Cutting Cross-Cloud Data Costs With BigQuery Lakehouse Caching

Cross-cloud analytics can become expensive when the same data has to move repeatedly between cloud platforms.

A company may have operational data in AWS, analytical data in another platform, and applications running across several clouds. The problem is not always the cost of storing the data. The bigger issue can be the repeated movement of that data whenever an analytics query needs it.

BigQuery lakehouse caching provides a way to reduce some of this repeated data access by keeping frequently used data closer to the query workload.

The goal is not to copy every dataset into BigQuery. The practical approach is to identify workloads where caching can reduce repeated remote reads while keeping the source data in its existing location.

The Cross-Cloud Data Problem

Consider an organization with this architecture:

AWS
 |
 +-- Operational data
 +-- Data lake
 |
 v
Cross-cloud network
 |
 v
BigQuery
 |
 v
Analytics

A query executed in BigQuery may need to access data that physically resides outside the Google Cloud environment.

If the same dataset is queried repeatedly, the system may repeatedly incur remote data access costs and additional latency.

For example:

Dashboard request
      |
      v
BigQuery
      |
      v
Remote data source
      |
      v
Read data
      |
      v
Return results

Now imagine hundreds of dashboard queries using the same underlying data.

The remote data path becomes a potential cost and performance bottleneck.

What Is Lakehouse Caching?

At a high level, lakehouse caching keeps data that is repeatedly accessed available in a location where subsequent queries can access it more efficiently.

Instead of:

Query 1 -> Remote data
Query 2 -> Remote data
Query 3 -> Remote data
Query 4 -> Remote data

the architecture can use a cache:

Query 1 -> Remote data -> Cache
Query 2 -> Cache
Query 3 -> Cache
Query 4 -> Cache

The exact caching behavior depends on the BigQuery feature and the external data source being used.

The architectural idea is straightforward:

Frequently accessed data
        |
        v
Cached closer to query execution
        |
        v
Repeated queries avoid unnecessary remote reads

When Caching Makes Sense

Caching is most useful when the same data is accessed repeatedly.

Good candidates include:

Business dashboards
Reporting workloads
Repeated analytical queries
Frequently accessed historical datasets
Shared datasets used by multiple teams
BI workloads with predictable access patterns

For example:

Sales Dashboard
     |
     +-- Daily revenue
     +-- Regional sales
     +-- Product trends
     +-- Customer metrics

If these queries repeatedly scan the same external data, caching can reduce unnecessary remote access.

When Caching May Not Help

Caching is not a universal solution.

It may provide limited benefit when:

Data is rarely queried.
Every query uses a different dataset.
Data changes continuously.
Queries always request new data.
The working set is too large.
The workload is primarily transactional.

For example:

Query A -> Dataset A
Query B -> Dataset B
Query C -> Dataset C
Query D -> Dataset D

If every query accesses unrelated data only once, a cache has less opportunity to provide value.

Start With the Data Access Pattern

Before implementing caching, analyze how the data is actually used.

Create a simple inventory:

Dataset	Location	Query Frequency	Data Size	Freshness
Sales	AWS	Very high	Large	Daily
Orders	AWS	High	Medium	Near real time
Logs	External lake	Medium	Very large	Hourly
Archive	External lake	Low	Very large	Daily

The most interesting candidates are generally large datasets with repeated access.

A small dataset that is queried infrequently may not justify additional caching complexity.

Separate Storage From Query Access

One of the advantages of a lakehouse architecture is that the source data does not necessarily need to be physically relocated just because another system needs to query it.

A simplified design is:

                    +----------------+
                    | BigQuery       |
                    | Analytics      |
                    +--------+-------+
                             |
                      Query / Cache
                             |
                             v
                    +----------------+
                    | External Data  |
                    | Lake / Cloud   |
                    +----------------+

This can be useful for organizations that want to keep ownership of data in its original cloud while allowing centralized analytics.

Avoid Unnecessary Full Copies

A common response to slow cross-cloud analytics is to copy everything into BigQuery.

That can work, but it introduces another data pipeline.

