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Taxfyle

 

CASE STUDY

Taxfyle transforms data infrastructure for better business insights

 

KEY RESULTS

case-study-checkmarkProvisioning time cut from weeks to minutes

case-study-checkmarkEnhanced data quality and analytics

case-study-checkmarkAutomated, serverless data pipelines


 

Customer overview

Taxfyle is a fintech startup that provides online tax filing services with the help of a network of licensed CPAs. Founded in 2021, They have over 4,000 CPAs and serve customers across the US. Taxfyle offers tax filing services at 40% less than retail tax services and guarantees a 48-hour turnaround time. Taxfyle has raised $4M in Series A funding and is growing rapidly.

 

Background

Taxfyle needed to collect and aggregate transactional information from various sources and build a data platform for analytics and machine learning. They had a hard requirement to adopt DataOps best practices, such as infrastructure automation, code-first data pipelines, and continuous testing and monitoring.

 

Challenge

Taxfyle partnered with CDW’s Google Cloud team to use its data and analytics capabilities. They wanted to build scalable, reliable, and secure data pipelines that could handle schema changes, data quality issues, and complex transformations. They also wanted to leverage Google Cloud's serverless and managed services to reduce operational overhead and costs.

 

Solution & impact

CDW’s Google Cloud team helped Taxfyle to build fully automated, serverless, and testable data pipelines using Dataflow. This enables Taxfyle to handle schema changes and data quality issues automatically. Dataflow also integrated with other Google Cloud services to provide a complete data platform solution. CDW also used Terraform to script the software and infrastructure components. This enabled Taxfyle to automate the deployment and configuration of their data platform, reducing workload provisioning from weeks to minutes. As a result, Taxfyle gained the ability to use historical and current data to make better business decisions, as well as to lay the groundwork for subsequent data and analytics initiatives within the organization.

 


 

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