Katalyst: Handling very large analytical datasets in a single experiment

Description

• Some experiments aggregate extremely large numbers of analytical runs (e.g., many plates or time points).
• Symptoms:
  • Slow loading of experiments.
  • Timeouts or errors in the Analysis Workspace.
  • Long reprocessing times.

Solution

• Consider splitting work across multiple experiments:
  • Instead of one giant experiment, create logical sub‑experiments (e.g., by plate, batch, or time point).
  • This keeps each experiment’s dataset more manageable.
• Review analytical data retention:
  • Evaluate whether all intermediate or diagnostic runs must be kept in the same experiment.
  • Archive or separate non‑critical runs if supported by your data governance policies.
• Tune infrastructure and configuration:
  • Ensure sufficient server resources (CPU, RAM, disk I/O).
  • Optimize Spectrus DB settings for large datasets (indexing, memory).
• For very large use cases:
  • Work with Technical Support and Services team to profile performance and possibly adopt specialized configurations for high‑throughput environments.