Byte2Bit™ Atlas: Storage optimization layer for numeric multidimensional data.
Cloud storage cost reduction
Lower monthly storage spend with lossless compression that scales across large sensor archives.
Faster analytics pipelines
Fast decompression and selective retrieval help teams query and process data quickly.
Lossless verification
Bit-for-bit reconstruction and chunk-level validation help teams trust compressed archives over time.
Benchmarks and cost analyses built on NOAA GFS, ERA5 Zarr, and large sensing datasets show how Byte2Bit™ Atlas can reduce storage and egress costs while preserving chunked, query-friendly workflows across GRIB, NetCDF, Zarr, HDF5, NumPy, and related numeric formats.
Benchmark-backed results.
Methodology linked below.
InCommodities
Danish algorithmic energy trading
20%
Better compression on raw global weather data versus zlib, with faster decompression than tested open-source alternatives.
Weather dataEuropean Weather Company
10M+ daily forecast users
37%
Storage reduction as compared to Zstd on DMI HARMONIE GRIB and Zarr weather data.
Zarr forecastingNordic Commodities Firm
Algorithmic trading across 10+ markets
2.10x
Compression ratio on GRIB2 sample data, reducing file size from 2.50 GB to 1.19 GB.
GRIB2"We tested Byte2Bit’s lossless compression on raw global weather data across multiple variables, benchmarking against zlib (compression level 5) and other open-source alternatives. In our evaluation, Byte2Bit achieved up to 20% better compression ratios while improving decompression performance significantly. Based on our results, it outperforms any open-source solution we are aware of, offering a superior combination of storage reduction and efficient data retrieval for large-scale scientific datasets.
Lukas Hedegaard Morsing
Machine Learning Scientist, InCommodities
Cost analyses and benchmark framing
These external writeups are useful for buyers who want to inspect the framing before a technical discussion.
How much can you save on 500 TB of data?
Detailed storage and egress cost analysis for large numeric archives, used to frame Atlas ROI assumptions.
Read analysisThe hidden costs of TDMS in cloud workflows
Analysis of storage and egress overhead in TDMS-heavy pipelines, including where compression changes total cloud cost.
Read analysisInputs and trust guardrails
NOAA GFS
Public operational weather forecast data from NOAA/NCEP, refreshed four times per day and suitable for reproducible benchmark references.
Open sourceARCO ERA5 Zarr
Public ERA5 reanalysis store used to evaluate chunked access behavior and compressed archive workflows.
Open sourceDAS-Month-02.2023
Large sensor-stream dataset accessed through Globus for evaluating storage reduction and retrieval behavior on sensing workloads.
Open sourceEstimate your savings
before rollout.
Use conservative benchmark-driven assumptions to estimate annual impact.
Total annual savings
$147,540
Storage plus egress combined
Monthly storage savings
$12,247
Monthly egress savings
$48
Annual storage savings
$146,964
Data after compression
491.5 TB
Assumptions use the format-specific reduction rates shown above and are intended as planning estimates. Actual savings depend on file structure, retention policy, storage tier, and query frequency.
Up and running
in minutes.
Byte2Bit™ Atlas fits existing pipelines without infrastructure migration.
01
Install the library
Single command install with support for Python workflows that use NumPy, Xarray, and Pandas.
pip install byte2bit-atlas02
Transform and verify
Point Byte2Bit™ Atlas at GRIB2, NetCDF, Zarr, or HDF5 and verify lossless integrity in the same flow.
import byte2bitZarr as b2b
b2b.transform("input.grib2", "output.byte2bit.b2b")
b2b.verify("input.grib2", "output.byte2bit.b2b")03
Query only required chunks
Read by chunk index to avoid full-file decompression and speed up analytics pipelines.
b2b.forcastQuery(short-name 2t \
--type-of-level heightAboveGround \
--start 2026-03-08T00:00:00Z \
--end 2026-03-09T00:00:00Z \
--threshold 6h \
--lat 52.52 \
--lon 13.40 \
--with-values
)Compatible with
Common questions,
straight answers.
Everything engineers usually ask before production rollout.
Yes. Teams can start with a representative sample, then roll out Atlas to new datasets or selected high-cost partitions before broader adoption.
What changes when
Byte2Bit™ Atlas is in your pipeline.
Compare a raw data path to a chunked, indexed Byte2Bit™ Atlas workflow for storage and analytics.
Sensor source
Raw stream
Raw storage
2.50 GB per file
Cloud storage
Full egress costs
Analytics
High latency
Storage per file
2.50 GB
Egress cost
Baseline
Decompression
Full file
Analytics
Slow
Ready to compress
data at scale?
Book a short discovery call and map Byte2Bit™ Atlas into your existing data flow.