Crafting Data Pipeline for Data Lake & Advanced Analytics

Case Study - Retail

Executive Summary

This company stands as a prominent American designer and marketer of children’s apparel. Established in 1865, the organization currently aims to derive insights from its retail data to enhance its understanding of customer needs.

QloudX, an AWS Advanced Consulting Partner, was approached by the specialty US Retail provider to assist in modernizing their data platform. The provider sought to establish a modern data solution that would consolidate their data in a data lake and data warehouse, facilitating real-time value extraction. Their goal was also to enable real-time decision-making and leverage prescriptive analytics. QloudX aided the provider in setting up this platform using a “Build-Operate-Transfer” (BOT) approach.

The Challenge

The company manages various essential business applications, each containing distinct datasets. However, they encountered challenges in extracting meaningful insights from customer data and accessing past reports. To address this, the company aimed to establish a comprehensive data platform that seamlessly integrates diverse applications as data sources.

They embarked on constructing analytical capabilities on the AWS platform, leveraging Tableau’s user-friendly interface and robust sharing features. This decision was influenced by Tableau’s strengths, including unlimited data storage, rapid processing speed, flexibility, security provisions, and overall reliability. Furthermore, the analytical reports were intended to provide an expanded scope, offering a comprehensive 360-degree view of both Customers and Orders.


Embarking on the creation of a robust Data Platform on AWS presents a remarkable opportunity. This endeavour involves leveraging a range of AWS Data and Analytical services to orchestrate diverse Data processes, encompassing vital aspects such as Data security and Data Governance.

Our Solution

QloudX was approached with this challenge due to our expertise in cloud technologies and strategic IT. We have successfully executed similar projects in the past and were confident in meeting the company’s demands for this challenge.

We seamlessly incorporated our solution into their subsequent systems. Additionally, we ensured the data was prepared for Machine Learning applications, enabling effortless utilization in forthcoming endeavours. Right from the outset, the customer gained access to their data, fulfilling a long-standing desire. Moreover, they could create an expanded array of reports, dashboards, and charts, facilitating deeper comprehension and analysis of the data. Notably, in some instances, they achieved real-time insights and assessments of in-store purchases categorized by geographical location.

Data Gathering: Initially, QloudX pinpointed applications to serve as data sources for the Data Lake and analytical processes. To initiate the flow of raw data, QloudX adopted diverse approaches for data collection. These methods encompassed real-time data streaming, scheduled data transfers from FTP to S3, routine data extraction from databases, replication of S3 buckets from source accounts to the control tower, and various data formats including JSON, CSV, log files, and Parquet.

Establishing the Data Lake: We transferred data from various sources into the S3 Data Lake, creating a structured environment to systematically arrange the source data. Simultaneously, we conducted pre-processing on the data or files when necessary, effectively consolidating all the data into a centralized repository.

Enhanced ELT Workflow: QloudX initiated the ELT (Extract, Load, and Transform) process by initially loading raw data into the sandbox environment. Subsequently, we performed data transformation, delineating connections between data points to identify key variables and determine an appropriate model. Following this, we extracted data through AWS Glue jobs and proceeded to load it into the AWS Redshift data warehouse database. Employing the AWS managed data warehouse, Redshift, we efficiently loaded the processed data, completing a comprehensive ELT workflow.

Ad-Hoc Analysis Capability: Utilizing AWS Athena, we established schemas and tables. AWS Athena, functioning as an interactive query service, simplifies the process of analysing data within the S3 data lake through standard SQL queries. This powerful tool facilitates the swift analysis of extensive historical datasets whenever needed.

Logging & Monitoring: We implemented effective logging and monitoring mechanisms within the data pipeline to ensure the successful execution of the pipeline process. This was achieved through the utilization of AWS DynamoDB for logging, SNS notifications, and CloudWatch for comprehensive monitoring.

Here are some additional highlights of this unique AWS analytical solution:

Diverse Data Collection Approaches: The solution employs various methods such as real-time data streaming, scheduled data transfers, routine database extractions, and replication from source accounts. This ensures comprehensive data coverage.

Structured Data Lake Environment: The data is systematically organized in the S3 Data Lake, providing a structured environment for efficient storage and retrieval.

Efficient ELT Workflow: The Extract, Load, and Transform (ELT) process is optimized for speed and accuracy. Raw data is loaded into the sandbox environment, undergoes transformation to identify key variables, and is efficiently loaded into the Redshift data warehouse.

Comprehensive Data Transformation: The solution effectively transforms data, establishing connections between data points to identify crucial variables and determine appropriate models, enhancing analytical capabilities.

Effective Logging and Monitoring: Robust logging and monitoring mechanisms using AWS DynamoDB, SNS notifications, and CloudWatch ensure the pipeline executes successfully. This provides transparency and accountability throughout the process.

A Solution that creates Value & Benefits

  • Enhanced Customer Understanding: Deeper insights into customer preferences
    and behaviour patterns empower the company to tailor offerings and marketing efforts, ultimately driving customer satisfaction and loyalty.
  • Real-Time Decision-Making: The ability to extract real-time value from data
    enables swift decision-making, especially crucial in dynamic market conditions.
  • Actionable Insights with Prescriptive Analytics: Implementing prescriptive
    analytics translates data into actionable recommendations, optimizing strategies for marketing, sales, and operations.
  • 360-Degree Customer and Order View: Comprehensive insights into customers
    and orders enable personalized strategies, leading to increased sales and customer satisfaction.
  • Real-Time Store Insights: Immediate feedback on in-store purchases by location
    allows for targeted marketing efforts, maximizing sales potential in specific areas.
  • On-Demand Data Analysis: AWS Athena enables ad-hoc analyses, providing
    quick responses to emerging business inquiries.
  • Emphasis on Core Business Functions: Leveraging AWS’s cloud-native
    development tools allow for a focus on product improvement, customer service, and market expansion.
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