Data Observability Platform Implementation
Implementation of Monte Carlo, Soda, Bigeye, Datadog or open-source observability platforms, tuned to your stack.
Bring enterprise data under the same kind of observability you already run for applications.
Logiciel implements enterprise data observability across pipelines, warehouses, lakehouses and AI workloads. Monte Carlo, Soda, Bigeye, Datadog or open-source patterns, plus the SLAs, lineage and incident response practice around them. We work alongside data platform, analytics and reliability teams to make data incidents visible, fixable and accountable.
Most enterprises know they need data observability. The implementation gets stuck on the same patterns.
We give enterprise data platform teams an observability practice they can actually run.
A clear definition of what is in scope, including critical data products, AI workloads and operational data flows.
An observability platform implementation tuned to your enterprise stack.
SLAs per data product, with freshness, volume, schema and quality thresholds tied to business impact.
Lineage across pipelines, warehouses, lakehouses and AI workloads.
An incident response practice with named owners across data engineering, analytics and product.
A documented operating model with reviews, dashboards and KPIs.
We cover the observability areas that recur across large enterprises.
Implementation of Monte Carlo, Soda, Bigeye, Datadog or open-source observability platforms, tuned to your stack.
SLA design per critical data product, including freshness, volume, schema and quality thresholds tied to business impact.
Observability across pipelines, warehouses and lakehouses on Snowflake, Databricks, Redshift, BigQuery and lakehouse architectures.
Observability for streaming and CDC pipelines on Kafka, Kinesis, MSK, Pub/Sub and Flink.
Observability for AI workloads, including data and feature inputs, retrieval quality and downstream model behaviour.
End-to-end lineage from source to consumption, including AI workloads, with impact analysis for incidents and changes.
Incident response practice with named owners, runbooks, post-mortems and KPIs across data engineering, analytics and product.
Dedicated Data Observability Squad
A long-running team of data engineers, reliability engineers and platform engineers embedded in your data platform function.
Data Observability Advisory and Staff Augmentation
Senior data reliability engineers who reinforce your in-house team during specific phases.
Outcome-Based Observability Engagements
Fixed-scope work, for example an observability platform implementation, a data SLA rollout or an incident response practice setup.
Implementation of Monte Carlo, Soda, Bigeye, Datadog or open-source observability platforms.
SLAs per critical data product, with thresholds tied to business impact.
Observability across pipelines, warehouses and lakehouses on Snowflake, Databricks, Redshift, BigQuery and lakehouse architectures.
Observability for streaming and CDC pipelines on Kafka, Kinesis, MSK, Pub/Sub and Flink.
Observability for AI workloads, including data and feature inputs, retrieval quality and downstream model behaviour.
End-to-end lineage from source to consumption, including AI workloads, with impact analysis.
Incident response practice with named owners, runbooks, post-mortems and KPIs.
Roles, processes, cadences and KPIs for an enterprise data observability practice.
Patterns from our delivery teams that have run through real enterprise deployments.
Enterprise Data Observability Operating Model
A reference for roles, processes, cadences and KPIs for an enterprise data observability practice.
Data Product SLA Framework
A practical framework for SLA design per data product, with thresholds tied to business impact.
1. Discovery and Scoping
We map data products, AI workloads, operational data flows and current observability gaps.
2. Operating Model and SLA Design
We design the operating model, SLAs per data product and incident response practice.
3. Platform Implementation
We implement the observability platform, integrate with pipelines and warehouses and tune alerts.
4. Rollout and Incident Practice
We roll out across data products, establish on-call and run the first incident reviews.
5. Operate and Improve
We move into a steady-state operating model with reviews, dashboards and KPIs.
Ready to treat Data Observability Solutions for Enterprise as production engineering instead of a side project? Partner with Logiciel to design, build and operate Data Observability Solutions for Enterprise that engineering, security and business teams can all defend.
We cover strategy, architecture, build, deployment and operations for Data Observability Solutions for Enterprise, aligned with your business priorities and operating constraints.
Most engagements reach a working pilot within 4-8 weeks, while larger rollouts run across phased waves over several months.
Yes. We integrate with cloud platforms, CRMs, ERPs, EHR, OT systems, analytics tools and other operational infrastructure depending on the use case.
Yes. We offer milestone-based pricing once scope, KPIs and delivery requirements are agreed.
You retain ownership of all workflows, integrations, prompts, infrastructure, systems and implementation assets.
We implement governance frameworks, observability, access controls, audit trails and compliance-aligned deployment practices.
We tune infrastructure, automate resource management, optimise deployment workflows and report operational cost back to teams and product lines.
Yes. We run managed operations with SRE, observability, on-call and continuous improvement.