Data Analytics and AI
Advanced KPI Reporting and Dashboard Redesign Powered by AWS
A home security provider sought to modernize its legacy data warehouse and business intelligence ecosystem. The objective was to design scalable SQL-driven ETL pipelines using Airflow, build data models in AWS Redshift and enhance Tableau dashboards to deliver data-driven deeper insights.
Client Challenges and Requirements
- Assess and document existing legacy data warehouse mappings and business rules.
- Build optimized fact and dimension tables for high-performance analytics.
- Design a scalable, SQL-driven data pipeline to automate ETL processes.
- Develop optimized SQL queries for ETL and BI workloads.
- Redesign and enhance dashboards to include advanced KPIs and improved visual performance.
- Implement validation rules across ETL and dashboard layers to ensure data accuracy.
Bitwise Solution
- Bitwise followed best practice and AWS cloud native purpose-built services for end-to-end data solution enhancement and redesigning to accelerate project timeline within budget requirements.
- Leveraged YAML-based framework for defining ETL job configurations like job parameter and logic. Then the framework dynamically generates python DAG for Apache Airflow, enabling easy orchestration, scalability and maintainability.
- Implemented a SQL-based approach for developing ETL jobs. Developed optimized SQL queries, Focusing on minimizing join operations, leveraging appropriate indexing strategies, to handle large volumes of data.
- Integrated data integrity checks into the YAML-based framework by defining validation rules such as schema validation, row counts, column match and data type checks.
- Identification of the business requirement and complexity of the dashboards. Build new Tableau dashboards by adopting iterative and modular approach.
- Create dynamic visualizations in Tableau using advanced metrics and KPIs, Integrate optimized SQL queries to ensure performance.
Tools & Technologies We Used
AWS MWAA
AWS S3
AWS Redshift
AWS CodePipeline
Tableau
DataGrip
Python
SQL
Jira
Confluence
Key Results
Enhanced dashboards with new KPIs and improved performance
Modular, scalable data pipelines that supports future growth
Reduces ETL processing time by using Optimized SQL and Parallel processing
Optimized use of AWS features and capability to minimize maintenance and cost
Structure-agnostic, SQL-driven, and scalable design with optimal performance