For example:

AWS
 |
 | ETL / replication
 v
BigQuery Storage
 |
 v
Analytics

Now the team has to manage:

Data replication
Scheduling
Schema changes
Data freshness
Duplicate storage
Pipeline failures
Reconciliation

Caching can be useful when the goal is to optimize repeated access without turning every external dataset into a permanent BigQuery copy.

Think About Data Freshness

Caching introduces an important question:

How fresh does the data need to be?

Consider two workloads.

Daily reporting

Data updated:
02:00

Report generated:
08:00

A cache that is refreshed according to the reporting schedule may be sufficient.

Real-time monitoring

Data updated:
Every few seconds

A cached copy may not provide the freshness required.

Before enabling caching, define the freshness requirement:

Real-time
Near real-time
Hourly
Daily
Weekly

Then determine whether the caching behavior fits that requirement.

Cost Comes From More Than Data Transfer

Cross-cloud analytics costs can involve several components.

A simple model is:

Total Cost
=
Query Processing
+
Remote Data Access
+
Data Transfer
+
Storage
+
Pipeline Operations

Caching can potentially reduce the repeated remote data access portion.

But it may add storage or cache-management costs.

Therefore, compare the complete workload rather than looking at a single billing line.

Build a Cost Model

Suppose a workload reads 1 TB from an external source each day.

Assume:

Daily remote reads:
1 TB

Monthly remote reads:
~30 TB

If the same data is repeatedly queried and can be cached effectively, the architecture may reduce repeated remote reads.

The important calculation is:

Cost without caching
=
Remote reads × query frequency

Cost with caching
=
Initial remote read
+
Cache storage
+
Cache refresh
+
Local query processing

Use actual billing data when evaluating the design.

Do not assume that caching automatically produces savings.

Identify High-Reuse Data

A useful metric is the ratio between data reads and unique data accessed.

For example:

Unique data accessed:      2 TB
Total monthly reads:      40 TB

That suggests a high amount of reuse.

Compare it with:

Unique data accessed:     35 TB
Total monthly reads:      40 TB

The second workload has much less reuse.

Caching is generally more interesting in the first scenario.

Use Query Logs to Find Candidates

Query history can reveal repeated access patterns.

Look for:

Frequently queried tables
Repeated date ranges
Repeated dashboards
Similar SQL statements
Large external scans
High query frequency from BI tools

For example:

08:00 Sales dashboard
09:00 Sales dashboard
10:00 Sales dashboard
11:00 Sales dashboard
...

If each request causes the same external data to be read, that dataset deserves investigation.

Do Not Cache Everything

A cache can become another storage system if it is used without discipline.

Define criteria such as:

Cache only if:

Query frequency > threshold
AND
Remote read cost > threshold
AND
Freshness requirement allows caching
AND
Data reuse is high

This makes caching an optimization strategy rather than a default architecture.

Handling Cache Invalidation

The traditional cache problem still applies:

How do you know when cached data is stale?

Possible strategies depend on the workload.

Time-based refresh

Refresh every hour

Schedule-based refresh

Refresh after daily ingestion

Event-driven refresh

Source update
      |
      v
Refresh cached data

The correct choice depends on how quickly the source data changes.

Consider Schema Changes

External datasets can change.

For example:

Version 1
customer_id
amount

Version 2
customer_id
amount
currency

A caching layer should not be treated as independent of schema management.

Define how the system handles:

Added columns
Removed columns
Renamed columns
Data type changes
Partition changes
Table replacement

Schema compatibility should be tested before production changes are rolled out.

BigQuery SQL Should Still Be Optimized

Caching does not eliminate poor query design.

For example, avoid:

SELECT *
FROM external_sales;

when only a few columns are required.

Prefer:

SELECT
    order_date,
    region,
    amount
FROM external_sales
WHERE order_date >= DATE '2026-11-01';

Good query design reduces the amount of data processed and makes the entire architecture more efficient.

Partition-Aware Queries Matter

If the external dataset is partitioned by date, use filters that allow the query engine to reduce unnecessary data access.

For example:

SELECT
    region,
    SUM(amount)
FROM sales
WHERE order_date >= DATE '2026-11-01'
  AND order_date < DATE '2026-12-01'
GROUP BY region;

A query that scans only the required period is generally more efficient than scanning the complete historical table.

