Data Lake on S3
S3-based data lakes with bronze, silver and gold layers, open table formats like Iceberg and Hudi, and Lake Formation governance.
A data platform on AWS that your business can actually trust.
Logiciel builds AWS data platforms that hold up under real workloads. Data lakes on S3, warehouses on Redshift, streaming on MSK and Kinesis, governance with Lake Formation, and pipelines that do not break at 3am.
Data platforms rarely fail on a single technology choice. They fail on the operating layer around them.
We give data and platform teams an environment they want to operate.
We work across the AWS analytics stack. The right pattern depends on your workloads.
S3-based data lakes with bronze, silver and gold layers, open table formats like Iceberg and Hudi, and Lake Formation governance.
Redshift Serverless and provisioned clusters, with workload management, materialised views and federated queries.
AWS Glue, dbt on Redshift and Athena, Step Functions and Airflow on MWAA for orchestration.
Real-time pipelines for events, telemetry and CDC using MSK, Kinesis Data Streams, Kinesis Firehose and Flink on KDA.
Lake Formation, Glue Data Catalog, fine-grained access control, lineage with OpenLineage and audit-ready logging.
Feature stores on SageMaker, training pipelines, vector stores for RAG and inference workloads tied to governed data.
A long-running team of AWS data engineers, platform engineers and analytics specialists embedded in your data function.
Senior AWS data architects and engineers who reinforce your internal team during build phases.
Fixed-scope work for a specific outcome, for example a Redshift migration, a Lake Formation rollout or a streaming pipeline launch.
Reference architectures, maturity assessments and multi-year data platform roadmaps.
S3-based data lakes with Iceberg or Hudi, partitioning, compaction, governance and access patterns.
Redshift Serverless, provisioned clusters, workload tuning, dbt models and federated queries across Redshift and S3.
Glue, MWAA, Step Functions, dbt and Spark-on-EMR pipelines with testing, lineage and observability.
MSK, Kinesis, Flink on KDA, schema registry, exactly-once patterns and integration with downstream warehouses.
Lake Formation, Glue Data Catalog, IAM Identity Center, row and column-level security, and audit reporting.
Patterns from our delivery teams that have run through real enterprise data programmes.
Enterprise AWS Lakehouse Reference Architecture
A practical lakehouse pattern that combines S3, Iceberg, Redshift, dbt and Lake Formation for governed analytics.
AWS Streaming Data Platform Pattern
A production pattern for CDC, event streaming and real-time analytics on MSK, Kinesis and Flink.
We map the business use cases, current data estate, governance constraints and cost expectations.
We design the AWS data architecture, choose patterns per use case and agree on a phased roadmap.
We build the platform in code, including storage, compute, orchestration, governance and observability.
We onboard the first BI, analytics and ML use cases, including data contracts, SLAs and access patterns.
We move into a steady-state operating model and widen the platform across business units and use cases.
Ready to put AWS Data Platform Services on production-software footing? Partner with Logiciel to design, build and operate AWS Data Platform Services that engineering, security and business teams can all defend.
We cover strategy, architecture, build, deployment and operations for AWS Data Platform Services, 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.