data-migration

name	data-migration
description	Migrate data between formats with a focus on converting HDF5 and NetCDF datasets to Zarr. Covers xarray-based conversion, direct zarr.copy operations, VirtualiZarr for reference-based virtual Zarr stores, kerchunk for legacy workflows, validation strategies, and batch migration pipelines.
metadata	{"references":["references/patterns.md","references/examples.md","references/common-issues.md"]}

Data Migration to Zarr

Migrate existing datasets from HDF5, NetCDF, and other formats to Zarr. This skill covers the full spectrum of migration approaches: xarray-based conversion for labeled scientific data, direct copy operations for group-level migration, VirtualiZarr for creating virtual Zarr stores without copying data, and kerchunk for legacy reference-based workflows. You will also learn validation strategies to ensure data integrity after migration.

Zarr Documentation: https://zarr.readthedocs.io/

xarray Documentation: https://docs.xarray.dev/

VirtualiZarr Documentation: https://virtualizarr.readthedocs.io/

kerchunk Documentation: https://fsspec.github.io/kerchunk/

h5py Documentation: https://docs.h5py.org/

Quick Reference Card

Installation & Setup

# Core tools
pixi add zarr xarray h5py netcdf4 numpy

# Using pip
pip install zarr xarray h5py netCDF4

# VirtualiZarr (virtual references)
pip install virtualizarr

# kerchunk (legacy reference approach)
pip install kerchunk

# For cloud sources
pip install fsspec s3fs gcsfs

Essential Operations

import xarray as xr

# ── NetCDF to Zarr (simplest path) ──
ds = xr.open_dataset("input.nc")
ds.to_zarr("output.zarr")

# ── HDF5 to Zarr via xarray ──
ds = xr.open_dataset("input.h5", engine="h5netcdf")
ds.to_zarr("output.zarr")

# ── Multiple NetCDFs to single Zarr ──
ds = xr.open_mfdataset("data_*.nc", chunks={"time": 30})
ds.to_zarr("combined.zarr", encoding={...})

# ── VirtualiZarr (no data copy) ──
from virtualizarr import open_virtual_dataset
vds = open_virtual_dataset("input.nc")
vds.virtualize.to_zarr("virtual_output.zarr")

# ── Validate migration ──
ds_src = xr.open_dataset("input.nc")
ds_dst = xr.open_zarr("output.zarr")
xr.testing.assert_allclose(ds_src, ds_dst)

Quick Decision Tree

Migrating data to Zarr?
├── Single file, simple structure?
│   └── xr.open_dataset() → ds.to_zarr()
├── Many files, same structure?
│   └── xr.open_mfdataset() → ds.to_zarr()
├── Don't want to copy data?
│   ├── Modern approach → VirtualiZarr
│   └── Legacy approach → kerchunk
├── Complex HDF5 groups/hierarchies?
│   └── h5py + zarr.copy_store or manual group iteration
├── Need to rechunk during migration?
│   └── xarray with chunks= and encoding=
└── Need validation?
    └── xr.testing.assert_allclose() + shape/dtype checks

When to Use This Skill

Use this skill when:

• Converting HDF5 files to Zarr for cloud storage
• Migrating NetCDF archives to Zarr for faster access
• Creating virtual Zarr references for existing files (no data copy)
• Combining many small files into a single consolidated Zarr store
• Rechunking data during format conversion
• Validating that migrated data matches the original source
• Building automated migration pipelines for large archives

Core Concepts

1. Migration Strategy Overview

Strategy	Data Copy	Speed	Storage Cost	Best For
xarray conversion	Full copy	Medium	2× during migration	Simple files, rechunking needed
zarr.copy_store	Full copy	Fast	2× during migration	Preserving exact HDF5 group structure
VirtualiZarr	No copy	Very fast	Minimal (refs only)	Large archives, cloud-hosted sources
kerchunk	No copy	Fast	Minimal (JSON refs)	Legacy workflows, existing JSON refs

Decision factors:

• Need rechunking? → xarray conversion (can specify new chunks)
• Need to keep source files? → VirtualiZarr (references, no copy)
• Migrating to cloud? → Full copy (eliminates dependency on source)
• One-time vs ongoing? → Full copy for one-time; virtual for growing archives

2. HDF5 to Zarr

Via xarray (recommended for scientific data):

import xarray as xr

# ── Single HDF5 file ──
ds = xr.open_dataset("simulation_output.h5", engine="h5netcdf")
print(ds)

# Write to Zarr with optimized chunks
encoding = {var: {"chunks": {"time": 30, "x": 100, "y": 100}} for var in ds.data_vars}
ds.to_zarr("simulation_output.zarr", encoding=encoding)

# ── HDF5 with groups ──
# xarray reads one group at a time
ds_group1 = xr.open_dataset("data.h5", group="experiment/run_001")
ds_group1.to_zarr("data.zarr", group="experiment/run_001")

Via h5py + zarr (for complex hierarchies):

import h5py
import zarr
import numpy as np

# ── Copy entire HDF5 structure to Zarr ──
with h5py.File("complex_data.h5", "r") as h5f:
    root = zarr.open_group("complex_data.zarr", mode="w")

    def copy_group(h5_group, zarr_group):
        # Copy attributes
        zarr_group.attrs.update(dict(h5_group.attrs))

        for key in h5_group:
            item = h5_group[key]
            if isinstance(item, h5py.Group):
                zg = zarr_group.create_group(key)
                copy_group(item, zg)
            elif isinstance(item, h5py.Dataset):
                chunks = item.chunks or item.shape
                zarr_group.create_array(
                    key,
                    data=item[:],
                    chunks=chunks,
                    dtype=item.dtype,
                )
                zarr_group[key].attrs.update(dict(item.attrs))

    copy_group(h5f, root)

