Business Challenges:

ü Heavy Administrative Overhead: Managing Windows, File, and SQL servers required a large infrastructure team.

ü Scalability Constraints: SQL-based ETL pipelines and relational databases made it difficult to handle growing data volumes.

ü Licensing & Downtime Costs: Multiple server copies for HA/DR led to high licensing fees and downtime issues.

ü Rigid Architecture: The ETL + multidimensional (SSAS) model lacked flexibility and required MDX skills.

ü Poor Accessibility: Business users struggled with server-based systems, reducing engagement and slowing down decisions.

Data Spot Consulting Group’s Solution: The team executed a cloud migration and analytics modernization strategy:

ü Data Lake on AWS S3

o Centralized storage for structured, semi-structured, and raw data from multiple departments, vendors, and applications.

o Simplified data ingestion via browser-based uploads.

ü ELT Processing Using Modern Stack

o Transformed data using AWS Glue, DBT, and Apache Airflow.

o Migrated from ETL to ELT model, storing raw data in Snowflake for flexible transformation.

ü Self-Service Analytics with BI Tools

o Replaced SSAS-based reporting with interactive dashboards using Power BI and Tableau.

o Enabled business users to access insights via browsers without server logins or training.

Technology Stack:

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