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GfK Data Ingestion via SAML

Automated GfK data retrieval behind a SAML-protected login.

SAML login with credentials from Azure Key Vault, automatic file discovery and Databricks ingestion of new files into Azure Data Lake.

NDA 2024

Sales & Marketing FMCG Connector / Integration Accelerators
Conceptual diagram of GfK market-data ingestion using SAML authentication, automated file discovery, Databricks and Azure Data Lake; not a customer screenshot.
Conceptual illustration of the ingestion flow - not a screenshot of the client system. Open full image

Challenge

GfK data had to be downloaded manually from a website with a SAML-federated login.

Delivered

A fully automated workflow for SAML login, file discovery and ingestion.

Enabled

New GfK files land in Azure Data Lake without manual steps or secrets in code.

Challenge

GfK, a leading market research company, provides critical consumer goods data - but accessing it required manual downloads via a website, with login flow protected by SAML federation. Sourcing and integrating that data into a modern analytics layer was cumbersome and error-prone, and the manual step blocked any attempt at automation.

Approach

We built a fully automated workflow that handles SAML login, file discovery, and ingestion end-to-end. Authentication uses Python's Mechanize library to drive the GfK Federation login flow programmatically, with credentials retrieved at runtime from Azure Key Vault - secrets never live in code or config files.

Once authenticated, the system uses BeautifulSoup to scan the GfK Connect portal and extract the list of available data files automatically, eliminating the need for manual discovery. A Databricks Compute Cluster then orchestrates ingestion: it filters out files already processed and transfers only new data into the Azure Data Lake - efficient, scalable, and idempotent.

  1. 01

    Authenticate

    Python’s Mechanize drives the GfK Federation login flow, with credentials retrieved at runtime from Azure Key Vault.

  2. 02

    Discover

    BeautifulSoup scans the GfK Connect portal and extracts the list of available data files.

  3. 03

    Ingest

    A Databricks Compute Cluster filters out files already processed and transfers only new data into Azure Data Lake.

Outcomes

Qualitative project outcomes; no measured savings are claimed.

  • Fully automated and secure data retrieval, eliminating the manual download step

  • Seamless integration with Azure cloud storage and downstream processing

  • Enhanced security: credentials managed via Azure Key Vault, never in source

  • Scalable architecture that adapts to growing data volumes and additional GfK feeds

  • Reproducible pattern reusable for any SAML-protected data source

SAML authentication Python (Mechanize, BeautifulSoup) Azure Key Vault Databricks Azure Data Lake

Solution areas: Data Foundations

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