Decision comparison
Exasol vs Google BigQuery
Exasol and BigQuery answer the same requirement from opposite directions. Exasol is a cluster you size and pay for by capacity, deployable on your own hardware or inside your own cloud account, optimised for fast in-memory analytical queries at high concurrency. BigQuery is serverless and Google Cloud only: there is no cluster, queries run on managed capacity, and you pay per terabyte scanned or buy committed slots.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
All 2 are cloud data warehouses.
Quick Comparison
| Decision factor | Exasol | Google BigQuery |
|---|---|---|
| What it is | A high-performance in-memory MPP analytics database, deployable on your own hardware, in your own cloud account, or as SaaS | Google Cloud's serverless data warehouse, with no infrastructure to size or manage |
| Operating model | You choose and size a cluster, whether self-hosted or managed | No cluster exists; queries are dispatched onto Google-managed capacity |
| Pricing shape | Contact for pricing | BigQuery offers two compute pricing models. On-demand pricing charges for bytes processed by each query, billed per TiB, with the first 1 TiB of query data per month free. Capacity pricing charges for compute capacity per slot-hour instead. Storage is billed separately, and BigQuery also has a free usage tier and free operations. |
| Deployment | Your own hardware, your own cloud account, or Exasol SaaS | Google Cloud only |
| Performance approach | In-memory columnar processing with self-tuning indexes for low-latency queries | Massive parallelism over columnar storage, with automatic scaling per query |
| Streaming and ML | Standard connectivity to streaming and ML tooling | Storage Write API for streaming, and BigQuery ML for models written in SQL |
| Best fit | Steady high-concurrency analytics where fixed cost and raw speed matter | Spiky or unpredictable analytics where nobody should be sizing infrastructure |
Exasol
- What it is:
- A high-performance in-memory MPP analytics database, deployable on your own hardware, in your own cloud account, or as SaaS
- Operating model:
- You choose and size a cluster, whether self-hosted or managed
- Pricing shape:
- Contact for pricing
- Deployment:
- Your own hardware, your own cloud account, or Exasol SaaS
- Performance approach:
- In-memory columnar processing with self-tuning indexes for low-latency queries
- Streaming and ML:
- Standard connectivity to streaming and ML tooling
- Best fit:
- Steady high-concurrency analytics where fixed cost and raw speed matter
Google BigQuery
- What it is:
- Google Cloud's serverless data warehouse, with no infrastructure to size or manage
- Operating model:
- No cluster exists; queries are dispatched onto Google-managed capacity
- Pricing shape:
- BigQuery offers two compute pricing models. On-demand pricing charges for bytes processed by each query, billed per TiB, with the first 1 TiB of query data per month free. Capacity pricing charges for compute capacity per slot-hour instead. Storage is billed separately, and BigQuery also has a free usage tier and free operations.
- Deployment:
- Google Cloud only
- Performance approach:
- Massive parallelism over columnar storage, with automatic scaling per query
- Streaming and ML:
- Storage Write API for streaming, and BigQuery ML for models written in SQL
- Best fit:
- Spiky or unpredictable analytics where nobody should be sizing infrastructure
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Exasol | Google BigQuery |
|---|---|---|
| Docker Hub pulls(Product adoption) | 2.2M | Not available |
| GitHub commits, 90d(Developer adoption) | 25 | Not available |
| GitHub stars(Developer adoption) | 81 | Not available |
| Search interest(Market interest) | 0 | 11 |
| Hacker News mentions, 90d(Community interest) | 1 | 7 |
| npm weekly downloads(Developer adoption) | 355 | 3.3M |
| PyPI weekly downloads(Developer adoption) | 239.4k | 33.5M |
| Stack Overflow questions(Community interest) | 89 | 26.2k |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Exasol
September 21, 2026Package vulnerabilities
npm · @exasol/exasol-driver-ts@0.8.0 · PyPI · pyexasol@2.4.1
0 vulnerabilities
across 2 packages
Repository security score
Not available
Google BigQuery
Package vulnerabilities
npm · @google-cloud/bigquery@9.0.3 · PyPI · google-cloud-bigquery@3.45.2
0 vulnerabilities
across 2 packages
Repository security score
Not available
Feature Comparison
| Feature | Exasol | Google BigQuery |
|---|---|---|
| Query | ||
| Standard SQL analytics | Full support | Full support |
| In-memory processing | Full support | Partial support |
| Automatic query tuning | Full support | Full support |
