Practical examples of data compression techniques in 2025

Real-world examples of data compression techniques you already use

Before getting into theory, it helps to start with everyday examples of data compression techniques that show up on your laptop, phone, and servers.

Think about these situations:

• You email a project folder as a single .zip file.
• Your browser loads a web page that feels snappy even on slow Wi‑Fi.
• Your phone stores thousands of photos without filling up instantly.
• You stream 4K video without your connection melting down.
• Your data warehouse scans billions of rows without timing out.

All of those are real examples of data compression techniques doing quiet, heavy lifting.

In practice, most systems combine multiple approaches: general-purpose file compression (like ZIP or gzip), media-specific compression (like JPEG or H.264), and database-oriented compression (like columnar encoding). The best examples are the formats and protocols that have survived years of real-world abuse and optimization.

Lossless compression: examples of data compression techniques that preserve every bit

Lossless techniques give you a perfect reconstruction of the original data. If you’re compressing source code, configuration files, database backups, or medical images for diagnosis, you can’t afford to lose a single bit.

Some of the best examples of lossless data compression techniques in everyday use are:

ZIP archives (DEFLATE under the hood)

The classic .zip file is probably the most familiar example of lossless compression. Most ZIP implementations use the DEFLATE algorithm, which combines:

• LZ77-style dictionary compression to replace repeated byte sequences with references.
• Huffman coding to assign shorter bit patterns to more frequent symbols.

You see this when you:

• Compress a folder before sending it over email.
• Export logs or reports from an application as a single zipped bundle.

ZIP is wildly popular because it’s portable across Windows, macOS, and Linux, and it balances speed with decent compression ratios for text, logs, and mixed content.

gzip and deflate in web servers and APIs

If you inspect HTTP response headers from a modern website, you’ll often see:

Content-Encoding: gzip

This is your browser being sent compressed HTML, CSS, and JavaScript. The server compresses content with gzip (again, DEFLATE-based), and the browser decompresses it on the fly.

This is one of the cleanest examples of data compression techniques directly improving user experience: smaller responses, faster page loads, and lower bandwidth usage. Web servers like Nginx and Apache, and application frameworks in Node.js, Java, and Python, all make this almost trivial to enable.

PNG images for graphics and UI assets

When you need pixel-perfect images—think logos, icons, screenshots—PNG is the lossless format of choice. PNG uses:

• Predictive filtering (to reduce redundancy row by row), then
• DEFLATE compression on the filtered data.

Real examples include:

• UI assets in mobile apps and web frontends.
• Screenshots in documentation and user guides.

Unlike JPEG, PNG won’t introduce artifacts around text or sharp edges, which is why designers and technical writers lean on it heavily.

Modern general-purpose codecs: zstd and Brotli

In the last few years, engineers have increasingly adopted newer algorithms like Zstandard (zstd) and Brotli:

• zstd (from Facebook/Meta) is widely used for logs, backups, and container images. It often delivers better compression than gzip at similar or higher speeds.
• Brotli (developed by Google) is now supported by all major browsers and is frequently used to compress web assets like JavaScript and CSS.

These are great examples of data compression techniques evolving toward higher throughput and better ratios, tuned for modern CPUs and large-scale systems.

For a deeper algorithmic background on lossless compression, the classic reference is the free online text from Stanford University on data compression and coding theory: https://web.stanford.edu/class/ee276/ (Stanford.edu).

Lossy compression: examples of data compression techniques for media

Lossy compression intentionally discards information that humans are unlikely to notice. You can’t get the original back bit-for-bit, but you get something that looks or sounds the same to us.

Here are some of the most important examples of lossy data compression techniques in real products.

JPEG and HEIC for photos

Your phone camera is a goldmine of examples of data compression techniques:

• JPEG uses a pipeline of color space conversion, block-based transforms (DCT), quantization, and entropy coding. It throws away high-frequency details your eyes barely notice, which is why a 10 MB raw image might compress to 1–2 MB.
• HEIC/HEIF (often used as .HEIC on iOS devices) builds on the HEVC video codec. It gives better compression than JPEG at similar or better visual quality, which is why newer phones default to it.

Real examples include:

• Social platforms that recompress your uploads to save space and bandwidth.
• Photo backup services that offer “high quality” (compressed) vs “original quality” tiers.

MP3, AAC, and Opus for audio

Music streaming and voice calls are classic examples of lossy compression at work:

• MP3 was the dominant format for downloadable music for years.
• AAC (used by Apple and many streaming services) delivers better quality at the same bitrate.
• Opus is optimized for real-time voice and is widely used in WebRTC for browser-based calls.

These codecs exploit psychoacoustic models—understanding which sounds your ear can’t distinguish or will naturally mask—to aggressively shrink file sizes while keeping audio subjectively clear.

H.264, H.265, and AV1 for video

Video is where compression really earns its keep. A raw 4K video stream is massive; without compression, consumer internet connections would be useless for video.

Common real-world examples include:

• H.264/AVC: Ubiquitous across streaming, video conferencing, phones, and cameras.
• H.265/HEVC: Better compression than H.264, often used for 4K and HDR.
• AV1: A newer, royalty-free codec backed by major tech companies. As of 2024–2025, AV1 is increasingly used by YouTube and other platforms for higher resolutions and bandwidth savings.

These codecs use motion compensation, block-based transforms, and perceptual quantization to reduce data while keeping video visually acceptable. They are some of the best examples of data compression techniques scaled to global traffic levels.

