performance, and reliability against measurable SLAs.
Detect stale data within minutes of expected refresh windows.
Catch upstream schema changes before they break consumers.
Send the right alert to the right team based on column-level lineage.
Spot row count drops or surges before downstream KPIs swing.
Codified data quality rules with severity routing.
Per-domain SLA dashboards for business owners - not just engineers.
Same monitoring primitives (freshness, volume, schema, custom SQL, anomaly detection) but broader scope and better TCO at scale. Monte Carlo and Bigeye are pure-play data observability tools; Logiciel includes their capabilities plus pipeline observability, BI/dashboard health, reverse-ETL monitoring, and cost telemetry — meaning you replace one observability tool plus 2-3 adjacent point tools instead of just the observability layer. For US customers monitoring 1,000+ datasets, our per-pipeline pricing typically saves 40-60% versus per-table pricing of pure observability vendors. Lineage-aware alert routing reduces alert fatigue dramatically — most customers cut false-positive volume 70% in the first month versus their previous tool.
By column-level lineage — owners of the affected pipeline plus downstream consumer teams are notified, with a single ack flow that prevents the same alert from waking up four people. Alerts route through Slack, PagerDuty, Opsgenie, Teams, email, or webhook; severity tiers control which channels fire (P1 = PagerDuty, P3 = Slack only). The lineage routing is the magic: when an upstream column changes, alerts go to the source-system owner and the downstream dbt model owner and the BI dashboard owner — but as one threaded conversation, not three separate fires. Most customers report 60-80% reduction in 'is anyone seeing this?' threads within the first month.
Slack, PagerDuty, Opsgenie, Microsoft Teams, ServiceNow, Jira, email, webhook, and SIEM (Splunk, Datadog, Elastic, Sumo) — all native. Two-way sync for ServiceNow and Jira so incident lifecycle stays in your existing tools. SCIM and SSO via Okta, Azure AD, Google Workspace, Auth0, and any SAML 2.0 IdP. Source integrations include Snowflake, Databricks, BigQuery, Redshift, Postgres, MySQL, MongoDB, Kafka, Kinesis, Pub/Sub, dbt Cloud, dbt Core, Airflow, Dagster, Prefect, Looker, Tableau, Mode, Sigma, ThoughtSpot, plus 200+ source systems for ingestion. New integrations ship roughly every 2-4 weeks based on customer demand.
Most teams have anomaly detection running on their top 25 datasets within 24 hours of signup, with the first surfaced anomaly typically arriving within 48-72 hours (often catching an issue the team hadn't noticed). The first week is baseline stabilization; weeks 2-4 are tuning and routing configuration; by day 30 most teams have eliminated 60-80% of 'is the data right?' Slack threads. ROI in the first quarter is typically expressed as engineering hours regained (8-12 hours per engineer per week) and stakeholder trust restored — fewer 'why doesn't this number match' meetings, faster financial close cycles, more confident exec dashboards.
Yes — anything you can express in SQL becomes a monitored check, with full configurability for severity, alerting cadence, routing, and escalation. Custom checks are versioned in Git alongside your data models (we treat them as code, not UI configuration that nobody reviews), reviewed in PRs, and tested in ephemeral environments before production. Common custom check patterns: business-rule reconciliation (revenue match across systems), distribution monitoring on critical KPIs, cross-table consistency (foreign-key integrity at scale). Custom checks coexist with platform-managed anomaly detection — you don't have to choose between rules and ML; we run both layers in parallel with unified alert routing.
Anomaly detection is per-dataset trained on 30-60 days of historical patterns to minimize false positives, with continuous re-training as patterns shift legitimately (product launches, seasonality, fiscal close). Most teams report fewer than 2 false positives per week per 1,000 datasets after the first week of stabilization. Initial onboarding can produce more noise as baselines establish; we provide a 7-day tuning period with an implementation engineer to suppress known-noisy patterns and adjust sensitivity. After tuning, signal-to-noise is typically 5-10x better than rule-only systems because the platform learns your patterns rather than alerting on every rule violation.
Yes — up to 25 datasets monitored free, forever. No credit card, no time limit, no feature crippling. The free tier includes anomaly detection, freshness monitoring, schema drift, lineage-aware routing, and Slack alerting on those 25 datasets — full capability, just bounded scope. About 30% of free-tier users convert to paid within 6 months, typically when their dataset footprint outgrows 25 or when they want enterprise governance. The other 70% stay on free indefinitely, which is fine — the goal is to make data quality accessible to teams who can't budget for enterprise tooling but still need their pipelines to work.
Connect your warehouse in 5 minutes. We'll auto-profile your top datasets, baseline normal patterns, and start alerting on anomalies - at no cost, no commitment.
