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Success story

Building a Modern Data Cloud Platform

Leading automotive company
Man having a data presentation at the office

Project Overview

Leading automotive company embarked on an ambitious journey to build a cutting-edge Data Cloud Platform from the ground up. The primary objective was to support the company’s rapid growth, foster innovation, streamline business processes, and enhance manufacturing capabilities. This greenfield project was designed with best-in-class industry standards, incorporating Medallion architecture and Data Mesh principles, while leveraging leading open-source technologies such as PySpark and Delta Lake to ensure flexibility and avoid vendor lock-in.

Key Objectives

  • Establish a scalable Data Cloud Platform to support fast company growth
  • Enable advanced analytics through robust data architecture
  • Facilitate real-time and batch data processing
  • Enhance data quality, governance, and democratization
Abstract cloud illustration

Key Roles and Responsibilities:

The project’s success was driven by a collaborative team with distinct roles:

  • Data Platform Design: Shaping the platform as a product
  • Data as a Product: Defining and managing data products
  • Data Architecture: Designing decentralized architecture and ETL techniques
  • Standards Development: Establishing best practices for data management
  • Cloud Data Engineering: Implementing scalable and efficient cloud solutions
  • Orchestration Design: Creating patterns for efficient data workflow management
  • Real-Time Processing Design: Architecting solutions for real-time data analytics
  • DevOps: Managing infrastructure automation, CI/CD pipelines, and cloud resource optimization

Strategic Initiatives and Achievements

Strategic Design and Architecture:

The platform architecture was crafted with a focus on scalability, flexibility, and performance. Key architectural components include:

  • Medallion Architecture: Supporting data transformations through bronze, silver, and gold layers
  • Data Mesh Implementation: Promoting decentralized data ownership and scalability
  • Metadata-Driven Frameworks: Enabling dynamic and automated data processing workflows

Core Functionalities:
The platform delivers a wide range of functionalities through advanced metadata-driven frameworks:

  • Data Ingestion: Seamless integration of diverse data sources
  • Data Transformation: Loading data into silver and gold zones
  • File Parsing: Handling JSON, XLSX, CSV, and fixed-width files
  • Access Control: Granting storage and database privileges
  • Data Lineage: Harvesting PySpark data lineage for transparency
  • Data Quality (DQ) Module: Ensuring high data integrity
  • Orchestration: Managing complex workflows with cost optimization strategies
    • Skipping pipelines without data changes
    • Running pipelines on clusters (small, medium) based on complexity
  • Automation: Automated JIRA ticket creation for issue tracking
  • Real-Time Data Processing:
    • CDC replication with Debezium
    • Azure Functions and Stream Analytics for real-time data streaming
  • Data Exposure: APIs for seamless data access and integration
  • Advanced Analytics Enablement: Integration with Azure Cognitive Services and Machine Learning Studio
  • Resource Optimization: Efficient cloud resource consumption through optimized DB and Spark pools
  • DevOps Integration: Development of automation scripts for streamlined operations

Project Achievements:

  • Over 3,000 active pipelines developed
  • Integration with more than a dozen data sources, including real-time data streams
  • Dramatic improvement in company-wide data literacy
  • Enhanced data quality across the organization
  • Centralization of siloed data, enabling data democratization and self-service analytics
Woman working on a laptop using remote cloud services

Tools and Technologies

The platform leverages a robust technology stack, including:

  • Cloud Storage & Databases: Azure Blob Storage, Delta Lake, Azure SQL, Cosmos DB
  • Data Integration & Processing: Event Hub, PySpark, Azure Synapse, Debezium, Azure Data Factory, Data Flow
  • Real-Time Analytics: Azure Stream Analytics, Azure Functions
  • Data Governance: Microsoft Purview
  • Development & Scripting: Python, T-SQL, Log Analytics
  • Data Formats & APIs: Parquet, APIs

Impact and Success

Company’s Data Cloud Platform has become a cornerstone of the company’s digital transformation. By centralizing data from disparate sources and enabling self-service analytics, the platform has democratized data access, improved data quality, and empowered users across the organization. The project not only met its objectives but also laid a solid foundation for future data-driven innovations, supporting company’s vision for continuous growth and technological leadership.

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