Enterprise 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, sized for enterprise scale.
Build an enterprise AWS data platform that supports analytics, AI and operations on the same foundation.
Logiciel builds enterprise AWS data platforms for large organisations. Data lakes on S3, warehouses on Redshift, streaming on MSK and Kinesis, governance with Lake Formation, and pipelines that survive change at enterprise scale. We work alongside data, platform and analytics teams to design, build and operate AWS data platforms that business units can build on.
Enterprise AWS data platforms rarely fail on a single technology choice. They fail on the operating layer around them.
We give enterprise data and platform teams an AWS environment they want to operate.
We work across the AWS analytics stack at enterprise scale.
S3-based data lakes with bronze, silver and gold layers, open table formats like Iceberg and Hudi and Lake Formation governance, sized for enterprise scale.
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 across business units.
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 across business units.
Feature stores on SageMaker, training pipelines, vector stores for RAG and inference workloads tied to governed data.
Pipeline monitoring, freshness, volume and schema checks, alerting and SLAs across business units.
Dedicated Enterprise AWS Data Platform Squad
A long-running team of AWS data engineers, platform engineers and analytics specialists embedded in your enterprise data function.
Data Platform Advisory and Staff Augmentation
Senior AWS data architects and engineers who reinforce your enterprise team during build phases.
Outcome-Based Data Platform Engagements
Fixed-scope work for a specific outcome, for example a Redshift migration, a Lake Formation rollout or a streaming pipeline launch across business units.
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.
MSK, Kinesis, Flink on KDA, schema registry and exactly-once patterns.
Lake Formation, Glue Data Catalog, IAM Identity Center, row and column-level security, and audit reporting.
Freshness, volume and schema monitoring, SLA reporting, incident response and on-call.
SageMaker feature stores, RAG architectures, vector stores and integration with Bedrock and SageMaker pipelines.
Patterns from our delivery teams that have run through real enterprise deployments.
A practical lakehouse pattern that combines S3, Iceberg, Redshift, dbt and Lake Formation for governed analytics across business units.
A production pattern for CDC, event streaming and real-time analytics on MSK, Kinesis and Flink.
1. Discovery and Use Case Mapping
We map the business use cases, current data estate, governance constraints and cost expectations across business units.
2. Target Architecture and Roadmap
We design the AWS data architecture, choose patterns per use case and agree on a phased roadmap.
3. Platform Build
We build the platform in code, including storage, compute, orchestration, governance and observability.
4. Use Case Onboarding Across Business Units
We onboard the first BI, analytics and ML use cases with data contracts, SLAs and access patterns.
5. Operate and Scale
We move into a steady-state operating model and widen the platform across business units and product lines.
Ready to put AWS Data Platform Services for Enterprise on production-software footing? Partner with Logiciel to design, build and operate AWS Data Platform Services for Enterprise that engineering, security and business teams can all defend.
We cover strategy, architecture, build, deployment and operations for AWS Data Platform Services 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.