Author. Schedule. Observe. Replay. Optimize. One platform. One pager schedule.
Data pipeline management isn't 'just orchestration.' It's authoring, scheduling, observability, lineage, cost, and SLA - across pipelines built in different languages by different teams. Logiciel unifies all of it for US data teams that have outgrown stitched-together tooling.
What this looks like for most teams:
Teams here typically need:
Authoring + scheduling + observability + lineage + cost in one tool. Authoring, scheduling, observability, lineage, and cost in one tool eliminate the integration tax of stitching five vendors with five SLAs.
A single oncall schedule covering the whole pipeline lifecycle. Single oncall schedule across the pipeline lifecycle is operationally simpler than rotating across five tools' separate paging models.
Stakeholder-readable SLAs without exporting CSVs every Monday. Stakeholder-readable SLAs without manual CSV exports turn data quality from an engineering preoccupation into a discipline business owners can govern.
Pipeline management as one product, not five.
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.
Git-native pipelines in SQL, Python, dbt, Spark.
Freshness, volume, schema, anomaly detection per pipeline.
Asset-level lineage across pipelines, models, and BI.
Cron, event-driven, or asset-based; idempotent backfills.
Per-pipeline cost attribution and forecasting.
Per-domain SLAs reported to business owners.
Often: Airflow + dbt Cloud + Monte Carlo + a homegrown cost dashboard. Sometimes: just one or two of those. The exact replacement depends on your current footprint, but the unification is the win - instead of four tools with four UIs, four billing models, four oncall integrations, and four upgrade cycles, you have one platform that handles the full pipeline lifecycle. For mid-market US customers, consolidation typically reduces TCO 30-50% in year one. Replacement is gradual, not big-bang; most teams migrate workloads over 3-9 months while running tools in parallel until parity is established. We provide migration playbooks for each common predecessor stack.
We integrate with Airflow, dbt, Spark (EMR, Databricks, Glue), Snowflake, Databricks, BigQuery, Redshift, Iceberg, Delta, Hudi, Kafka, Kinesis, Pub/Sub, Postgres, MySQL, MongoDB, plus 200+ source systems for ingestion and 100+ destinations for reverse-ETL. BI integrations cover Looker, Tableau, Mode, Sigma, ThoughtSpot, Power BI. Observability and incident integrations cover Slack, PagerDuty, Opsgenie, Teams, ServiceNow, Jira. New integrations ship every 2-4 weeks based on customer demand. If you have a tool we don't integrate with yet and it's blocking, we can usually deliver a custom integration in 2-4 weeks under contract.
Native SAML SSO, OIDC, SCIM provisioning, and SCIM deprovisioning - typically wired in on day one of implementation. Group-based RBAC at the pipeline, asset, and project levels with column-level access controls integrated with the catalog. Just-in-time access for sensitive datasets through approval workflows (ServiceNow, Jira) integrated with your existing IAM. Audit logging on every access event flows into your SIEM (Splunk, Datadog, Elastic). For Microsoft-heavy enterprises, native Entra ID integration including conditional access. For US Federal and regulated customers, we support US-citizen-only engineering access with documented chain-of-custody.
Yes - per-domain SLA dashboards designed for business owners, not just engineers. Stakeholders see freshness SLAs, accuracy metrics, and operational status for the data products their domain depends on, without needing to understand pipeline internals. Dashboards are configurable per audience (Sales Ops sees one view, Finance sees another, Product sees a third) and update in real time. For executive consumers, we generate weekly summary reports automatically - eliminating the typical 'data team prepares stakeholder report every Friday' overhead. The transparency typically reduces 'is the data right?' meetings by 60-80% within a quarter of adoption, freeing data team time for substantive work.
Yes - most teams start with 5-10 pipelines (typically the most painful or the most strategic) and grow from there as confidence builds. Migration is incremental and reversible: Airflow keeps running for legacy DAGs while new pipelines and migrated DAGs run on Logiciel. There's no forced cutover and no parallel system you maintain forever. By the time you've migrated 50-100 pipelines, the operational benefits compound and the migration accelerates organically. Full migration of a 200-DAG Airflow installation typically takes 8-16 weeks of active engineering effort, paced to your team's capacity. Migration is fixed-fee per-pipeline-bucket so the budget stays predictable.
Per active pipeline - not per task run, not per row, not per seat. Pricing is predictable when you backfill, replay, or grow your team. Mid-market customers (50-200 pipelines, 5-30 data engineers) typically pay $40-90K ARR. Enterprise tiers (1,000+ pipelines, multi-BU, regulated, dedicated TAM, US-citizen support) start at $200K ARR. We benchmark TCO at evaluation against your incumbent stack (often Airflow + Monte Carlo + dbt Cloud combined); typical savings are 30-50%. Pricing is transparent and contractually capped - no surprise overage bills when you add a new business unit or replay six months of historical data.
Yes - optional managed tier with US-aligned 24/7 coverage. Standard tier (8x5 US business hours, named US TAM, P1 < 1hr first response, monthly reviews) starts at $40K monthly. Enterprise tier (24/7, P1 < 15min, US-citizen pool, dedicated principal architect, quarterly executive QBRs, custom SLAs) starts at $120K monthly. Managed operations covers pipeline reliability, cost optimization, governance changes, capacity planning, incident response, and quarterly architecture reviews. About 30% of our customers run fully managed; 50% run self-managed with retained advisory hours; 20% are fully self-managed. All tiers include knowledge transfer as a contractual deliverable.
Book a 45-minute demo. Bring your messiest pipeline. We'll show you authoring, observability, cost, and SLA management in one place - and where the migration starts.
