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SQL data warehouses to Google Big Query

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  • SQL data warehouses to Google Big Query

Client Needs

  • Transition from a legacy SQL-based data warehouse to a modern, scalable cloud solution.

  • Gain real-time analytics and reporting capabilities.

  • Reduce reliance on manual data processing and validation.

  • Achieve cost-effective infrastructure with minimal maintenance overhead.

Existing Challenges

  1. Performance Bottlenecks: Legacy SQL systems struggled with large workloads, leading to slow query performance and delays in report generation.

  2. Manual Processes: Data ingestion and validation required human intervention, introducing errors and inefficiencies.

  3. Inconsistent Reporting: Lack of real-time data access led to discrepancies and unreliable business insights.

  4. High Infrastructure Maintenance: Maintaining and scaling on-prem systems was resource-intensive and expensive.

  5. Limited Scalability: Traditional SQL setups lacked the flexibility to scale efficiently with growing data volumes.

Architecture Overview

The migration architecture followed a phased, automated model:

  1. Data Sources: Legacy SQL databases.

  2. Table Creation: BigQuery tables mirrored from SQL structures.

  3. Code Conversion: SQL scripts transformed to BigQuery-compatible SQL.

  4. Data Ingestion: Automated pipelines using Dataflow, Cloud Functions.

  5. Data Validation: Checks for consistency, integrity, and accuracy.

  6. BI Integration: Power BI, Tableau, QlikView for real-time visualization.

Challenges

  • Performance limitations with large SQL workloads.

  • Manual data ingestion and validation processes.

  • Inconsistent reporting and lack of real-time data.

  • High maintenance and operational costs.

Solutions

  • Migrated entire SQL workloads to Google BigQuery.

  • Automated ETL pipelines improved data flow and scalability.

  • Enabled validation protocols for structural and data integrity.

  • Integrated popular BI tools for real-time data access.

Outcomes

  • 75% faster query execution and report generation

  • Zero on-premise infrastructure, reducing costs significantly

  • Improved data accuracy, integrity, and auditability

  • Scalable architecture enabling real-time dashboarding

I'm interested

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