Zarr Storage Explained: Optimizing Geospatial Workflows
- Anvita Shrivastava
- Oct 6
- 4 min read
Updated: Oct 7
When it comes to handling geospatial data, storage efficiency and quick access to large datasets are indispensable. As we consider datasets with higher dimensions, size, and complexity, the traditional storage formats can act as a bottleneck, hindering analysis and visualization. This is where Zarr storage comes into play, a modern, chunked, and compressed storage format capable of handling huge multidimensional arrays quickly with its unique methodology.
What is Zarr Storage?
Zarr is an open-source format designed to store N-dimensional arrays in a manner that enables chunking, compression, and parallel processing. In contrast to standard file formats that require reading the entire dataset into memory, Zarr enables the selective reading of slices of data. Geospatial analysts can now read, process, and visualize only the data that is needed, significantly improving performance for procedures reliant on satellite imagery, climate models, or LiDAR point clouds.
The key features of Zarr are as follows:
Chunked storage: Zarr takes a large array and stores it in independent, smaller arrays to facilitate I/O speed.
Compression: Zarr offers support for several compression algorithms, including Blosc and Zstd, to save money on storage.
Cloud-friendly: Zarr was designed to work fluidly with object storage systems like AWS S3, Azure Blob, and Google Cloud Storage.
Parallel access: Zarr did create single-threaded processes that spot access, read the array, and then act as a bottleneck to perform computation that returned results to the main process. Instead, Zarr could decouple the threads, utilize parallel access to get chunks of the array simultaneously, thus decreasing the time to completion and speeding up computation.
Why MrSID and Zarr Work Well in Geospatial Workflows
Geospatial workflows increasingly involve multi-terabyte datasets. Depending on your specific use case, these datasets may, understandably, present challenges for many traditional datasets. MrSID performs particularly well where many traditional formats struggle. MrSID accomplishes this by achieving highly efficient compression of enormously high-resolution images, while still allowing for fast random access to large raster images. In other words, MrSID is highly optimally suited to storing aerial images, satellite imagery, or any geospatial image-based products without losing any detail or quality. Analysts can zoom, pan, and extract regions of interest quickly without downloading and rendering whole datasets into memory. This can be especially useful for desktop GIS users or anyone who needs to conduct processing locally.
In addition to being highly efficient for remote image workflows, Zarr is a great counterpart to MrSID, particularly for workflows that require cloud-based processing. Zarr is a compressed, chunked storage format that allows analysts to:
Process big data workflows with Zarr's chunking mechanism, enabling calculations only on the parts of the entire dataset of interest, without needing to download everything.
Run an efficient parallelized workflow of computing and perform large-scale calculations using cloud computing, perhaps utilizing Dask or Xarray.
Integrate into modern state-of-the-art data-science workflows using Python or other languages that allow for scalable analytics.
Altogether, using MrSID for storage or local access for fast and efficient extraction of image products or image-based workflows and using Zarr for cloud-native and asynchronous parallel workflows allows organizations to develop a hybrid geospatial workflow that is fast and scales with whatever platform the users prefer. Essentially, both workflows can exist simultaneously and allow for the efficiencies of a desktop GIS and cloud-based analyses.
Why MrSID is Superior to Traditional Storage Solutions
In the pre-Zarr era, types of geospatial edges meant MrSID became a widely utilized geospatial imagery file format due to its high compression ratio and fast random access capabilities. Due to the way MrSID processes and stores its raster imagery, it is a great choice for the storage of large raster images. Unlike uncompressed types, MrSID enables massive datasets to be stored with respectable qualities in clear images that allow for smooth zooming and panning capabilities in GIS applications.
While MrSID performs highly as an image and geospatial data storage format, it is primarily a proprietary format. Proprietary formats typically have less flexibility in cloud workflows and tend to limit the ability to work or integrate with open-source processing pipelines. This is where Zarr is likely to perform better than a MrSID: Zarr provides open standards, cloud-native compatibility, and is easily used with modern data science tools. Therefore, Zarr represents a new opportunity focused on scalable geospatial analytics, taking advantage of its open structure and easier access for processing.
Zarr storage is creating dramatic improvements to geospatial workflows by simply enabling scalable, flexible, and cloud-friendly access to large multidimensional datasets. While formats like MrSID still exist, per-familiarization with them as high-compression raster storage formats, Zarr's open architecture, along with its ability for parallel processing within the platform and full adoption as relatable with ease of integration with modern data-science processing tools, organization lends itself as an obvious choice for newer, next-generation geospatial analytics.
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