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Data Lake Storage Cost Calculator

Work out data lake storage cost instantly with clear inputs, formula shown and shareable results.

About Data Lake Storage Cost Calculator

A data lake reaches steady state at monthly ingest times retention, so 80 TB monthly with three years of retention holds 2.9 PB raw. Converting to a columnar format such as Parquet with compression typically achieves five-to-one or better, cutting that to under 600 TB — and the same conversion reduces query scan cost proportionally.

Formula

Lake storage

compressed per month = raw / compression ratio; steady state = compressed x retention months; cost = TB x 1000 x per-GB rate

Worked example

With the default values below, Data Lake Storage Cost Calculator returns Steady-state monthly storage cost of $13,248.00. Change any input above and every figure here is recalculated in your browser.

Inputs used

Raw data ingested per monthminimum 0.001 TB
80 TB
Retentionminimum 1 months
36 months
Compression ratio for columnar formataccepts 1–30 x
5 x
Storage price per GB-monthminimum 0
0.023

Results

Steady-state monthly storage cost
$13,248.00
Steady-state storage volume
576.0 TB
Monthly saving from columnar compression
$52,992.00
Annual cost
$158,976.00

Step by step

  1. 1Values used
  2. 2Lake storage
  3. 3Steady-state monthly storage cost= 13,248.00
  4. 4Steady-state storage volume= 576.0 TB
  5. 5Monthly saving from columnar compression= 52,992.00
  6. 6Annual cost= 158,976.00

Assumptions and limits

  • The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
  • In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
  • Figures are rounded for display to 2 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.

How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.

Frequently Asked Questions

Why does columnar format compress so well?

Values in a column share a type and often repeat, so dictionary encoding, run-length encoding and delta encoding all apply. Row formats mix types and compress far worse.

Should raw data be kept after conversion?

Often yes for reprocessing, but in the cheapest archive tier. Keeping raw JSON in standard storage alongside Parquet doubles cost for little benefit.

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