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Case Study

ETL Dashboard-Case Study

About ETL Dashboard

The ETL Dashboard was created by Emvigo to allow users to access informative insights from large datasets. The dashboard is designed to enable users to make informed decisions by identifying critical data points through interactive business intelligence. It supports different analytical approaches and enables users to generate detailed analytical reports from CSV, excel, or parquet files. The purpose is to provide customers with an efficient and effective means of managing and fetching analytical reports for their bulk data.

Problem Statement and Our Inferences 

Developing an ETL dashboard pipeline that helps to process, transform, and save large datasets, providing reliable insights for informed decision-making. The ETL dashboard solves problems for business owners looking to increase sales and move their company forward. Users can create a session and upload data in multiple formats, which are then loaded into visualization tools like AWS QuickSight. The solution also offers automation and scheduling features.

Our Solution

The ETL Dashboard application extracts data uploaded by the user, generates a detailed summary, and compiles it based on the file uploaded. It can be integrated with customer-managed internal databases, eliminating the need for exporting files. The tool provides customization options for the dashboard and a scheduler system for data integration without manual involvement. Users can connect their data sources to our solution for automation.

The Emvigo Approach  

During the discovery phase, a team of business analysts collected and formulated insights on the required analytical data and graphical representations for small and mid-sized companies. Based on this study, a plan was developed to create an ETL dashboard accommodating these requirements. The development followed an agile methodology, with the BA team working closely with the development team to create a fail-proof system capable of processing large CSV files into analytical data. The dashboard accommodates the analytical requirements of various niches and industries.

The Solutions Approach

The ETL Dashboard tool has four core processes: uploading, validation, transformation, and visualization. AWS S3 is used for the effective management of big-sized data, and customized validation mechanisms are used to ensure data quality. Transformation minimizes data complexities, while AWS Quicksight and AI Support are used for data visualization. The drill-down mechanism allows for deep insights, and reports can be exported and viewed by users. Improvements and sophistication are ongoing.

Integration with external applications and a fully automated extraction of documents is something that adds more value to the ETL Dashboard. If the user has a predefined set of patterns in which they want the data to be exported, then it can be saved in the application. This export mechanism works in two ways.

  • In one model, the application shows the recently exported model with the recent exports shown. 
  • In the other model, the user can also save an export pattern as an export template and exports will be done based on the template. 

The tool offers two methods to standardize data export for customers and can also extract data from websites and convert it to CSV or Excel format. It can be integrated with multiple servers simultaneously, allowing users to fetch data from multiple servers as per their needs and requirements.

Major Features

  • Customizable and Interactive Dashboard
  • Export as PDF or Excel Files
  • An Admin Portal with Secure Access
  • Upload the files and view the dashboard Insights within 10 min
  • Content Validation using RegExp
  • View Upload Session History
  • ETL Pipeline Process within 10 min
  • Support of Millions Data Upload
  • Multiple File Upload Options
  • Timeline Filters
  • Mobile Device Support
  • Drill Down support to view the deep insights of high-level
  • Email Report Options
  • Connect and Support of datastores (Amazon Aurora, Amazon RDS for MySQL, PostgreSQL, Amazon Redshift, DynamoDB, and Amazon S3)
  • Scheduler for Uploaded files or Datasource
  • View the History of Scheduled Data
  • AI-Enabled & Quicksight Spice Dashboards
  • API Support-Trigger and view the activity using API
  • Dashboard Embedding or Integration in your application

The Milestones and Challenges we resolved

  • Challenge 1-Integration of Quicksight for generating dashboard URL.
  • Challenge 2-To set up Database for bulk data to be processed.

Check out our full Case study to know more about how we resolved these challenges.

Techstacks we offered  

  • ReactJS, NodeJS and Python
  • AWS resources (AWS S3, Glue, RDS, AWS Cognito, API Gateway, Lambda, Cloud Watch Secret Manager, WAF, etc)

What’s next in Line?

The ETL Dashboard is planned to have an alert and automatic suggestion feature, along with the capability to find and import desired data. A side panel filter will help users filter data quickly, and recent filters can be reused. Database connections supported by AWS Glue, Scheduled Data Sync, and Action-based BI Dashboards will also be made available. Users can select headers for processing, helping those who are not technically proficient to change fields for data processing.



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