AI-first engineering

AWS Data Platform Services.

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.

Book an Intro Call Common Questions

2 frameworks

Reference architectures for AWS data

6 outcomes

What you get from an AWS data platform

Why Logiciel

Why Enterprise Data Platforms on AWS Get Stuck.

Why Logiciel · 01

Pipelines are owned by individuals, not teams.

Why Logiciel · 02

Data quality issues are caught by business users, not the platform.

Why Logiciel · 03

Governance arrives as a slide deck instead of a control plane.

Why Logiciel · 04

Storage costs grow faster than analytical value.

Why Logiciel · 05

BI dashboards and ML pipelines run on the same Redshift cluster and fight each other.

Why Logiciel · 06

Nobody can answer where a number on a board report came from.

What you get

What You Get When You Work With Logiciel on AWS Data.

A modern AWS data architecture with clear separation between storage, processing and consumption.

Pipelines built in code, with tests, observability and lineage.

Lake Formation governance, fine-grained access and audit-ready logs.

Cost reports that map storage and compute back to teams and use cases.

A platform that supports BI, machine learning and product analytics without conflicts.

A documented operating model that data engineers can run after handover.

Technology

AWS Data Platform Solutions Built for Enterprise Scale.

Data Lake on S3

What it meansS3-based data lakes with bronze, silver and gold layers, open table formats like Iceberg and Hudi, and Lake Formation governance.

Cloud Data Warehouse on Redshift

What it meansRedshift Serverless and provisioned clusters, with workload management, materialised views and federated queries.

ETL and ELT with Glue and dbt

What it meansAWS Glue, dbt on Redshift and Athena, Step Functions and Airflow on MWAA for orchestration.

Streaming Data on MSK and Kinesis

What it meansReal-time pipelines for events, telemetry and CDC using MSK, Kinesis Data Streams, Kinesis Firehose and Flink on KDA.

Data Governance and Cataloguing

What it meansLake Formation, Glue Data Catalog, fine-grained access control, lineage with OpenLineage and audit-ready logging.

Machine Learning and AI on the Data Platform

What it meansFeature stores on SageMaker, training pipelines, vector stores for RAG and inference workloads tied to governed data.

Engagement

Engagement Models Designed for AWS Data Platform Services Delivery.

Dedicated AWS Data Platform Squad

A long-running team of AWS data engineers, platform engineers and analytics specialists embedded in your data function.

↳ Engagement

Data Platform Advisory and Staff Augmentation

Senior AWS data architects and engineers who reinforce your internal team during build phases.

↳ Engagement

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.

↳ Engagement

Under the hood

AWS Data Platform Services We Deliver.

AWS Data Architecture and Strategy

Reference architectures, maturity assessments and multi-year data platform roadmaps.

Included

AWS Data Lake Implementation

S3-based data lakes with Iceberg or Hudi, partitioning, compaction, governance and access patterns.

Included

Redshift and Lakehouse Engineering

Redshift Serverless, provisioned clusters, workload tuning, dbt models and federated queries across Redshift and S3.

Included

ETL and ELT Pipeline Engineering

Glue, MWAA, Step Functions, dbt and Spark-on-EMR pipelines with testing, lineage and observability.

Included

Streaming Data Pipelines on AWS

MSK, Kinesis, Flink on KDA, schema registry, exactly-once patterns and integration with downstream warehouses.

Included

AWS Data Governance and Lake Formation

Lake Formation, Glue Data Catalog, IAM Identity Center, row and column-level security, and audit reporting.

Included

Technology

AWS Data Platform Services Insights & Frameworks.

Patterns from our delivery teams that have run through real enterprise data programmes.

Technology

Enterprise AWS Lakehouse Reference Architecture

A practical lakehouse pattern that combines S3, Iceberg, Redshift, dbt and Lake Formation for governed analytics.

Technology

AWS Streaming Data Platform Pattern

A production pattern for CDC, event streaming and real-time analytics on MSK, Kinesis and Flink.

Technology

Our AWS Data Platform Services Framework.

Discovery and Use Case Mapping

We map the business use cases, current data estate, governance constraints and cost expectations.

