Notes from the lab

Analytic Pipeline Optimization

Notes on getting more throughput out of the same hardware by pushing computation closer to storage instead of pulling everything into memory first. This is the working record for the Analytic Engineering research track.

Key findings

• Pushdown filtering before joins cut shuffle volume substantially on the test dataset.
• Vectorized transforms outperform row-wise UDFs once batch size clears a threshold.
• Column pruning earlier in the plan reduced peak memory more than expected.

Benchmark

Screenshots

Attachment

analytic-pipeline-optimization-notes.pdf PDF · full benchmark methodology & raw numbers

Download

Dimensional Modeling for LLM Fine-Tuning

Star schemas designed for efficient retrieval and embedding generation. Full write-up and benchmark in progress — see the Data Model Architecture research track for the current state of this work.

Semantic Layer Design Patterns

A taxonomy of semantic layers for consistent metric definitions across BI tools and downstream consumers.

Key findings

• Metric definitions drift fastest at the join between transactional and reporting schemas.
• A single source of truth for metric logic reduces reconciliation work more than adding more dashboards ever does.

Data Lineage & Governance

Tracking data provenance across complex ETL pipelines — notes on what breaks lineage tracking in practice and what's worth instrumenting versus what isn't.