Developing a Robust Retail Data Pipeline with Snowflake

Data Accuracy & Consistency With snowflake For Retail Data Management
Client Overview

The client is one of the giant retailers in the US that offers a variety of food supplies, and services. They have a strong reputation for providing high-quality products and outstanding customer service.

Client Requirement
  • Develop a data pipeline to get the raw data from each platform into targeted tables in Snowflake.
  • Fetch the data from targeted tables based on platform, fiscal week, and network and compare the results with the source/raw data.
  • Create an excel file to compare the source data with the resulting data from the snowflake tables.
  • Generate a report on a weekly basis to highlight which platform, network, and date range have inaccurate or missing data issues.

Implemented Solution

  • Queried a data pipeline to transform and load raw data from various platforms into the targeted tables in Snowflake.
  • Developed a query to group the activity and conversion data from the targeted Snowflake tables based on fiscal week and network for each platform.
  • Created an excel template to compare the source and targeted data based on fiscal week, network, and platform.
  • Fetched the sourced data directly from various platforms and inserted it into the excel template.
  • Run the developed query to get the grouped data from the targeted snowflake tables and load it in the excel template.
  • Created calculated fields in excel to compare the activity and conversion data between the source vs snowflake data and find out the data discrepancies in %.
  • Updated the source as well as the targeted data every week in excel to get the latest data.
  • Generate an excel report on a weekly basis to highlight the % data discrepancies for each network and fiscal week of the current fiscal year for each platform.
Robust Data Pipeline For Accurate Reportinng With Snowflake


Data Warehousing - Snowflake
Query language - SQL
Reporting - Excel

Client Benefits

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Data Accuracy

With the developed model, there is a 70% reduction in data inaccuracy and inconsistency leading to a more precise dataset.

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Time Reduction

It ensures cost-effectiveness because it saves time and money by making sure that the datasets collected and used in processing are clean and accurate.

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Easy Decision-making

Due to reduction in data inaccuracy and inconsistency it is helpful for top management to make informed decisions.

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Fighting Data Decay

Validating data can assist the organization in reducing the potential errors caused by data decay by identifying where data is missing, incomplete, inconsistent, and inaccurate.

Get more factual in your business decision-making process with Data Analytics


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