SECTOR

Manufacturing and Energy

ACTIVITY

The customer manufactures steel and other metal products including framework, hand tools, and parts for agricultural machinery.

The project at a glance

The manufacturer required faster processing and analytics capabilities for both cloud-hosted and locally stored data, as part of a broader approach to keep pace with data-related challenges and imperatives:
  • Quickly processing ever-increasing volumes of data of disparate types, and storing this data.
  • Protecting data against breaches and sharing it securely.
  • Introducing fast, smart processes for guaranteeing data quality, integrity, and reliable predictions.
  • Establishing SMART KPIs to optimize stock management, product traceability, production and demand planning, and more.
  • Controlling costs.
  • Continuously monitoring production data and machine performance to detect quality issues and schedule maintenance work.
Contexte et objectif

PROJECT OBJECTIVES

In order to support its quality management and data governance processes, the customer required tools for managing data more quickly and easily, with a view to:
    • Scaling up processing capacity in order to handle increasing data volumes.
    • Reducing analysis time and standardizing the use of the decision-support system (DSS) for faster decision-making on an ever-growing number of matters.
    • Organizing data for greater security.
    • Exploiting data by more easily identifying new data-use opportunities.

WORK PERFORMED

  • Selection of Synapse

    Microsoft’s Synapse analytics platform was selected as the appropriate tool to meet these objectives. Synapse, part of the Azure suite, is an easy-to-administer PaaS service that supports advanced data analytics and large-scale data processing. It offers six key features:
    • Importing raw data into the data lake, addressing the problem of storing large amounts of data from different sources and of different types (files, databases, APIs).
    • Cleaning up and processing validated data, then reintegrating this data into a specific area within the data lake.
    • Registering analysis results and predictive data in a dedicated area of the data lake using an automated data processing system based on machine learning.
    • Exposing the processed data and results via a virtual data warehouse so that it can be shared securely.
    • Periodically importing all Power BI data sets by querying the data warehouse, in order to ensure reports remain up to date.
    • Monitoring logs and metrics via Azure Monitor in order to track all activity and anticipate issues at every stage in the process.

    Overall project approach

    • Scoping: define the architecture, the CI/CD and operating pipeline, and the security and configuration arrangements, and establish a GDPR-compliant work mode for stored data.
    • Installation: establish the cloud infrastructure, configure the components, and complete the technical validation.
    • Operation: implement best practices and produce the documentation.
Missions réalisées
Bénéfices

BENEFITS

  • By using the Synapse platform for data processing and analytics, the customer:
    • Is able to optimize and control costs:
      • The customer is only billed for resources actually used.
      • There are no infrastructure costs to the customer as Synapse is entirely administered by Hardis Group.
    • Has access to a modern, flexible, and scalable data architecture without needing to worry about data-storage constraints. 
    • Benefits from the high-performance security management features offered by Microsoft Azure.
      • Configuration by certified administrators.
      • Access rights for the AAD and RBAC cloud resources managed by Microsoft.
      • GDPR-compliant solution with data stored in French datacenters.
    • Can utilize the usage tracking feature, logs, and alert system provided by Microsoft Azure.
    • Is able to reduce its carbon footprint, since Synapse requires no new electronic components.

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