Data Compression for Analytics: Reducing Storage and Improving Performance
Data compression reduces storage costs and can improve query performance by reducing I/O. Learn how compression techniques work for analytical workloads and when to use them.
Data compression reduces the storage footprint of analytical data while often improving query performance. For analytics workloads that scan large amounts of data, compression reduces I/O operations, which typically dominate query execution time.
Understanding compression enables better storage economics and faster queries in analytical systems.
Why Compression Matters for Analytics
Storage Cost Reduction
Analytical datasets grow continuously:
- Years of historical transactions
- High-cardinality event data
- Multiple copies for redundancy
- Staging, intermediate, and serving layers
Compression multiplies storage budgets - a 5x compression ratio means storing 5 years of data for the cost of 1.
Performance Improvement
Counterintuitively, compression often speeds queries:
- Storage I/O is slow relative to CPU
- Reading less data reduces I/O time
- Decompression is fast with modern algorithms
- Net effect is usually faster queries
For analytical workloads, compression helps performance.
Network Efficiency
Data moves between systems:
- Replication across regions
- Transfers from data lakes to warehouses
- Query results to clients
Compressed data moves faster.
How Analytical Compression Works
Column-Oriented Storage
Analytics databases store data by column:
Row storage: [id, name, date, amount] [id, name, date, amount] ...
Column storage: [id, id, id, ...] [name, name, name, ...] [date, date, date, ...] ...
Column storage enables better compression because similar values cluster together.
Column Encoding
Before general compression, columns use specialized encodings:
Dictionary encoding: Replace repeated values with integer codes.
Original: ["active", "active", "pending", "active", "closed"]
Dictionary: {0: "active", 1: "pending", 2: "closed"}
Encoded: [0, 0, 1, 0, 2]
Low-cardinality columns compress dramatically.
Run-length encoding: Store repeated values as count + value.
Original: [100, 100, 100, 100, 200, 200, 300]
Encoded: [(4, 100), (2, 200), (1, 300)]
Sorted or clustered data benefits from RLE.
Delta encoding: Store differences between sequential values.
Original: [1000, 1001, 1003, 1004, 1008]
Encoded: [1000, 1, 2, 1, 4]
Sequential or nearly-sequential values compress well.
Bit packing: Use minimum bits needed for value range.
Values 0-15 need only 4 bits instead of 32
1 million values: 4MB instead of 128MB
Small value ranges compress efficiently.
General Compression
After encoding, general compression algorithms apply:
LZ4: Very fast compression and decompression, moderate ratio.
Snappy: Similar to LZ4, optimized for Google workloads.
Zstandard (ZSTD): Better compression ratio, still fast.
Gzip: Maximum compression, slower.
Modern databases typically use LZ4 or Zstandard.
Compression in Practice
Cloud Data Warehouses
Modern warehouses handle compression automatically:
Snowflake: Automatic compression, no configuration needed.
BigQuery: Columnar storage with automatic compression.
Redshift: Column encodings plus LZO/Zstandard compression.
Databricks: Parquet files with Snappy/Zstandard.
Trust the defaults unless you have specific needs.
Data Lakes
Lake storage requires format choices:
Parquet: Columnar format with built-in compression. The standard for analytics.
ORC: Similar to Parquet, common in Hadoop ecosystems.
Avro: Row-based with compression, better for write-heavy.
CSV/JSON: Compress with Gzip, but consider columnar formats instead.
Parquet with Zstandard is the common choice for analytical lakes.
File Formats and Compression
Combine format and compression thoughtfully:
parquet + zstd → Best compression ratio for analytics
parquet + snappy → Faster decompression, good compression
csv.gz → Legacy compatibility, less efficient
json.gz → Semi-structured data, least efficient
Format choice matters more than compression algorithm.
The Codd AI Platform works with compressed data sources seamlessly, providing a semantic layer that delivers consistent business metrics regardless of underlying storage optimizations.
Compression Tradeoffs
Write Performance
Compression adds write overhead:
- CPU time for compression
- Possible buffering for better compression
- Delayed visibility for compressed data
For write-heavy workloads, choose faster compression.
