Are there any open source alternatives to Great Expectations that handle automated data testing without driving up our cloud compute bills?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
Are there any open source alternatives to Great Expectations that handle automated data testing without driving up our cloud compute bills?
4 real searches · 1 real-search pass
open source data quality testing alternatives Great Expectations Soda Core dbt tests Deequ Pandera 2026Google ↗
OBSERVEDComparison
Soda Core open source data quality scans architecture SQL warehouse compute costGoogle ↗
OBSERVEDYes / no
AWS Deequ open source data quality library Spark checks documentationGoogle ↗
OBSERVEDYes / no
Pandera documentation dataframe validation pandas polarsGoogle ↗
OBSERVEDInstruction
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
ChatGPT real searches (4)
Classes come from the query-classifier (a local Laya model). 3 of 4 calls are low-confidence (under 50%) — hover a query in the table to see its confidence.
Searches and sources
ChatGPT4 real searches · gpt-6
open source data quality testing alternatives Great Expectations Soda Core dbt tests Deequ Pandera 2026 Google ↗
Soda Core open source data quality scans architecture SQL warehouse compute cost Google ↗
AWS Deequ open source data quality library Spark checks documentation Google ↗
Pandera documentation dataframe validation pandas polars Google ↗
- Documentation · docs.getdbt.com
- GitHub · github.com
- Writing custom generic data tests · docs.getdbt.com
- Documentation · pandera.readthedocs.io
- GitHub · github.com
- GitHub · github.com
- Documentation · github.com
- Documentation · posit-dev.github.io
- GitHub · github.com
- GitHub · github.com
- Documentation · docs.elementary-data.com
- GitHub - elementary-data/dbt-data-reliability: This dbt package captures metadata, artifacts, and test results so you can detect anomalies, monitor data quality, and build metadata tables. It powers Elementary OSS and feeds the wider context layer used by Elementary Cloud’s full Data & AI Control Plane. · github.com
- Soda Core License Update: Moving to Elastic License 2.0 · soda.io
- soda-core/LICENSE at main · sodadata/soda-core · github.com
- Soda Core GitHub · github.com
Queries
4 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
open source data quality testing alternatives Great Expectations Soda Core dbt tests Deequ Pandera 2026 OBSERVED | Google ↗ AI Mode ↗ | — | Comparison | — | — | 1x of 1 | |
Soda Core open source data quality scans architecture SQL warehouse compute cost OBSERVED | Google ↗ AI Mode ↗ | — | Yes / no | — | — | 1x of 1 | |
AWS Deequ open source data quality library Spark checks documentation OBSERVED | Google ↗ AI Mode ↗ | — | Yes / no | — | — | 1x of 1 | |
Pandera documentation dataframe validation pandas polars OBSERVED | Google ↗ AI Mode ↗ | — | Instruction | — | — | 1x of 1 |