Technical expertise

Architecture and design of Data solutions

Data Lake

A Data Lake is a storage zone that allows you to store all your structured and unstructured data in its native format, at any scale. By keeping your data lake documented and governed, you can explore raw data, visualize it, or even use it to train a machine learning model. Today, the Data Lake is considered an essential component of a modern data architecture. The cloud enables you to deploy such a component quickly, securely, and at a lower cost.

Data Warehousing

A data warehouse is a central repository of information that can be analyzed to make more informed decisions. Unlike the Data Lake, the data warehouse imposes a model that allows you to navigate from one data domain to another. Data flows into a data warehouse from transactional systems, relational databases, and other sources, typically on a regular cadence. The AWS services allow you to build and operate a modern Data Warehouse at scale. Our experts will guide you through the implementation of such a project and are familiar with the various approaches on the market (Kimball, Inmon, Data Vault 2.0 and hybrid approaches).

Data Lakehouse

The Data Lakehouse approach combines the advantages of a data lake (raw data exploration) with those of a data warehouse (formalism, imposed model and structured data). Tools such as AWS S3 and AWS Redshift, Databricks or Snowflake, make it easy to implement such an approach by creating transparency between the lake and the warehouse. Our experts will guide you in the design and deployment of a Data Lakehouse approach.

Big Data and NoSQL

With AWS you can develop virtually any type of Big Data application. Rapidly build highly scalable and secure applications with Hadoop and Spark technologies. No infrastructure to deploy or maintain, everything is serverless on AWS with Glue and EMR.

Depending on the use cases and business needs, NoSQL solutions (time series databases, graph databases, key value databases) can be quickly implemented to manage specific analysis and performance needs.


AWS pre-trained AI tools allow you to quickly and easily build and deploy voice or image processing solutions, predictions or forecasts at a low cost. Our consultants are familiar with the AWS AI eco-system and will support you in implementing such solutions. Let’s mention some of the leading tools such as: Amazon Transcribe, Amazon Comprehend, Amazon Forecast, Amazon Recognition.

For more advanced use cases or needs, Amazon SageMaker will allow you to quickly and easily create, train and deploy machine learning (ML) models at scale for all use cases with fully managed infrastructure, tools and workflows.

Our reference architecture

Based on many years of experience in the design of AWS data solutions, our architects have developed a reference architecture to build a modern data platform. It is based on the AWS best practices (Well Architected Framework – Analytical Lens) and will allow you to address any data related use case, while ensuring a high level of compliancy and security.

Technical mastery of data tools

Our experts have a deep knowledge and mastery of the AWS data and AI/ML ecosystem. Lucy in the Cloud is Data & Analytics certified and Redshift Service Delivery certified.
But our expertise also covers AWS Glue, DMS, MSK, Kinesis, DynamoDB, QuickSight, Sagemaker, MWAA, etc.   In addition, we are partners with several key Data players, such as Snowflake and Databricks.

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