Glossary

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Glossary / Evaluation and implementation guide

Data Compression

Data compression is a storage encoding technique that reduces the physical size of tables.

Modern columnar warehouses compress automatically using encodings like run-length or dictionary encoding, which shrink repetitive columns such as event_type or campaign_id dramatically. Because scans read fewer bytes, queries often stay fast or even speed up.

It is a table design lever, not partitioning, tiering, or query optimization.

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A practical example

Example: a behavioral table with 2 billion email events stores event_type as a dictionary-encoded column.

Repeated values collapse to a fraction of raw size, so the monthly warehouse storage line drops while dashboard queries on that column scan less data.

What to evaluate before investing

  • Ask which encodings the engine applies per column type and whether you can override them.
  • Test whether compression is automatic at ingest or requires manual table redesign later.
  • Confirm whether compressed data still supports fast filters and joins on high-cardinality columns.

Limitations and tradeoffs

Tradeoff: compression works best on repetitive, low-cardinality columns. Free-text fields and high-cardinality identifiers compress poorly, and heavy compression can add CPU cost on write-heavy ingestion.

Plan your next step with MeshLine

Connect this decision to your automation, organic marketing and customer lifecycle management. In a MeshLine demo, discuss your existing tools, the scope you need and how to measure the result.

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