Need to scale our data processing infrastructure to handle petabyte-scale datasets

We are dealing with extremely large datasets and need enhanced data ingestion and analysis capabilities to handle petabyte-scale data processing. Currently, our system's ability to process and analyze these massive datasets is limited, which is hindering our capacity to leverage the full potential of our data sources for AI-driven fraud detection and analytics.

Our proposed functionality should enable our system to:

• Ingest and process petabyte-scale data efficiently

• Perform complex analytical queries on large datasets with acceptable performance

• Support various data formats and sources relevant to our fraud detection and analytics use cases

How we envision this working:

• Implement a distributed data processing framework (e.g., Spark, Hadoop) for parallel processing

• Optimize data storage and indexing for faster query execution

• Provide tools for data partitioning and management

Important requirements for us:

• Scalability: The solution must scale horizontally to accommodate future data growth

• Performance: Analytical queries should execute within a reasonable timeframe

This is critical for our ability to perform large-scale data analysis and maintain our competitive edge in processing massive datasets for fraud detection and client analytics.