Do Not Confuse Caching With Materialized Views

These approaches solve related but different problems.

Approach	Main Goal
Cache	Reuse frequently accessed data
Materialized view	Store precomputed query results
ETL copy	Create a separate physical dataset
External table	Query data without fully moving it
Aggregation table	Serve repeated analytical queries efficiently

A materialized result can be more appropriate when users repeatedly request the same aggregation.

Caching is more useful when repeated access to underlying data can be served efficiently without rebuilding the complete result each time.

A Practical Cross-Cloud Architecture

Consider an organization with data stored in AWS and analytics performed in BigQuery.

A practical architecture might look like:

                AWS
                 |
        +--------+--------+
        |                 |
        v                 v
   Operational       Data Lake
      Data                |
                           |
                           v
                    External Table
                           |
                           v
                    BigQuery Query
                           |
                    +------+------+
                    |             |
                    v             v
                 Cache          BI Tool

The exact implementation depends on the external storage technology and BigQuery capabilities being used.

The important design principle is that frequently accessed analytical data should not repeatedly cross the same expensive network boundary when the workload can safely reuse it.

Monitoring a Cached Workload

After implementing caching, measure whether it actually improves the workload.

Track:

Query latency
Data processed
Remote data reads
Cache hit behavior
Storage cost
Data freshness
Query frequency
Transfer-related costs

Compare before and after.

For example:

Metric	Before	After
Average query time	12 sec	4 sec
Remote data read	800 GB/day	150 GB/day
Query volume	2,000/day	2,000/day
Data freshness	1 hour	1 hour

The numbers are illustrative, but this is the kind of measurement required to validate the architecture.

Common Mistakes

Caching everything

Only high-reuse datasets normally justify caching.

Ignoring freshness

A cheaper query is not useful if it returns data that is too stale.

Assuming cache equals free storage

Caching can introduce additional storage and operational costs.

Ignoring query optimization

Poor SQL can remain expensive even after introducing caching.

Copying all data unnecessarily

A permanent replicated dataset may create more operational work than needed.

Failing to measure the result

Caching should be treated as a measurable optimization.

Forgetting schema evolution

External source changes can break downstream queries and cached workloads.

When Lakehouse Caching Is a Good Fit

Consider caching when:

Data remains in another cloud.
BigQuery performs repeated analytical queries against it.
The same data is accessed frequently.
Cross-cloud reads are expensive or slow.
Data freshness requirements allow reuse.
The cost of caching is lower than repeated remote access.

It may not be the right choice when data is rarely reused or must always be read directly from the latest source state.

Practical Implementation Checklist

Before enabling a caching strategy, answer these questions:

Where is the source data stored?
How often is it queried?
How much data is transferred per query?
How much of that data is reused?
How fresh must the data be?
What is the cost of remote access?
What is the cost of cached storage?
How will cache refresh work?
How will schema changes be handled?
Which queries benefit most?
How will cache effectiveness be measured?
What is the fallback if cached data is unavailable?

Advantages

A well-designed caching strategy can provide:

Lower repeated cross-cloud data access
Reduced query latency
Better performance for recurring analytical workloads
Less pressure on remote data sources
Potential reduction in data-transfer-related costs
A way to optimize existing multi-cloud architectures without moving every dataset

Disadvantages

There are also trade-offs:

Cached data can become stale.
Additional storage may be required.
Cache refresh logic needs monitoring.
Schema changes require coordination.
Low-reuse datasets may not benefit.
Poorly designed caching can add complexity without reducing costs.

Conclusion

Cross-cloud analytics does not always require moving all data into the cloud where the queries run.

BigQuery lakehouse caching can be useful when the same external data is queried repeatedly and the freshness requirements allow that data to be reused closer to the query workload.

The right approach starts with measurement. Identify which datasets generate repeated remote reads, determine how much data is actually reused, calculate the complete cost of remote access, and compare it with the cost and operational overhead of caching.

Caching should be treated as a workload-specific optimization, not as a replacement for good data architecture.

When applied to high-reuse analytical workloads, it can reduce unnecessary cross-cloud data movement while allowing the source data to remain where it already belongs.