| Materialised views | Full support | Full support |
| Operations | ||
| No infrastructure to size | Not verified | Full support |
| Predictable fixed cost | Full support | Partial support |
| Pay only for queries run | Not verified | Full support |
| Instant scaling for bursts | Partial support | Full support |
| Deployment | ||
| Self-hosted on your own hardware | Full support | Not verified |
| Run in your own cloud account | Full support | Not verified |
| Fully managed service | Full support | Full support |
| Multi-cloud availability | Partial support | Not verified |
| Platform | ||
| Machine learning in SQL | Partial support | Full support |
| Streaming ingestion | Partial support | Full support |
| Native BI integration | Full support | Full support |
| Query data in object storage | Partial support | Full support |
Query
Standard SQL analytics
In-memory processing
Automatic query tuning
Materialised views
Operations
No infrastructure to size
Predictable fixed cost
Pay only for queries run
Instant scaling for bursts
Deployment
Self-hosted on your own hardware
Run in your own cloud account
Fully managed service
Multi-cloud availability
Platform
Machine learning in SQL
Streaming ingestion
Native BI integration
Query data in object storage
Which approach fits
Exasol and BigQuery answer the same requirement from opposite directions. Exasol is a cluster you size and pay for by capacity, deployable on your own hardware or inside your own cloud account, optimised for fast in-memory analytical queries at high concurrency. BigQuery is serverless and Google Cloud only: there is no cluster, queries run on managed capacity, and you pay per terabyte scanned or buy committed slots.
When each approach fits
Choose Exasol if:
Choose Exasol when utilisation is high and steady, when a fixed bill matters more than elasticity, or when the database must run inside your own infrastructure. In-memory processing with self-tuning indexes targets interactive analytical queries with many concurrent users, and capacity pricing means an analyst writing an expensive query does not change the invoice.
Choose Google BigQuery if:
Choose BigQuery when the workload is spiky or unpredictable and nobody should be sizing infrastructure. Queries scale automatically, idle periods cost only storage, and BigQuery ML and the Storage Write API extend the warehouse into modelling and streaming without separate services. Committed slot reservations cap spend when per-scan pricing becomes unpredictable.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
Will per-scan pricing cost more than a fixed cluster?
It depends on how much data your queries read. A team running a few hundred well filtered queries a day over partitioned tables usually pays less than a cluster sized for peak, because the idle hours are free. A team running unfiltered scans over large tables can pay a great deal. BigQuery's slot reservations exist precisely for organisations that would rather have the fixed bill Exasol gives by default.
Can we run either outside a public cloud?
Exasol can, on your own hardware or inside your own cloud account. BigQuery cannot; it runs on Google Cloud only. BigQuery Omni queries data in AWS and Azure, but the service itself remains Google-operated. If an on-premise or data-residency requirement applies, it settles the comparison before anything else is weighed.
What does serverless actually remove?
Cluster sizing, patching, scaling decisions and the capacity-planning meeting. There is no node count to choose and no cluster to pause, so there is also nothing to forget to pause. What it does not remove is query discipline: an unpartitioned table scanned repeatedly is expensive on BigQuery in a way it is not on a fixed-capacity cluster, where the same query just runs slowly.
How do the machine learning stories compare?
BigQuery ML lets analysts train and run models with SQL statements against warehouse tables, which suits teams whose modelling work is forecasting and classification rather than deep learning. Exasol integrates with external ML tooling through standard connectivity instead. If SQL-native modelling would genuinely be used, that is a real difference; if your data scientists work in Python notebooks, it is not.
Which handles high concurrency better?
Exasol is built for many concurrent analytical users on one cluster, with in-memory data and automatic indexing keeping latency low. BigQuery handles concurrency by dispatching onto shared managed capacity, which absorbs bursts well but gives less direct control over latency at the top end. For a dashboard used by hundreds of people simultaneously, test both at your real concurrency rather than trusting either claim.