For a more technical treatment of media compression, the Massachusetts Institute of Technology (MIT) OpenCourseWare materials on digital signal processing and multimedia systems are a solid reference: https://ocw.mit.edu (MIT.edu).

Database and analytics: examples of data compression techniques in storage engines

If you work with data warehouses or analytics platforms, you’re dealing with compression even if you never touch the knobs directly. Columnar databases and modern storage engines aggressively compress data to reduce disk I/O and memory usage.

Columnar compression in data warehouses

Systems like Amazon Redshift, Snowflake, Google BigQuery, and Apache Parquet-based platforms use columnar storage. That means they store each column’s values together, which makes patterns easier to compress.

Examples of techniques they use include:

• Run-length encoding (RLE): Great when a column has repeated values, like status codes or boolean flags. Instead of storing AAAAA, you store “A, repeated 5 times.”
• Dictionary encoding: For columns with a limited set of values (countries, product categories), you map each distinct value to a small integer and store the integers.
• Delta encoding: For sorted numeric columns like timestamps or IDs, you store the difference between consecutive values, which are often small and more compressible.

These are subtle but powerful examples of data compression techniques tuned specifically for analytics workloads, where scanning billions of rows is routine.

Log and backup compression in infrastructure

Operational teams rely heavily on compression for log retention and backups. Some practical patterns:

• Database backups compressed with gzip or zstd to cut storage costs and speed up transfer to cloud storage.
• Log aggregation systems (like Elasticsearch, OpenSearch, or custom pipelines) storing logs in compressed formats to handle massive ingest volumes.

In many modern setups, zstd has become a go-to example of data compression techniques that strike a good balance between CPU cost and compression ratio for large, text-heavy datasets.

Hybrid and modern web: examples include Brotli, HTTP/2, and beyond

Modern web performance work is full of interesting examples of data compression techniques layered together.

Text and binary protocols over HTTP

On the text side, you’ll typically see:

• Brotli or gzip for HTML, CSS, and JavaScript.
• JSON payloads compressed before being sent from APIs.

On the binary side, protocols like gRPC and HTTP/2 use header compression and framing to reduce overhead. HPACK (for HTTP/2) and QPACK (for HTTP/3) are specialized examples of compression techniques designed specifically for HTTP headers, which are repetitive across requests.

Compression in messaging and streaming systems

Systems like Kafka, NATS, and cloud messaging services often support pluggable compression for messages:

• Producers compress message batches with gzip, Snappy, or zstd.
• Brokers store and replicate compressed batches.
• Consumers decompress on read.

Here, the trade-offs are very concrete: higher compression ratios save disk and network but cost CPU. Teams often benchmark different algorithms on their actual message payloads to find the best examples of formats that fit their use case.

How to choose among examples of data compression techniques

With so many examples of data compression techniques available, the decision usually comes down to three axes:

• Lossless vs lossy: Do you need bit-perfect recovery (source code, medical records, scientific data), or is perceptual quality enough (photos, video, audio)?
• Speed vs size: Are you CPU-bound or bandwidth/storage-bound? For high-throughput systems, faster codecs like zstd at moderate levels often beat maximum compression settings.
• Ecosystem support: Do your tools, libraries, and hardware accelerators support the format natively? H.264 and gzip are everywhere; AV1 and Brotli are newer but growing fast.

A practical way to learn is to take a few of the best examples—say, gzip, zstd, and Brotli for text; JPEG and WebP/AVIF for images—and run them on your own data. Measure:

• Compressed size
• Compression and decompression time
• Impact on end-to-end latency and resource usage

That kind of benchmarking quickly turns abstract examples into concrete engineering decisions.

If your data includes health or clinical information, compression choices can intersect with regulatory requirements. The U.S. National Institutes of Health (NIH) has guidance on handling and protecting health data, including storage and transmission considerations: https://www.nih.gov (NIH.gov). While it doesn’t prescribe specific codecs, it’s a good reminder that compressed data is still sensitive data.

FAQ: common questions about examples of data compression techniques

What are some common examples of data compression techniques used on the web?
The most common examples include gzip and Brotli for compressing HTML, CSS, and JavaScript, PNG for lossless images, JPEG and WebP for lossy images, and H.264 for embedded video. HTTP/2 also uses header compression (HPACK) to reduce repetitive headers.

Can you give an example of lossless vs lossy compression for the same image?
Yes. Save a screenshot as PNG (lossless) and as JPEG (lossy) at 80% quality. The PNG will typically be larger but pixel-perfect, while the JPEG will be smaller but may show artifacts around text and high-contrast edges. Both are valid examples of data compression techniques, just optimized for different goals.

Which examples of techniques are best for database workloads?
For databases and analytics, examples include run-length encoding, dictionary encoding, delta encoding, and general-purpose codecs like zstd or LZ4. Columnar formats like Apache Parquet and ORC combine several of these to get high compression and fast scans.

Are there examples of compression that can harm data quality?
Any lossy technique can harm data quality if pushed too hard. Over-compressed JPEG images can look blocky, aggressive video compression can cause banding and motion artifacts, and low-bitrate audio can sound metallic. When applying lossy techniques, always test visually or audibly on real examples from your workload.

How do I pick the right example of a compression method for my project?
Start by deciding whether you can tolerate loss. Then test a few candidates on your own data: for text and logs, compare gzip, zstd, and Brotli; for images, compare PNG, JPEG, and WebP/AVIF; for backups, try zstd at different compression levels. Measure space savings and performance, and choose the method that gives you the best trade-off for your constraints.