Low-code solutions using Azure & Snowflake
Company Information
ProCogia embarked on a strategic initiative to enhance the data processing capabilities of a Leading Sports Organization’s Performance Data Science team. By implementing low-code solutions with Azure Data Factory and Snowflake, the project aimed to tackle significant challenges in data scalability and query performance. This approach was designed to revolutionize sports analytics by optimizing data extraction, storage, and analysis processes.
The Challenge
The client was experiencing issues with data extraction and query performance as the volume of data increased multifold. Their current pipelines were not built to scale and did not follow the Data Engineering best practices.
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.
Conclusion
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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