Columnar compression isn't new technology.
But when you're dealing with hundreds of terabytes or petabytes of analytical data, the difference between 10:1 and 30:1 compression can become a major infrastructure consideration.
For GBase Database(GBase 8a MPP Cluster), reported columnar compression ratios can reach 1:20 to 1:30 under suitable data characteristics and workloads.
So what does that actually mean?
Let's run the numbers.
From 100 TB to 4 TB
Take a simple example.
If 100 TB of raw data achieves a 1:25 compression ratio:
Raw data
100 TB
↓
1:25 compression
↓
~4 TB compressed
That means approximately 96 TB of raw storage volume is eliminated from the compressed representation.
The calculation is straightforward:
Compressed Size = Raw Size ÷ Compression Ratio
100 TB ÷ 25 = 4 TB
At a 1:20 ratio:
100 TB ÷ 20 = 5 TB
At a 1:30 ratio:
100 TB ÷ 30 ≈ 3.33 TB
So the same 100 TB dataset could theoretically occupy roughly 3.3–5 TB after compression, depending on the actual compression ratio achieved.
That's a significant difference at warehouse scale.
Why Can Columnar Data Compress So Well?
The basic principle is relatively simple.
Traditional row-oriented storage keeps different columns together:
Row 1: ID | Date | Region | Product | Amount
Row 2: ID | Date | Region | Product | Amount
Row 3: ID | Date | Region | Product | Amount
Columnar storage reorganizes the data:
ID: 101, 102, 103, 104...
Date: 2026, 2026, 2026, 2026...
Region: US, US, US, US...
Amount: 100, 120, 100, 110...
Values within the same column tend to have similar data characteristics.
That creates opportunities for techniques such as:
Dictionary encoding
Run-length encoding
Delta encoding
Bit packing
Sorting and clustering
General-purpose compression algorithms
The result can be substantially better compression for analytical datasets.
But 1:30 Is Not a Universal Number
This is one of the most important points when evaluating compression.
A claimed 1:20–1:30 compression ratio should not be interpreted as a guaranteed result for every dataset.
Actual compression depends on the data.
For example:
Highly repetitive data may compress extremely well.
Low-cardinality columns often provide excellent compression opportunities.
Sorted or well-clustered data can also improve compression.
On the other hand, high-cardinality or highly random data may achieve much lower ratios.
So during a POC, don't ask:
"Does GBase Database(GBase 8a) support 1:30 compression?"
Ask:
"What compression ratio does my production dataset achieve?"
That's the number that matters.
Storage Savings Are Only Part of the Story
Storage reduction can obviously affect infrastructure costs.
But the value of columnar compression goes beyond simply buying fewer disks.
Less physical data can also mean:
Lower storage footprint
Less I/O
More data fitting into cache
Faster analytical scans in suitable workloads
Lower backup volume
Reduced data movement
Of course, the actual cost impact depends on the complete infrastructure architecture, including replication, backup, storage tiers, compute, and cloud or on-premise pricing.
That's why a POC should measure total infrastructure consumption, rather than calculating savings from raw storage alone.
Compression Isn't the Only Optimization
Another interesting capability in GBase Database(GBase 8a MPP Cluster) is its approach to analytical indexing.
For wide analytical tables, manually designing indexes can become increasingly difficult.
Imagine a table with hundreds—or even thousands—of columns.
Which columns should be indexed?
How many indexes are enough?
When should they be rebuilt?
What happens when query patterns change?
GBase Database(GBase 8a) provides coarse-grained intelligent indexing designed to reduce this manual maintenance burden, with indexes automatically matched during query processing.
For analytical workloads, that can be valuable because performance optimization isn't only about compression.
It's also about reducing the amount of data that needs to be examined.
What Should You Measure in a POC?
If you're evaluating GBase Database(GBase 8a) for a large data warehouse, measure at least four things:
1. Compression Ratio
Load representative production data and calculate:
Raw Size ÷ Compressed Size
2. Query Performance
Test real analytical queries, especially large scans, joins, aggregations, and BI workloads.
3. Storage and I/O
Measure actual storage consumption and I/O behavior rather than relying only on theoretical compression ratios.
4. Operational Effort
Track how much DBA work is required for indexing, maintenance, loading, backup, and capacity management.
This last metric is often ignored.
A database that saves storage but requires significantly more manual administration may not deliver the expected TCO advantage.
The Bigger Picture
For small datasets, compression ratios may not dramatically change the economics.
At 100 TB, 500 TB, or multiple petabytes, the equation becomes very different.
A 1:20 compression ratio means:
1 PB raw
↓
~50 TB compressed
At 1:30:
1 PB raw
↓
~33.3 TB compressed
Those are not minor differences.
But the real lesson isn't simply:
"Higher compression is better."
It's:
Measure compression together with query performance, I/O, and operational complexity.
For large analytical workloads, GBase Database(GBase 8a MPP Cluster) combines columnar storage, compression, MPP processing, and intelligent indexing around one core objective:
Process massive datasets efficiently without turning every performance optimization into a manual DBA project.
And that's the number worth testing with your own data.
