Low-code solutions using Azure & Snowflake

Company Information

The Challenge

Procogia’s Approach

We analyzed their existing architecture and pipelines to identify areas that can be automated & optimized and understood potential risks as the pipelines would need to be scaled.​

We designed pipelines to streamline and automate the data extraction process from APIs with the help of ADF.

The pipeline ingested & stored raw data in ADLS and loaded & transformed the data in Snowflake.

To enhance automation and facilitate incremental loading, we introduced a Log Table in Azure Table storage. The Log Table served as a checkpoint, allowing for the efficient tracking and management of data synchronization.

The Results

Automated and scalable Data Engineering Pipelines implemented..

ETL/ELT pipeline processing time reduced by ~50%.

Query time for Snowflake queries on analytical tables was reduced by as much as 30% in some cases, which also helps in cost reduction.

Data Engineering Best practices such as scalable & robust architecture, modular codes, data quality & validation, error handling & monitoring, version control, documentation etc. incorporated.


The partnership between ProCogia and the sports organization led to a breakthrough in their analytics, notably improving efficiency and scalability in data handling. By leveraging Azure Data Factory and Snowflake, the project addressed key issues in data scalability and performance, resulting in faster processing and reduced query times. This success highlights the impact of innovative data engineering solutions in enhancing sports analytics capabilities.

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