Real ChatGPT answer · index seed6Dp6IIYYMZ8completeB2B software · data and analytics platformsinformational
What are the main performance differences between Superset and Metabase when querying millions of rows of smart home IoT data from an S3 data lake?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What are the main performance differences between Superset and Metabase when querying millions of rows of smart home IoT data from an S3 data lake?
6 real searches · 1 real-search pass
Apache Superset documentation Athena Trino caching async queries performance SQL LabGoogle ↗
OBSERVED
Metabase documentation Athena database support query performance caching dashboard queriesGoogle ↗
OBSERVED
Apache Superset vs Metabase performance large datasets S3 data lake Athena TrinoGoogle ↗
OBSERVED
site:metabase.com/docs/latest Athena database driver query caching Metabase dashboard performance caching configurationGoogle ↗
OBSERVED
site:metabase.com/docs/latest data sources Amazon Athena MetabaseGoogle ↗
OBSERVED
site:docs.aws.amazon.com Athena performance optimize queries S3 Parquet partitioning data scannedGoogle ↗
OBSERVED
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.
Classifying 6 queries…
Searches and sources
ChatGPT6 real searches · gpt-6
Apache Superset documentation Athena Trino caching async queries performance SQL Lab Google ↗
Metabase documentation Athena database support query performance caching dashboard queries Google ↗
Apache Superset vs Metabase performance large datasets S3 data lake Athena Trino Google ↗
site:metabase.com/docs/latest Athena database driver query caching Metabase dashboard performance caching configuration Google ↗
site:metabase.com/docs/latest data sources Amazon Athena Metabase Google ↗
site:docs.aws.amazon.com Athena performance optimize queries S3 Parquet partitioning data scanned Google ↗
- Async Queries via Celery · superset.apache.org
- Development How-tos · superset.apache.org
- How to run Metabase at scale · metabase.com
- My dashboard is slow · metabase.com
- Caching · superset.apache.org
- Caching · superset.apache.org
- Optimize your tables · docs.aws.amazon.com
- Optimize data · docs.aws.amazon.com
- Optimize data · docs.aws.amazon.com
- Optimize Iceberg tables · docs.aws.amazon.com
- Caching query results · metabase.com
- Amazon Athena · metabase.com
- Frequently Asked Questions · superset.apache.org
Semantic map
Every query placed by meaning (EmbeddingGemma 2, 256-d). Shaded regions are topics, nearby points are near-paraphrases, and a circle's size is how often the query came back.
SimulatedReal search
Hover a query to see its counterpart · zoom with Ctrl/⌘ + scroll, a pinch, double-click or +
Queries
6 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
Apache Superset documentation Athena Trino caching async queries performance SQL Lab OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Metabase documentation Athena database support query performance caching dashboard queries OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Apache Superset vs Metabase performance large datasets S3 data lake Athena Trino OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
site:metabase.com/docs/latest Athena database driver query caching Metabase dashboard performance caching configuration OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
site:metabase.com/docs/latest data sources Amazon Athena Metabase OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
site:docs.aws.amazon.com Athena performance optimize queries S3 Parquet partitioning data scanned OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |