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Databases · head to head

BigQuery vs Fivetran HVR

BigQuery logo

BigQuery

Databases

Google Cloud's serverless analytical warehouse, billed either by bytes scanned per query or by reserved compute slots.

From
Free
Rated
-
Full reviewWebsite
Fivetran HVR logo

Fivetran HVR

Databases

Log-based change data capture for database replication and synchronization

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only BigQuery has a free tier, so it costs nothing to try first.
  • Each has a real cost: BigQuery on-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.; Fivetran HVR pricing not published separately; part of Fivetran's usage model
  • They diverge on capability: BigQuery covers Serverless compute, Fivetran HVR covers Log-based change capture.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery and Fivetran HVR actually diverge.

Attributes where BigQuery and Fivetran HVR differ
AttributeBigQueryFivetran HVR
Starting priceFreeOn request
Pricing modelusage-basedUsage-based, integrated with Fivetran billing
Free tierYesNo
PlatformsWeb, Cloud APIData Warehouse, Data Lake, Operational Databases
Founded20082012

Identical on both: user rating (Not yet rated), category (Databases).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in BigQuery

  • Serverless compute
  • Separation of storage and compute
  • Two pricing models
  • Partitioning and clustering
  • Materialised views
  • BigQuery ML
  • Storage Write API
  • BI Engine

Only in Fivetran HVR

  • Log-based change capture
  • Real-time replication
  • Schema evolution handling
  • Multi-table consistency
  • Zero-impact monitoring
  • Fivetran integration

What people use each for

The jobs each tool is most often brought in to do.

BigQuery

  • A warehouse for an organisation already on Google Cloud, where identity, logging and billing are consolidated in the same placenot Fivetran HVR
  • Bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster runningnot Fivetran HVR
  • Event and clickstream analytics ingested continuously through the Storage Write API and queried without a load windownot Fivetran HVR
  • Analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portabilitynot Fivetran HVR

Fivetran HVR

  • Real-time database replication to data warehousesnot BigQuery
  • Maintaining operational copies for analyticsnot BigQuery
  • Disaster recovery and business continuitynot BigQuery
  • Feeding AI systems with fresh governed datanot BigQuery

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

BigQuery

  • On-demand billing charges for bytes read from every column a query references, so an unqualified select or a missing partition filter turns a routine query into a large bill, and the cost is discovered after the fact rather than at review time.
  • There is no way to join tables that live in different regions, so a data estate split across regions for residency reasons has to be reconciled with copies and the storage and transfer that implies.
  • It is not built for point lookups; retrieving a single row has latency measured in hundreds of milliseconds or more, so BigQuery cannot serve an application's read path and always needs a second store in front of it.
  • Frequent small mutations run into DML concurrency limits and the cost of rewriting storage blocks, so a workload that updates individual rows continuously behaves badly compared with an append-only design.
  • The compute exists only inside Google Cloud, so while tables can be exported, the accumulated GoogleSQL, scheduled queries, authorised views, ML models and IAM structure do not move, and switching warehouses is a rewrite of the analytical layer.

Fivetran HVR

  • Pricing not published separately; part of Fivetran's usage model
  • Requires Fivetran relationship for implementation
  • Limited standalone documentation and community resources
  • May be overkill for simple batch replication needs
  • Competitive offerings provide more transparent per-connector pricing

Pricing, plan by plan

BigQuery

Free
  • Free TierFree
    • 1TB queries/month
    • 10GB storage/month
    • Standard support
  • On-demand$6.25/TB
    • Pay per query
    • Pay per storage
    • All features

Fivetran HVR

On request

No published plan breakdown. See the Fivetran HVR review.

Which should you pick?

Choose BigQuery if

  • You need serverless compute.
  • You want to start without paying.
  • You work on Web, Cloud API.
  • You also want separation of storage and compute.
BigQuery review

Choose Fivetran HVR if

  • You need log-based change capture.
  • You work on Data Warehouse, Data Lake, Operational Databases.
  • You also want real-time replication.
Fivetran HVR review

Questions people ask

Is BigQuery or Fivetran HVR better?
Neither clearly leads. BigQuery starts at Free and Fivetran HVR at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery or Fivetran HVR?
BigQuery has a free tier; the other does not. Paid plans start at Free for BigQuery and On request for Fivetran HVR.
Does BigQuery or Fivetran HVR run on more platforms?
BigQuery runs on Web, Cloud API. Fivetran HVR runs on Data Warehouse, Data Lake, Operational Databases.
Can I use BigQuery for free?
Yes. BigQuery has a free tier, so you can try it without paying. Fivetran HVR starts at On request.
What is BigQuery best used for?
BigQuery is most often used for a warehouse for an organisation already on google cloud, where identity, logging and billing are consolidated in the same place, bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster running, event and clickstream analytics ingested continuously through the storage write api and queried without a load window, analytics teams with no infrastructure staff, where the absence of anything to tune or patch is worth more than dialect portability. Of those, a warehouse for an organisation already on google cloud, where identity, logging and billing are consolidated in the same place and bursty analytical workloads with long idle periods, where paying per query beats keeping a cluster running are not what Fivetran HVR is typically brought in for.
What can BigQuery do that Fivetran HVR cannot?
BigQuery covers Serverless compute, Separation of storage and compute, Two pricing models, Partitioning and clustering. Fivetran HVR covers Log-based change capture, Real-time replication, Schema evolution handling, Multi-table consistency.

Answered from the vendors’ own pages

BigQuery: How is BigQuery actually billed?

Storage is billed separately from compute. Compute is either on-demand, priced by the bytes a query reads from the referenced columns, or capacity-based, where you reserve autoscaling slots. Most cost surprises come from on-demand queries that scan more than expected.

Fivetran HVR: What databases does Fivetran HVR support?

HVR supports major databases including Oracle, SQL Server, PostgreSQL, MySQL, and others that emit transaction logs.

Source
BigQuery: How do I control query cost?

Partition and cluster tables so queries prune data, select only the columns needed, use materialised views for repeated aggregations, and set maximum bytes billed on queries so a runaway scan fails instead of billing.

Fivetran HVR: Is HVR part of standard Fivetran pricing?

Yes, HVR is integrated into Fivetran's usage-based pricing model with standard per-row-synced and transformation costs.

Source
BigQuery: Can I use it without being on Google Cloud?

The service only runs on Google Cloud. BigQuery Omni can query data held in S3 or Azure storage, but the compute is still Google's and the account relationship is still with Google.

BigQuery: Is it suitable for serving application queries?

No. Latency for single-row reads is far too high. BigQuery is an analytical warehouse and application read paths need a transactional database or a cache in front of it.

BigQuery: When should I move from on-demand to capacity pricing?

When on-demand spend becomes both large and predictable, or when unpredictable spend is a bigger problem than query queueing. The switch trades a variable bill for a fixed one plus contention between workloads.

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