What's the most cost-effective way to run BI dashboards using a query engine at petabyte scale?

The most cost-effective pattern at petabyte scale is usually:

Separate storage from compute, and make the query engine only scan what it must

In practice, that means:

1. Keep data in cheap object storage
   Use formats like Parquet or ORC on S3 / GCS / Azure Blob.

2. Use a distributed SQL engine that can push filters down and prune partitions
   Examples: Trino/Presto, Athena, BigQuery, Snowflake, Databricks SQL, depending on cloud and ops preferences.

3. Optimize the table layout heavily for BI

   • Partition by the most selective time dimension, usually date
   • Sort/cluster by common dashboard filters, like customer_id, region, product
   • Avoid “small files”; compact into larger files, often 128–512 MB
   • Use columnar compression

4. Pre-aggregate for dashboard use cases Most BI dashboards do not need raw facts at query time.

   • Build daily/hourly aggregate tables
   • Materialize common metrics by dimension combinations
   • Use summary tables or a semantic layer

5. Cache aggressively

   • Result caching
   • Query caching
   • BI tool caching
   • Warm/cached aggregate tables for top dashboards

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What is usually cheapest in real life?

If you want the lowest infrastructure management overhead:

• Serverless query engine + object storage
• Example: Athena + Parquet + partitioning + aggregates

This is often very cost-effective because you pay mostly for data scanned, and you avoid always-on clusters.

If you have lots of repeated dashboard queries and need predictable performance:

• A dedicated BI warehouse or a cluster with caching
• Example: Snowflake / BigQuery / Databricks SQL / ClickHouse / Redshift RA3
• These can be cheaper than serverless if dashboards are run constantly and the same data is queried repeatedly.

If your workload is mostly dashboards over a curated subset of data:

• ClickHouse is often one of the most cost-efficient options for very fast BI at scale
• Especially when you can store pre-modeled, compressed, denormalized data and want sub-second queries

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Best-practice architecture

A common cost-efficient stack looks like this:

• Raw data lake in object storage
• ETL/ELT layer creates curated BI tables
• Aggregate tables/materialized views for dashboard queries
• Query engine over those curated tables
• BI semantic layer to route dashboards to the right aggregates

This minimizes:

• bytes scanned
• CPU per query
• concurrency cost
• repeated computation

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Rule of thumb

For BI dashboards at petabyte scale, the biggest cost saver is usually not the engine itself — it’s:

• reducing scanned data
• precomputing common metrics
• using columnar storage
• avoiding querying raw fact tables directly

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Simple recommendation

If you want a practical default:

• Start with Parquet in object storage
• Use Trino/Athena/BigQuery/Snowflake depending on cloud
• Build aggregate tables for dashboards
• Add caching
• Only move to an always-on MPP engine if serverless query costs become too high or latency is too variable

If you want, I can also give you:

1. a cloud-by-cloud recommendation (AWS/GCP/Azure), or
2. a cost comparison of Athena vs Trino vs BigQuery vs ClickHouse for BI dashboards.