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A leading courier delivery services company with around 12 million shipments going every month required a responsive, highly available, scalable and secure data warehouse to store and analyze the data with high level of automation.

We made a visible and measurable impact to our client's business

100,000+ man hours saved per year

Challenge

Industry Overview

With the current architecture they faced high refresh time for dashboards, high degree of manual intervention associated especially with data ingestion and lack of resilience. The client wanted an efficient redshift-based architecture design to overcome these limitations and ensure greater flexibility for integrating the stored data with various other analytical tools, incurring minimum costs.

Why were we brought in?

To help the logistic giant overcome this problem Ganit was successfully able to come up with a much more robust and sustainable model.

Our approach

Methodology

  • Ganit proposed the client to shift to AWS Aurora as their primary backend database along with integrations to various other AWS services for smooth data ingestion, data analysis and dashboard creation for various applications including FR Plus and many more which helps facilitate the decision-making process for the management.
  • For the data ingestion, the data is unloaded into an S3 bucket from where it is processed using ETL and the processed data is shifted to Amazon Aurora for further processing and Amazon Redshift for warehousing.
  • Amazon Aurora PostgreSQL uses a high-performance storage subsystem customized to take advantage of fast distributed storage and improves upon PostgreSQL for massive throughput and highly concurrent workloads.
  • Also, the Aurora is directly connected with various BI tools like quicksight and tableau for creating dashboards to measure different KPI’s which ultimately facilitates the client’s decision-making. Also, the recurring reports are being automated using Apache Airflow and sent to clients within the specified interval.

A valuable difference

Impact

The decision-making process became much quicker and more convenient while also replacing complex integrations in the architecture with much simpler AWS services for a much better smooth workflow.

This model not only facilitates macro decisions the company takes considering the long-run horizon but also various other reports and dashboards to help them with the micro day-to-day level decisions.

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