Target Architecture and Roadmap

We design the AWS data architecture, choose patterns per use case and agree on a phased roadmap.

Platform Build

We build the platform in code, including storage, compute, orchestration, governance and observability.

Use Case Onboarding

We onboard the first BI, analytics and ML use cases, including data contracts, SLAs and access patterns.

Operate and Scale

We move into a steady-state operating model and widen the platform across business units and use cases.

Resources

Insights for tech leaders.

Whitepaper · PDF

How a VP of Data Got a Modern Data Platform Funded in Two Board Meetings

Whitepaper · PDF

Building AI-Ready Healthcare Data Infrastructure: The 90-Day Roadmap from Staging Bottleneck to Production

Whitepaper · PDF

How an Energy Company Stopped Paying for Silent Data Quality Failures

Questions

Frequently asked questions.

What does AWS Data Platform Services include?

We cover strategy, architecture, build, deployment and operations for AWS Data Platform Services, aligned with your business priorities and operating constraints.

How long does AWS Data Platform Services typically take?

Most engagements reach a working pilot within 4-8 weeks, while larger rollouts run across phased waves over several months.

Can Logiciel integrate AWS Data Platform Services with our existing systems?

Yes. We integrate with cloud platforms, CRMs, ERPs, EHR, OT systems, analytics tools and other operational infrastructure depending on the use case.

Do you offer fixed-cost engagements for AWS Data Platform Services?

Yes. We offer milestone-based pricing once scope, KPIs and delivery requirements are agreed.

Who owns the deliverables from a AWS Data Platform Services engagement?

You retain ownership of all workflows, integrations, prompts, infrastructure, systems and implementation assets.

How do you handle governance and compliance for AWS Data Platform Services?

We implement governance frameworks, observability, access controls, audit trails and compliance-aligned deployment practices.

How do you optimize cost for AWS Data Platform Services?

We tune infrastructure, automate resource management, optimise deployment workflows and report operational cost back to teams and product lines.

Do you support ongoing operations after launch for AWS Data Platform Services?

Yes. We run managed operations with SRE, observability, on-call and continuous improvement.

Let's build

Accelerate AWS Data Platform Services.

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.

Design your AWS data platform

AWS Data Platform Services.

See Logiciel in action.

Why Enterprise Data Platforms on AWS Get Stuck.

Pipelines are owned by individuals, not teams.

Data quality issues are caught by business users, not the platform.

Governance arrives as a slide deck instead of a control plane.

Storage costs grow faster than analytical value.

BI dashboards and ML pipelines run on the same Redshift cluster and fight each other.

Nobody can answer where a number on a board report came from.

What You Get When You Work With Logiciel on AWS Data.

AWS Data Platform Solutions Built for Enterprise Scale.

Data Lake on S3

Cloud Data Warehouse on Redshift

ETL and ELT with Glue and dbt

Streaming Data on MSK and Kinesis

Data Governance and Cataloguing

Machine Learning and AI on the Data Platform

Engagement Models Designed for AWS Data Platform Services Delivery.

Dedicated AWS Data Platform Squad

Data Platform Advisory and Staff Augmentation

Outcome-Based Data Platform Engagements

AWS Data Platform Services We Deliver.

AWS Data Architecture and Strategy

AWS Data Lake Implementation

Redshift and Lakehouse Engineering

ETL and ELT Pipeline Engineering

Streaming Data Pipelines on AWS

AWS Data Governance and Lake Formation

AWS Data Platform Services Insights & Frameworks.

Patterns from our delivery teams that have run through real enterprise data programmes.

Enterprise AWS Lakehouse Reference Architecture

AWS Streaming Data Platform Pattern

Our AWS Data Platform Services Framework.

Discovery and Use Case Mapping

Target Architecture and Roadmap

Platform Build

Use Case Onboarding

Operate and Scale

Insights for tech leaders.

How a VP of Data Got a Modern Data Platform Funded in Two Board Meetings

Building AI-Ready Healthcare Data Infrastructure: The 90-Day Roadmap from Staging Bottleneck to Production

How an Energy Company Stopped Paying for Silent Data Quality Failures

Frequently asked questions.

Accelerate AWS Data Platform Services.