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Data Lake: how to manage large volumes of data for AI, simulations, and Digital Twins

Many Artificial Intelligence, simulation, or Digital Twin projects stop short of delivering real value. Not because of a lack of models or skills, but because data is not accessible, reusable, or manageable over time.

The Data Lake was created to address this problem. It is not a trend-driven technology, but an infrastructure designed to collect, store, and use large volumes of heterogeneous data without forcing them into rigid schemas.

This is why the Data Lake is often described as the “back room” of Artificial Intelligence systems: the place where data remains available, searchable, and ready to be used for analysis, simulations, and advanced models.

What a Data Lake is and why it matters

A Data Lake allows raw data to be stored in its original format. Text, images, simulation outputs, sensor data, scientific files, and documents can coexist in the same environment without an imposed structure at ingestion time.

Structure is applied only when the data is read. This approach makes the system more flexible than traditional databases and allows the same data to be reused for different purposes over time, even as requirements change.

Data access is enabled through metadata, which supports fast and selective searches even across very large data volumes.

Why innovators actually use it

In industrial environments, Data Lakes are used to collect operational data, sensor streams, images, and technical documentation in a single place. This enables historical analysis, predictive maintenance, system integration, and the use of data in simulation models.

For startups, a Data Lake avoids repeated redesigns of the data infrastructure. Datasets grow, evolve, and are reused for new models without rebuilding the system each time.

In Public Administration, Data Lakes often underpin Digital Twin projects, territorial management, mobility, energy systems, and environmental monitoring. Managing static and dynamic data together is a practical requirement, not a theoretical one.

In research organisations, Data Lakes make it possible to store large volumes of experimental and simulation data and connect them directly to high-performance computing systems for analysis and model validation.

The role of HPC in the IT4LIA Data Lake

A Data Lake becomes especially effective when it is integrated with high-performance computing. Within the IT4LIA context, stored data can be processed directly on supercomputers, without the need to move or duplicate it.

This approach reduces processing time, simplifies workflows, and enables analyses that would otherwise require significantly greater resources and specialised expertise on standard infrastructures.

Typical use cases include scientific simulations, AI model training on large datasets, and continuous updates of databases used in Retrieval Augmented Generation RAG systems.

Data Lake, RAG, and language models

Retrieval Augmented Generation systems only work when source data is accessible, updatable, and versioned. The Data Lake serves as the foundation for storing original documents, generating embeddings, updating vector databases, and tracking different versions over time.

In the model described in the white paper, the Data Lake hosts both raw data and generated artefacts, making the overall process easier to control and reproduce.

Digital Twins and real-world data

A Digital Twin requires heterogeneous data that is continuously updated. Static information and dynamic data streams must coexist and remain available for ongoing analysis and simulation.

The Data Lake provides this continuity by allowing new data to be added without disrupting existing processes and by automating access to information when it is needed.

The IT4LIA AI Factory Data Lake service

IT4LIA AI Factory, in collaboration with EuroCC Italy, provides support for using the Data Lake to store and access simulation datasets, integrating it with High Performance Computing infrastructures.

The service Simulation Data Lake (SDL) for scientific simulation data management is designed to support companies, startups, public administrations, and research organisations in the practical use of Data Lakes, from data access to integration within AI and simulation workflows.

Download white paper

If you are considering the use of a Data Lake for simulations, AI, or Digital Twins but are unsure where to start download the white paper “Data Lake: the back room of Artificial Intelligence”.





    Resources

    Data Lake: how to manage large volumes of data for AI, simulations, and Digital Twins

    Many Artificial Intelligence, simulation, or Digital Twin projects stop short of delivering real value. Not because of a lack of models or skills, but because data is not accessible, reusable, or manageable over time.

    The Data Lake was created to address this problem. It is not a trend-driven technology, but an infrastructure designed to collect, store, and use large volumes of heterogeneous data without forcing them into rigid schemas.

    This is why the Data Lake is often described as the “back room” of Artificial Intelligence systems: the place where data remains available, searchable, and ready to be used for analysis, simulations, and advanced models.

    What a Data Lake is and why it matters

    A Data Lake allows raw data to be stored in its original format. Text, images, simulation outputs, sensor data, scientific files, and documents can coexist in the same environment without an imposed structure at ingestion time.

    Structure is applied only when the data is read. This approach makes the system more flexible than traditional databases and allows the same data to be reused for different purposes over time, even as requirements change.

    Data access is enabled through metadata, which supports fast and selective searches even across very large data volumes.

    Why innovators actually use it

    In industrial environments, Data Lakes are used to collect operational data, sensor streams, images, and technical documentation in a single place. This enables historical analysis, predictive maintenance, system integration, and the use of data in simulation models.

    For startups, a Data Lake avoids repeated redesigns of the data infrastructure. Datasets grow, evolve, and are reused for new models without rebuilding the system each time.

    In Public Administration, Data Lakes often underpin Digital Twin projects, territorial management, mobility, energy systems, and environmental monitoring. Managing static and dynamic data together is a practical requirement, not a theoretical one.

    In research organisations, Data Lakes make it possible to store large volumes of experimental and simulation data and connect them directly to high-performance computing systems for analysis and model validation.

    The role of HPC in the IT4LIA Data Lake

    A Data Lake becomes especially effective when it is integrated with high-performance computing. Within the IT4LIA context, stored data can be processed directly on supercomputers, without the need to move or duplicate it.

    This approach reduces processing time, simplifies workflows, and enables analyses that would otherwise require significantly greater resources and specialised expertise on standard infrastructures.

    Typical use cases include scientific simulations, AI model training on large datasets, and continuous updates of databases used in Retrieval Augmented Generation RAG systems.

    Data Lake, RAG, and language models

    Retrieval Augmented Generation systems only work when source data is accessible, updatable, and versioned. The Data Lake serves as the foundation for storing original documents, generating embeddings, updating vector databases, and tracking different versions over time.

    In the model described in the white paper, the Data Lake hosts both raw data and generated artefacts, making the overall process easier to control and reproduce.

    Digital Twins and real-world data

    A Digital Twin requires heterogeneous data that is continuously updated. Static information and dynamic data streams must coexist and remain available for ongoing analysis and simulation.

    The Data Lake provides this continuity by allowing new data to be added without disrupting existing processes and by automating access to information when it is needed.

    The IT4LIA AI Factory Data Lake service

    IT4LIA AI Factory, in collaboration with EuroCC Italy, provides support for using the Data Lake to store and access simulation datasets, integrating it with High Performance Computing infrastructures.

    The service Simulation Data Lake (SDL) for scientific simulation data management is designed to support companies, startups, public administrations, and research organisations in the practical use of Data Lakes, from data access to integration within AI and simulation workflows.

    Download white paper

    If you are considering the use of a Data Lake for simulations, AI, or Digital Twins but are unsure where to start download the white paper “Data Lake: the back room of Artificial Intelligence”.