When evaluating a data warehouse, query performance gets most of the attention.
But there is another number that can have a major impact on real-world operations:
How fast can you load data?
A warehouse may deliver excellent query performance, but if loading the daily data takes hours, the entire analytics pipeline can become a bottleneck.
This is where GBase Database(GBase 8a MPP Cluster) focuses on a different problem: high-throughput data loading and large-scale analytical processing.
In benchmark scenarios, GBase Database(GBase 8a) has demonstrated data loading throughput of more than 30 TB per hour.
A Simple Data Loading Example
A typical bulk-loading operation can look like this:
LOAD DATA INFILE '/data/warehouse/daily_transactions_20260809.csv'
INTO TABLE fact_transactions
FIELDS TERMINATED BY ','
OPTIONALLY ENCLOSED BY '"'
LINES TERMINATED BY '\n';
After loading, teams can verify the amount of data processed and measure the elapsed time:
SELECT
COUNT(*) AS total_rows,
pg_size_pretty(
SUM(pg_total_relation_size(relid))
) AS total_size,
current_timestamp - load_timestamp AS load_duration
FROM fact_transactions_meta;
The exact SQL and monitoring approach will depend on the deployment and workload, but the principle is straightforward:
Measure the complete loading pipeline, not just the database's theoretical throughput.
Why Loading Speed Matters
Consider a data warehouse receiving 30 TB of new data.
At 30 TB per hour, the raw loading throughput is roughly:
30 TB/hour → 500 GB/minute → 8.3 GB/second
That changes the operational window significantly.
For organizations running daily or near-real-time data pipelines, faster loading can mean:
Shorter ETL windows
Fresher analytical data
Less pressure on batch schedules
More time for downstream transformations
Better utilization of the warehouse infrastructure
Of course, real production throughput depends on factors such as data format, network bandwidth, storage performance, cluster size, table design, and workload concurrency.
The benchmark number should therefore be treated as a reference point, not a guaranteed production result.
Compression Changes the Storage Equation
Loading speed is only one side of the equation.
Large analytical datasets also create a storage problem.
GBase Database(GBase 8a) uses columnar storage and compression techniques designed for analytical workloads. Under certain workloads, reported compression ratios can reach up to 1:30.
That means:
100 TB raw data
↓
1:30 compression
↓
~3.3 TB compressed data
This is particularly relevant for large-scale analytical environments.
However, a 1:30 compression ratio should not be interpreted as a universal result. Actual compression depends heavily on data characteristics, column cardinality, sorting, encoding, and workload design.
The right question isn't:
"Can my database compress data 30:1?"
It's:
"What compression ratio can my actual dataset achieve?"
That is something worth measuring during a POC.
Loading + Compression = A Different Value Proposition
This is where GBase Database(GBase 8a MPP Cluster) becomes interesting.
Its positioning isn't to be a universal database for every workload.
It is designed specifically around large-scale analytical workloads, combining:
MPP architecture + columnar storage + compression + high-throughput data loading
For a data warehouse team, these capabilities address two fundamental problems:
Move data in quickly.
Store and analyze it efficiently.
The combination becomes especially important as data volumes move from terabytes toward hundreds of terabytes or petabytes.
What Should You Test in a POC?
If you're evaluating GBase Database(GBase 8a) for a data warehouse migration, don't test only query latency.
Test at least four dimensions:
1. Data Loading
Measure sustained throughput with production-like data volumes.
2. Compression
Calculate the actual raw-to-compressed storage ratio using representative datasets.
3. Query Performance
Run your real BI queries, joins, aggregations, and reports—not just vendor-provided benchmarks.
4. Concurrency
Test loading and analytical queries at the same time.
This last test is particularly important.
A warehouse that loads data quickly in isolation may behave very differently when dozens or hundreds of analytical queries are running simultaneously.
The Bigger Picture
For large analytical systems, database performance isn't just about:
"How fast is my query?"
It is also about:
"How quickly can I ingest data, how efficiently can I store it, and how consistently can I analyze it at scale?"
That's the problem GBase Database(GBase 8a MPP Cluster) is designed to address.
The headline number—30+ TB/hour loading throughput—is interesting.
But the more important takeaway is the architecture behind it:
For massive analytical workloads, ingestion performance and storage efficiency can be just as important as query performance.
And those are the numbers worth testing with your own data.
