Data Platform Review
Situation
In 2024, our team was assigned to address substantial data quality and operational efficiency challenges in a rapidly growing data environment. The existing data infrastructure, composed of diverse data sources and large volumes of structured and unstructured data, required a significant overhaul to meet high data integrity and performance standards.
Task
We aimed to design and implement a comprehensive data quality framework to ensure data accuracy, integrity, and efficiency across multiple databases. This involved integrating new technologies, refining data pipelines, and automating monitoring tools to meet the organization's evolving needs.
Action
To tackle this task, we employed a variety of technologies and methodologies:
Data Storage: We utilized Microsoft Azure Blob Storage for scalable, secure data storage, ensuring easy access and managing large datasets.
Data Warehouse: Snowflake was chosen for its performance and scalability, allowing us to efficiently handle massive data volumes and complex queries.
Languages & Tools: We used Python and SQL extensively to build ETL (Extract, Transform, Load) processes, automate data quality checks, and manipulate data.
Visualization: Tableau was implemented to create dashboards for real-time monitoring of data quality metrics.
The first step was identifying the critical values and frequently accessed tables. We defined specific data quality rules, such as ensuring the 'acceptdate' field was neither null nor before 2010. Detailed documentation was created to outline the data quality, integrity, and efficiency measures, which served as a guide for the entire project.
Next, we developed automated Python and SQL tests to enforce these rules across our datasets. We integrated these tests into our ETL pipelines, ensuring that any data failing the quality checks was flagged for review. This was crucial in maintaining the integrity of the data as it moved through various processing stages.
We also faced several challenges:
Handling Large Data Volumes: Ensuring the ETL processes were optimized to handle large datasets without performance degradation.
Data Consistency Across Systems: Ensuring data remained consistent and accurate across various platforms, including Azure Blob Storage and Snowflake.
Real-time Monitoring: Developing a Tableau dashboard that provided real-time insights into data quality metrics, such as failure rates and processing times, while minimizing resource usage.
To overcome these challenges, we leveraged Snowflake's scalability and Python and SQL's flexibility. We also optimized the ETL processes to reduce data processing times, directly contributing to cost savings.
Result
The project resulted in substantial improvements in both data quality and operational efficiency. Key achievements included:
Data Quality Improvement: Data errors were reduced by over 90%, achieving a failure rate of less than 1% annually.
Operational Efficiency: ETL processing times were reduced by 10%, resulting in significant cost savings on cloud resources.
Enhanced Monitoring: The Tableau dashboard provided clear, real-time visualization of data quality metrics, enabling quicker issue identification and resolution.
Scalability: The new data infrastructure is now better equipped to handle future data growth, ensuring long-term sustainability.
These results optimised resource utilisation and empowered analysts and stakeholders with reliable and timely data, leading to better decision-making across the organization.
Conclusion:
By incorporating more details about the technologies used, challenges faced, and specific technical contributions, your project description becomes more tailored to a data engineering audience. This approach highlights your technical expertise, problem-solving skills, and work impact, making it highly relevant to a data engineering role.
