DataOps Platform That Brings Engineering Discipline to Data Teams

Both, deliberately. The platform encodes DataOps practices in working software (CI/CD for pipelines, observability, runbooks, oncall, SLA tracking); we also offer DataOps maturity assessments, embedded coaching, and team transformation engagements. Most customers want both: the tool gives engineering teams primitives they can run themselves; the coaching provides external perspective, accelerated cultural change, and decision authority that a tool alone can't deliver. Pricing reflects the split: per-asset platform license, fixed-fee per coaching engagement. Customers who buy only the tool typically engage coaching when they hit cultural friction; customers who buy only coaching add the tool when they want to operationalize the practices between engagements.

Sub-setting and synthetic data generation for safe non-prod testing. Sub-setting tools extract a representative slice of production data with referential integrity preserved; synthetic generation creates realistic-looking data that has zero PII or business-sensitive content. Both approaches are configurable per source — typically sub-setting for development environments (analysts need to feel real data shapes), synthetic for security-sensitive contexts (HIPAA, GDPR-protected data). Test data refresh is automated and audited. For regulated customers, the test data approach is auditable evidence of data protection in non-prod environments — a frequent gap in pre-DataOps shops.

Yes — maturity assessments and embedded coaching available. Maturity assessment is fixed-fee, 4-week engagement that benchmarks your team against industry-standard DataOps practices (CI/CD, observability, incident management, SLA discipline, automation, governance) and produces a 90-day uplift plan with named owners and measurable outcomes. Embedded coaching is typically 3-6 months with a US-based DataOps lead working alongside your team — pairing on incidents, leading retros, codifying runbooks, mentoring senior engineers. Coaching is most effective when paired with platform adoption; the tool reinforces the practices, the coaching builds the cultural muscle.

Per active asset — DataOps capabilities (CI/CD, observability, incident management, SLA tracking) included in standard tier. Mid-market customers (5-30 data engineers, 200-500 assets) typically pay $40-90K ARR. Enterprise tiers ($200K+) add advanced governance, custom workflows, dedicated TAM. Coaching engagements are priced separately at fixed-fee per engagement, ranging from $50K (4-week assessment) to $400K (6-month embedded coaching). Pricing is transparent with workload-grounded TCO comparisons available at evaluation. Compare to building these capabilities in-house: DataOps platform engineering teams are typically 3-6 engineers ($900K-$1.8M annual cost), so the platform pays back quickly even before coaching value.

Pipelines and dbt projects are versioned in Git (your existing repos), tested in ephemeral environments (separate dev/staging/prod with data subsetting), and deployed via promotion (dev → staging → prod with automated checks at each gate). Tests include schema validation, dbt test execution, anomaly detection on synthetic data, and integration tests against staging data. Failed tests block deploys; passed tests promote automatically. We integrate with GitHub Actions, GitLab CI, Jenkins, and other CI platforms — meaning you don't replace your existing CI, you extend it. For US teams without mature CI/CD practices, the platform provides defaults and templates that establish the practice without requiring DevOps expertise on the data team.

Native GitHub Actions, GitLab CI, Jenkins, CircleCI, and other CI platforms — meaning you keep your existing CI infrastructure and add Logiciel-specific actions/jobs to it. We don't replace your CI; we extend it with data-specific testing, deployment, and validation steps. For teams using GitHub Actions, we publish reusable actions in the marketplace; for teams using GitLab CI, we publish reusable templates. The integration pattern means data engineers use the same CI workflow as software engineers, breaking down the cultural divide that's been a leading cause of data team isolation. For teams without mature CI practices yet, we provide reference templates.

Concretely: all pipelines version-controlled in Git, code-reviewed in PRs, tested in ephemeral environments, deployed via promotion (dev → staging → prod). All datasets have observability (freshness, anomaly detection, schema drift) and lineage-aware alerting. All incidents have runbooks. Per-domain SLAs are measured and reported to business owners. Mean-time-to-detect (MTTD) is under 5 minutes for critical pipelines; mean-time-to-resolve (MTTR) is under 1 hour for high-severity issues. Pipeline change failure rate is under 5%. Cost is attributed to teams and budgeted. These metrics are measured continuously, not aspirationally — the difference between mature DataOps and DataOps theater.