Data: Streamlining and Consolidating Enterprise Data Operations


A large health-based agency sought expertise in expanding/replacing and streamlining operations as part of the organization’s data modernization initiatives.  The goal was to consolidate the multiple, stove-piped systems and siloed data into a centralized model allowing easier information exchange and predictive analytics of existing pathogen-specific data as well as the influx of COVID-19 pandemic response-related data.

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CTAC assisted in the effort to consolidate the individual data projects into a unified data lake using Azure Cloud and Databricks. Here’s how we achieved it:

  1. Azure Cloud Infrastructure: CTAC leveraged Microsoft Azure’s robust cloud infrastructure to establish a secure and scalable foundation for the data lake. Azure’s capabilities allowed us to ensure data security, compliance, and high availability.
  2. Databricks as the Analytics Platform: Databricks was chosen as the analytics engine to process and analyze data within the data lake. Its collaborative and scalable features made it the ideal choice for data engineering, transformation, and analytics tasks.
  3. Data Ingestion and Integration: Data from various sources and formats were ingested into the data lake using Azure Data Factory and Azure Databricks. We developed ETL pipelines to cleanse, transform, and harmonize data, ensuring consistency and accuracy.
  4. Data Governance and Security: Azure’s built-in security features, along with Databricks’ access controls, enabled us to enforce strict data governance policies and access controls, safeguarding sensitive data and ensuring compliance with industry regulations.
  5. Scalability and Performance: Databricks’ auto-scaling capabilities ensured that the data lake could handle growing volumes of data and concurrent user demands without compromising performance.
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By consolidating individual data projects into an agency-wide data lake using Azure Cloud and Databricks, the agency achieved data synergy, breaking down data silos, and unleashing the full potential of their data assets. This transformation paved the way for data-driven decision-making and fostering innovation by providing:

  • Centralized Data Repository: All data, previously scattered across different projects, were now consolidated into a single data lake.
  • Enhanced Analytics: Databricks enabled advanced analytics and machine learning capabilities, empowering the organization to derive valuable insights from the combined data sources.
  • Future-Ready: The solution is scalable and adaptable, capable of accommodating future data growth and evolving analytics needs.