Query Patterns
Different queries benefit differently:
Full scans: Benefit most from compression - less I/O.
Point lookups: May not benefit - still need to read and decompress blocks.
Aggregations: Benefit from compression plus column pruning.
Analytical patterns benefit most.
CPU vs I/O Balance
The tradeoff depends on your bottleneck:
I/O bound: Compression helps - trade CPU for less I/O.
CPU bound: Compression may hurt - adds CPU load.
Balanced: Modern systems are usually I/O bound for analytics.
Monitor to understand your bottleneck.
Compression Ratio vs Speed
Algorithms trade ratio for speed:
| Algorithm | Compression Ratio | Compression Speed | Decompression Speed |
|---|---|---|---|
| LZ4 | Lower | Fastest | Fastest |
| Snappy | Lower | Very fast | Very fast |
| Zstandard | Higher | Fast | Fast |
| Gzip | Highest | Slow | Moderate |
Choose based on read vs write frequency.
Optimizing Compression
Data Ordering
Sort data for better compression:
- Group similar values together
- Enable run-length encoding
- Improve delta encoding efficiency
Clustering by commonly filtered columns helps both compression and query performance.
Column Selection
Some columns compress better:
High compression: Status codes, categories, dates, sorted keys.
Low compression: UUIDs, hashes, random numbers.
Include compression in data type decisions.
Partition Strategy
Smaller files may compress less efficiently:
- Compression works better with more data
- Very small partitions reduce compression ratio
- Balance partition granularity with compression needs
Avoid over-partitioning.
Encoding Selection
When manual control is available:
- Dictionary encoding for low-cardinality columns
- Delta encoding for sorted numeric columns
- Raw encoding for random data
Match encoding to data characteristics.
Measuring Compression
Compression Ratio
Calculate actual compression:
Compression Ratio = Uncompressed Size / Compressed Size
A ratio of 5 means data is 1/5 the original size.
Storage Analysis
Understand your storage:
- Total storage versus logical data size
- Compression ratio by table
- Storage growth trends
- Cost per TB with current compression
Storage analysis guides optimization.
Query Impact
Measure compression effect on queries:
- Query time with different compression
- I/O bytes read
- CPU utilization during queries
- Decompression overhead
Performance testing validates compression choices.
Compression Best Practices
Use Columnar Formats
For analytical data:
- Parquet is the standard
- ORC for Hive ecosystems
- Avro for streaming/write-heavy
Columnar formats enable best analytical compression.
Let Databases Decide
Modern databases optimize well:
- Automatic encoding selection
- Adaptive compression
- Optimized for their architecture
Override defaults only with good reason.
Consider the Full Pipeline
Compression applies throughout:
- Source extraction
- Network transfer
- Landing storage
- Transformation stages
- Serving layer
Compress at each stage appropriately.
Monitor and Adjust
Compression isn't set-and-forget:
- Data characteristics change
- New data patterns emerge
- Technology improves
- Requirements evolve
Review compression effectiveness periodically.
Compression and AI Analytics
Compression supports AI workloads:
Training data: Large training datasets benefit from compression.
Feature storage: Feature stores compress well with encoding.
Model serving: Compressed models reduce memory and transfer.
Inference data: Compressed inputs reduce I/O for predictions.
Context-aware analytics platforms that combine compression with intelligent caching and semantic understanding deliver fast, cost-effective AI-powered analytics at scale.
Getting Started
Organizations optimizing compression should:
- Baseline current state: Measure current compression ratios and storage costs
- Identify opportunities: Find tables with poor compression or high storage
- Choose appropriate formats: Use columnar formats for analytical data
- Enable default compression: Let databases optimize automatically
- Test performance impact: Verify compression helps (or at least doesn't hurt) queries
- Monitor ongoing: Track compression effectiveness over time
Compression is low-hanging fruit for analytics optimization - modern defaults work well, storage costs decrease, and query performance often improves.
Questions
Compression typically improves analytical query performance by reducing I/O - reading less data from storage means faster queries. The CPU cost of decompression is usually much smaller than the I/O savings. However, compression adds overhead for write operations and can slow row-level lookups.