Understanding Zarr: Cloud-Native Geospatial Data Storage
- Anvita Shrivastava
- 3 days ago
- 3 min read
Updated: 2 days ago
In the present data-centric era, we must create viable approaches to analyze and derive knowledge from large geospatial datasets. Standard data formats are often unsuitable for terabytes of satellite imagery or LiDAR because they lack characteristics including scalability, cloud-readiness, and efficiency. Zarr is a cloud-native format that is very well suited to fit this paradigm shift. It is also designed for distributed computing and implemented for modern cloud architectures. Zarr will change how geospatial professionals store, access, and analyze large datasets.
What is Zarr?
Zarr is an open-source standard for the storage of N-dimensional arrays, which are chunked and compressed, and it avoids the monolithic file formats of the past. Zarr allows for data to be chunked into smaller pieces that can be compressed and stored independently of other chunks. This enables faster access time as it allows for the efficient handling of the data in storage and memory. Zarr has a cloud-native architecture that makes it suitable for cloud environments, where chunks of data can be stored in the object storage solutions offered by Amazon S3, Google Cloud Storage, and Azure Blob storage, and read and written in parallel.
Key Zarr Features
Cloud Native: Built for modern object storage solutions.
Chunked storage: Efficiently handle massive datasets in parallel.
Compression: Lower cost for storage with speed.
Scalable and Flexible: Works with Python libraries such as xarray and Dask for analysis on large computer systems.
MrSID vs. Zarr: What’s different?
It is worthwhile to discuss MrSID, another tried-and-true geospatial storage format, to provide some context before going deeper into Zarr. MrSID is certainly an excellent format for high compression ratios, a great way to store very large raster datasets, and incredibly quick to render data, making it a superior choice for desktop GIS applications. When organizations have strict goals of reducing storage, quality of imagery is still essential, and MrSID is likely the right choice.
Zarr wins in the delivery of cloud native workflows, distributed computing, and engagement with open-source functions over MrSID. Although MrSID has improved compression, Zarr, by its very nature and design, the chunked and parallelizable structure, is better situated for modern cloud varieties and large-scale geospatial analytics.
Zarr in Real Life With Geospatial Applications
Analysis of Satellite Imagery: Allow for storage and analysis of petabytes of Earth observation data without downloading the entire dataset.
Climate Modelling: Store and manage multi-dimensional climate datasets across distributed clusters.
Urban Planning and LiDAR Processing: Permit fast access and processing of high-resolution LiDAR data at city-scale.
How to Get Started with Zarr
Install the Zarr library:
Pip install zarr
Store your data in chunks:
import zarr
import numpy as np
data = np.random.random((10000, 10000))
z = zarr.array(data, chunks=(1000, 1000), compressor=zarr.Blosc())
Integrate with xarray or Dask for analysis:
import xarray as xr
ds = xr.open_zarr('path_to_zarr_dataset')
Zarr is a newer cloud-optimized storage format that is useful for distributed geospatial workflows, but MrSID is the superior choice in terms of compression, rendering speed, and usability in desktop GIS. Companies and organizations that place a premium on storage efficiency, visualization, and ease of learning will benefit more from working in MrSID. Zarr has its advantages with cloud computing and “big data” analytics, but from a GIS and storage optimization perspective, nothing will compete with the performance of MrSID.
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