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Analytics System

Live
25% Revenue Growth

📋Project Overview

The client's previous analytics setup was slow, unreliable, and didn't provide timely insights. We designed and built a new system using a modern data stack. Data was ingested from various sources (databases, APIs, logs) into a data lake (AWS S3), processed using Spark jobs, and loaded into a Redshift data warehouse. A Tableau dashboard was created for visualization, providing key metrics and trends that led to data-driven decisions and significant revenue growth.

⚠️Challenges Faced

The existing analytics infrastructure presented significant hurdles:

  • Performance Issues: Legacy system was extremely slow, making data exploration tedious.
  • Data Silos: Information was fragmented across various databases and spreadsheets, preventing a holistic view.
  • Lack of Timeliness: Reporting was manual and infrequent, hindering agile decision-making.
  • Scalability Limits: The old system couldn't handle increasing data volumes or new data source integrations.

⚙️Solutions Implemented

We architected and delivered a modern, scalable analytics platform:

  • Modern Data Pipeline: Built an ETL (Extract, Transform, Load) pipeline using AWS S3 for data lake storage, Apache Spark for processing, and Amazon Redshift as the data warehouse.
  • Data Consolidation: Integrated disparate data sources (SQL databases, APIs, logs) into the centralized Redshift warehouse.
  • Interactive Visualization: Developed dynamic and insightful dashboards using Tableau, connected directly to Redshift.
  • Automation: Automated the entire data ingestion, processing, and reporting workflow.
  • User Training: Empowered the client's team with training on leveraging the new Tableau dashboards.

🎯Key Results

The revamped analytics system drove significant business value:

  • Revenue Growth: Actionable insights directly contributed to a 25% increase in revenue.
  • Timely Insights: Provided near real-time access to key performance indicators (KPIs).
  • Efficiency Gains: Reduced time spent on manual reporting by over 70%.
  • Scalability: Established a robust and scalable foundation capable of handling future data growth.

🛠️Key Technologies & Skills

Analytics
Business Intelligence
Data Pipeline
AWS
Spark
Redshift
Tableau
ETL