Bidatia

SERVICES

Data platforms

The same team designs, builds and runs the platform. We take data end to end, from source to report or AI model, with quality, traces and cost under control, so your teams can trust every figure.

Tell us about your projectHow we do it

Official partner Microsoft Azure

Does any of this sound familiar?

  • Data in silos

    ERP, CRM, files and applications that don't talk to each other, and manual loads every week.

  • A platform that doesn't scale

    Overnight jobs that never finish, cloud costs growing with no explanation and nobody who understands the whole.

  • AI has nothing to draw on

    Without clean, integrated, governed data, AI projects never get past the demo.

HOW WE HELP

What we help you solve with this service

Turning data into decisions

«We have data, but it's hard to turn it into useful information for the business.»

  1. We define the indicators with the people who decide, once.
  2. We integrate data from your systems into a single reliable model.
  3. Clear dashboards and self-service for every department.
Related servicesData and analytics

Strategic reporting

«We need a complete view of the operation, not twenty separate reports.»

  1. We define a balanced scorecard with management: objectives, indicators and owners for each area.
  2. We bring data from across the operation into a single, governed and reliable model.
  3. Ready to be exploited with AI: natural-language questions, alerts and root-cause analysis.
Related servicesData and analyticsData governance

Connecting and modernising systems

«We have Excel, applications, an ERP and other disconnected systems, or processes that are barely digitalised.»

  1. A data platform that integrates your systems and eliminates manual loads.
  2. Odoo adapted to your processes, with customer and supplier portals.
  3. Migrations to Fabric, Databricks or Snowflake without stopping the business.
Related servicesOdoo and business digitalisationAI-powered development

DATA PLATFORMS

What we do

  • Data end to end

    Sources, batch and streaming ingestion, layered model (bronze, silver, gold), warehouse or lakehouse, and consumption via BI or API.

  • Fabric, Databricks or Snowflake

    Specialist teams for all three. We choose by data, cost and ecosystem, not preference.

  • On-premise or cloud

    In your own data centre or on Azure, Google Cloud and AWS, with identity, network and cost under control.

  • Power BI to Fabric migration

    Licensing and capacity, Spark integration, governance with Purview and model redesign.

  • DataOps and quality

    Versioned code, continuous deployment, data tests and observability with AlmIA.

  • Production support

    Evolutionary and corrective maintenance by the same team that built the platform.

How we do it

  1. Target architecture

    Inventory of sources, volumes, latencies and uses; platform choice with estimated cost.

  2. Platform foundations

    Identity, network, environments, repository, continuous deployment and the first ingestions.

  3. Data products

    Layered domains, with quality, lineage and documentation from day one.

  4. Operations

    Monitoring, cost control, support and evolution with new use cases.

Illustrative mock-up

What you get

  • Documented reference architecture and platform decision
  • Platform deployed as code, with environments and CI/CD
  • Ingestions and layered model for the priority domains
  • Quality tests, lineage and monitoring
  • Operations runbooks and support plan

What it brings to each role

A platform sized to your business, with predictable cost and no dependence on a single person.

Technology

  • Microsoft Fabric
  • Databricks
  • Snowflake
  • Azure
  • AWS
  • Google Cloud
  • Spark
  • dbt
  • Informatica
  • Kafka
  • Delta Lake
  • Terraform
  • Azure DevOps
  • GitHub

Related technologies

Related case studies

See all case studies

  • Diversified business holding

    From SAP to a lakehouse in Microsoft Fabric

    Challenge
    Reports spread across SAP R/3, SAP BusinessObjects, several ERPs and CRMs. Without a single analytics environment, there was no way to add advanced analytics or contain licensing costs.
    Solution
    Analysis of the reports, roadmap and management model; a new architecture, migration of the developments to a lakehouse in Fabric with integrity validation, auditing and load control, row- and column-level security for sensitive data, and data governance implemented on Azure with Purview.
    Result
    Centralised reports, lower licensing and development costs, an analytics layer for self-service and data services for third parties, and a foundation ready for machine learning.
    Data platforms

  • Real-time digital advertising company

    Migration to a cloud-native architecture with Databricks

    Challenge
    The data and analytics platform was not keeping pace with business growth: integration and reporting processes needed modernising, and the teams had no experience with the new technology.
    Solution
    Target architecture on AWS, migration of the integration and reporting processes to Databricks, and knowledge transfer to the teams.
    Result
    Scalable storage with orchestrated pipelines, a foundation ready for machine learning, and the client's teams running operations autonomously.
    Data platforms

  • Public university

    Governance portal for a federated data space

    Challenge
    The use cases depended on proprietary GIS technology, creating barriers to entry for new municipalities, and the chain of trust and the catalogue had to be managed autonomously.
    Solution
    A neutral governance portal, software for the provider, promoter and consumer roles, a federated catalogue on EDC and the DSP protocol, and GAIA-X credentials.
    Result
    New municipalities join without GIS licences, and the governance logic lives in the data space itself.
    Data platforms

FAQ

Frequently asked questions

Straight answers to what we're asked most.

Which data platform suits me: Fabric, Databricks or Snowflake?

It depends on your ecosystem, volume, team and cost. We have specialist teams for all three and choose by data, cost and ecosystem, not preference.

Do you run the platform after building it?

Yes. We provide production support and DataOps with the same team that built it, and we train your team for self-service.

Do you work on-premise?

Yes. We deploy in your data centre when regulation, cost or latency make it advisable, and in hybrid architectures with the cloud.

How do you control cloud costs?

With initial sizing, tagging by domain and project, consumption alerts and regular reviews of capacity and workloads.

Shall we talk about your project?

A first 30-minute conversation, no strings attached.

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