High-Volume Data Retrieval via HTTP

Handling large-scale data retrieval, the team successfully implemented a solution capable of fetching over 1 terabyte of data daily through HTTP protocols. By overcoming challenges like rate limiting and ensuring seamless processing, the solution enabled reliable and scalable data integration for downstream analysis and storage.

Data Sources

Ingestion

Storage

Visualization

Data Sources:

Data was retrieved from diverse HTTP endpoints, aggregating critical information for further analysis. Careful handling of API rate limits ensured consistent and uninterrupted data collection from these sources.

Ingestion:

Using Databricks, performant data ingestion pipelines were designed and executed. These pipelines leveraged the distributed computing power of Databricks workers to handle the high volume and velocity of incoming data, ensuring efficiency and scalability.

Storage:

The retrieved and processed data was stored in Azure Data Lake, providing a secure and scalable repository for large-scale datasets. This storage solution enabled downstream processing and analysis with minimal overhead.

Visualization:

While visualization wasn’t the core focus of this use case, the solution’s integration with downstream tools like Power BI provided the ability to explore insights derived from the retrieved data when necessary.