SOVEREIGN DATA ANALYTICS WITHOUT COMPROMISE: MANAGED APACHE SPARK AND APACHE AIRFLOW JOIN BIGQUERY ON PREMI3NS

Published on 10/27/2026

French public institutions, regulated enterprises, and Vital National Operators (Opérateurs d'Importance Vitale, or OIVs) generate hundreds of terabytes of operational, sensor, and transactional data every day. A robust, high-quality data foundation is the essential bedrock of any AI platform, serving as the raw fuel that powers intelligent algorithms, drives accurate predictions, and ultimately determines the system's overall capability and reliability.

Building a sovereign data platform shouldn't mean stepping backward in technology. While European public institutions and critical operators generate massive volumes of data daily, their digital ambitions have historically been bottlenecked by fragmented tools and manual infrastructure management. PREMI3NS is helping to change that reality.

Today, we are announcing the availability of Managed Service for Apache Spark (formerly Dataproc) and Managed Service for Apache Airflow (formerly Cloud Composer) on PREMI3NS. Together with BigQuery, these services complete the core Cloud Data Platform on PREMI3NS. The formal notification (J0) to extend our SecNumCloud qualification to cover these specific data services has been sent to ANSSI and the process is now actively underway.

Three core engines for sovereign data pipelines

Every modern data platform requires three foundational capabilities:

petabyte-scale analytics
distributed data processing
end-to-end orchestration.

Running all three as managed services inside the PREMI3NS perimeter eliminates the operational tax of self-hosted software:

Managed Service for Apache Spark (formerly Dataproc): Provision ephemeral, right-sized Apache Spark and Apache Hadoop clusters in under 90 seconds, compared to 20 to 30 minutes on traditional cloud or self-managed infrastructure. Engineering teams can run existing PySpark, Scala, Java, or Spark SQL jobs for Extract, Transform, and Load (ETL) processing and machine learning without re-architecting code, reading and writing directly to Cloud Storage and BigQuery via high-throughput native connectors.

Managed Service for Apache Airflow (formerly Cloud Composer): Schedule, orchestrate, and monitor Python-based Directed Acyclic Graphs (DAGs) that execute data pipelines across on-premises sources, Cloud Storage, Managed Service for Apache Spark, and BigQuery. Managed Service for Apache Airflow removes the infrastructure burden of managing Kubernetes clusters, relational metadata databases, and underlying infrastructure security patching, while providing configurable scheduled snapshots and built-in worker autoscaling that dynamically adjusts compute to pipeline demand without overprovisioning for peak workloads.

BigQuery (Serverless Data Warehouse): Analyze structured, semi-structured JSON, and geospatial data at petabyte scale with zero infrastructure management. BigQuery separates distributed columnar storage from dynamic compute to scale automatically per query, executing complex multi-table joins and high-throughput streaming ingestion without manual indexing, vacuuming, or cluster sizing. Because BigQuery on PREMI3NS exposes the exact same GoogleSQL syntax, REST APIs, command-line interface (bq), and client libraries as Google Cloud, alongside JDBC and ODBC drivers that natively support the sovereign universe domain configuration, teams can run existing analytical workloads and connect their familiar Business Intelligence (BI) tools without rewriting a single query.

In addition, built-in BigQuery Machine Learning (BigQuery ML) lets analysts train and run internal machine learning models directly inside the warehouse using standard SQL, including regression, clustering, matrix factorization, contribution analysis, and time-series forecasting and anomaly detection, without moving sensitive data outside the warehouse perimeter.

Outperforming self-managed open-source stacks

In many sovereign cloud environments, organizations are forced to assemble standalone open-source tools, such as self-managed Apache Spark, self-hosted Apache Airflow on Kubernetes, or single-purpose analytical engines like ClickHouse and Trino. While open-source software provides code portability, operating these stacks yourself introduces hidden costs and security gaps:

Lower total cost of ownership through serverless compute: Self-managed analytical databases and Spark clusters require static provisioning for peak load, meaning you pay for idle servers around the clock. On PREMI3NS, BigQuery charges only for compute consumed during query execution, and Managed Service for Apache Spark spins up ephemeral clusters on demand and tears them down the moment a job finishes.

Automated performance optimization: Standalone open-source analytical engines require tuning hundreds of configuration parameters and often degrade on complex multi-table joins. BigQuery and Managed Service for Apache Spark automatically optimize query execution plans and shuffle operations across petabyte-scale datasets.

Built-in SecNumCloud security and governance: Stitching together open-source tools leaves your security team responsible for configuring role-based access control, log pipelines, and key management across disparate systems. On PREMI3NS, BigQuery, Managed Service for Apache Spark, and Managed Service for Apache Airflow integrate natively with Identity and Access Management (IAM), Virtual Private Cloud (VPC) Service Controls and Cloud Logging, managed and operated exclusively by S3NS.

Real-world data applications on PREMI3NS

Combining BigQuery, Managed Services for Apache Spark and Airflow enables regulated organizations to move sensitive workloads from raw ingestion to production AI within a single French-operated perimeter:

Insurance | Automated actuarial risk and claims pipelines: Insurers can use Managed Service for Apache Airflow to schedule nightly ingestion of policyholder records, process unstructured claims documents at scale with PySpark on Managed Service for Apache Spark, and consolidate structured risk indicators in BigQuery, accelerating digital service delivery under strict European data protection rules.

Financial services | High-volume fraud and regulatory reporting: Banks and payment operators can run distributed Apache Spark jobs on Managed Service for Apache Spark to score millions of daily transactions and join them with historical ledger data in BigQuery, generating auditable reports for regulators without exposing transaction telemetry outside France.

Energy, utilities, and healthcare | Feeding sovereign AI models: Industrial and clinical teams can normalize terabytes of sensor logs or anonymized health records using Managed Service for Apache Spark and BigQuery, then use built-in BigQuery Machine Learning (BigQuery ML) models for predictive maintenance and clinical decision support.

Start building your sovereign data platform

You no longer have to choose between the rapid innovation and scale of a global hyperscaler, and the robust security of an ANSSI SecNumCloud-qualified platform.

Explore the PREMI3NS technical documentation to review service specifications for BigQuery, Managed Service for Apache Spark, and Managed Service for Apache Airflow, or contact the S3NS team to schedule an architecture walkthrough and migrate your first Apache Spark and Apache Airflow workloads.

Blaise Vignon

Product & Marketing Director at S3NS