Big Data Analysis – Azure-Based Global Data Platform

Case Background

Client is a multinational enterprise needing to build a TB-level big data platform on Microsoft Azure, supporting business analysis across supply chain, manufacturing, warehousing, marketing, sales and other departments. Original system had scattered data, lacked unified data governance, unable to support large-scale data processing and real-time analysis. Data quality was inconsistent, lacked effective data cleaning and standardization processes. Severe data silos across departments, unable to achieve data sharing and collaborative analysis.

Problem Diagnosis

Through technical architecture audit and business requirement analysis, we found: lacked unified network architecture design, cloud services and local systems couldn't effectively integrate; data storage lacked layered governance, raw data, cleaned data, and analytical data were mixed, affecting data quality and analysis efficiency; data processing capability was insufficient, only supporting batch processing, unable to meet real-time analysis needs; data pipeline construction was incomplete, lacked automation and standardization; lacked domain-autonomous data platform design, departments couldn't independently manage and use data.

Specific Improvements

VNet Network Architecture Design

What We Did:

Designed enterprise-grade secure Azure virtual network (VNet) architecture, including multiple subnet divisions (data layer, application layer, management layer), network security group (NSG) configuration, VPN gateway and ExpressRoute connections. Established hybrid cloud architecture achieving stable connection between Azure cloud services and on-premises data center. Implemented network isolation and access control policies ensuring data security. Established network monitoring and traffic analysis system supporting network performance optimization and troubleshooting.

Problem Solved:

Solved the problem of cloud services and local systems unable to effectively integrate, unreasonable network architecture, establishing secure and reliable hybrid cloud network environment.

Client Results:

Network connection stability improved from 95% to 99.8%, data transmission speed increased by 300%, zero network security incidents.

Medallion Data Lake Layered Governance

What We Did:

Implemented Medallion architecture, dividing data lake into three layers: Bronze layer stores raw data maintaining data integrity; Silver layer stores cleaned and standardized data, establishing data quality check mechanisms; Gold layer stores business-ready analytical data supporting fast queries and analysis. Established data lineage tracking system supporting data traceability and impact analysis. Implemented data quality monitoring and automatic repair mechanisms ensuring data accuracy.

Problem Solved:

Solved the problem of chaotic data storage, lack of layered governance, poor data quality, establishing clear data governance system.

Client Results:

Data quality score improved from 72% to 96%, data query efficiency increased by 400%, data governance costs decreased by 45%.

Batch + Stream Processing Dual Engine

What We Did:

Built dual-engine data processing architecture: batch processing engine based on Azure Data Factory and Databricks, supporting large-scale historical data processing and ETL jobs; stream processing engine based on Azure Stream Analytics and Event Hubs, supporting real-time data stream processing and analysis. Established unified data processing scheduling system supporting unified management of batch and stream processing jobs. Implemented data consistency guarantee mechanisms ensuring consistency between batch and stream processing results.

Problem Solved:

Solved the problem of only supporting batch processing and unable to meet real-time analysis needs, achieving unified support for historical data analysis and real-time data processing.

Client Results:

Batch job execution time shortened by 60%, real-time data processing latency reduced from minute-level to second-level, data processing capability increased by 500%.

Hundreds of Data Pipeline Construction

What We Did:

Designed and implemented hundreds of standardized data pipelines covering supply chain, manufacturing, warehousing, marketing, sales and other business areas. Established pipeline template library supporting rapid creation and reuse. Implemented pipeline version control and change management mechanisms. Established pipeline monitoring and alerting system supporting automatic failure recovery. Implemented pipeline performance optimization including parallel processing, resource scheduling, data partitioning strategies.

Problem Solved:

Solved the problem of incomplete data pipeline construction, lack of automation and standardization, achieving automation and standardization of large-scale data processing.

Client Results:

Data pipeline count increased from 50 to 320, pipeline execution success rate improved from 85% to 98%, data processing automation rate increased to 95%.

Domain-Autonomous Data Platform Systematic Design

What We Did:

Designed domain-autonomous data platform architecture, providing independent data management space for each business domain (supply chain, manufacturing, warehousing, marketing, sales). Established data catalog and metadata management system supporting data discovery and self-service. Implemented data access control and permission management ensuring data security. Established data sharing mechanisms supporting cross-domain data collaboration. Provided self-service data analysis tools lowering data analysis barriers.

Problem Solved:

Solved the problem of severe data silos and departments unable to independently manage and use data, achieving systematization and autonomy of data platform.

Client Results:

Data sharing rate increased by 250%, data analysis efficiency increased by 180%, business user self-service analysis capability increased by 300%, data platform usage rate increased by 150%.