3. NetCDF to Zarr

import xarray as xr

# ── Single NetCDF file ──
ds = xr.open_dataset("climate_model_output.nc", chunks={"time": 30})
encoding = {
    "temperature": {"chunks": {"time": 30, "lat": 90, "lon": 180}, "dtype": "float32"},
    "precipitation": {"chunks": {"time": 30, "lat": 90, "lon": 180}, "dtype": "float32"},
}
ds.to_zarr("climate_output.zarr", encoding=encoding, consolidated=True)

# ── Multiple NetCDF files → single Zarr store ──
ds = xr.open_mfdataset(
    sorted(Path("netcdf_archive/").glob("*.nc")),
    combine="nested",
    concat_dim="time",
    chunks={"time": 30},
)
ds.to_zarr("combined_archive.zarr", encoding=encoding, consolidated=True)

# ── Preserving CF metadata ──
# xarray automatically preserves CF attributes (units, long_name, etc.)
# Verify after conversion:
ds_zarr = xr.open_zarr("climate_output.zarr")
for var in ds_zarr.data_vars:
    print(f"{var}: units={ds_zarr[var].attrs.get(, )}")

4. VirtualiZarr

VirtualiZarr creates a Zarr store that contains only references (byte offsets) pointing to data in the original files. No data is copied — reads go directly to the source files.

from virtualizarr import open_virtual_dataset
import xarray as xr

# ── Create virtual references for a single file ──
vds = open_virtual_dataset("large_data.nc")
print(vds)

# ── Combine multiple files into a virtual dataset ──
vds_list = []
for path in sorted(Path("archive/").glob("*.nc")):
    vds = open_virtual_dataset(str(path))
    vds_list.append(vds)

combined = xr.concat(vds_list, dim="time")

# ── Write virtual Zarr store (only writes references, not data) ──
combined.virtualize.to_zarr("virtual_archive.zarr")

# ── Read the virtual store like a normal Zarr store ──
ds = xr.open_zarr("virtual_archive.zarr")
print(f"Shape: {dict(ds.dims)}")
# Data reads go to the original .nc files

# ── Write virtual references to Icechunk (versioned) ──
from icechunk import IcechunkStore, StorageConfig
storage = StorageConfig.s3_from_env(bucket="my-bucket", prefix="virtual.zarr")
store = IcechunkStore.open_or_create(storage=storage, mode="w")
combined.virtualize.to_zarr(store)
store.commit("Virtual references from NetCDF archive")

5. Kerchunk (Legacy)

Kerchunk scans existing files and produces JSON reference files that allow Zarr to read the original data without copying.

import json
from kerchunk.hdf import SingleHdf5ToZarr
from kerchunk.combine import MultiZarrToZarr
import fsspec

# ── Scan a single HDF5/NetCDF file ──
url = "s3://noaa-data/file_001.nc"
with fsspec.open(url, mode="rb", anon=True) as f:
    refs = SingleHdf5ToZarr(f, url).translate()

# Save references to JSON
with open("refs_001.json", "w") as f:
    json.dump(refs, f)

# ── Combine multiple reference files ──
ref_files = ["refs_001.json", "refs_002.json", "refs_003.json"]
refs_list = []
for rf in ref_files:
    with open(rf) as f:
        refs_list.append(json.load(f))

combined_refs = MultiZarrToZarr(
    refs_list,
    concat_dims=["time"],
).translate()

with open("combined_refs.json", "w") as f:
    json.dump(combined_refs, f)

# ── Open the virtual dataset ──
import xarray as xr
mapper = fsspec.get_mapper("reference://", fo="combined_refs.json")
ds = xr.open_zarr(mapper)

6. Validation

Always validate after migration to ensure data integrity.

import xarray as xr
import numpy as np

def validate_migration(source_path, zarr_path, engine="netcdf4"):
    """Compare source dataset with migrated Zarr store."""
    ds_src = xr.open_dataset(source_path, engine=engine)
    ds_dst = xr.open_zarr(zarr_path)

    errors = []

    # Check dimensions
    if dict(ds_src.dims) != dict(ds_dst.dims):
        errors.append(f"Dimension mismatch: {dict(ds_src.dims)} vs {dict(ds_dst.dims)}")

    # Check variables
    src_vars = set(ds_src.data_vars)
    dst_vars = set(ds_dst.data_vars)
    if src_vars != dst_vars:
        errors.append(f"Variable mismatch: missing={src_vars - dst_vars}, extra={dst_vars - src_vars}")

    # Check values
    for var in src_vars & dst_vars:
        src_data = ds_src[var].values
        dst_data = ds_dst[var].values
        if src_data.dtype != dst_data.dtype:
            # Allow float64 → float32 if encoding was applied
            src_data = src_data.astype(dst_data.dtype)
        if not np.allclose(src_data, dst_data, equal_nan=True, rtol=1e-5):
            max_diff = np.nanmax(np.abs(src_data - dst_data))
            errors.append(f"{var}: max difference = {max_diff}")

    # Check attributes
    for var in src_vars & dst_vars:
         attr  ds_src[var].attrs:
             attr   ds_dst[var].attrs:
                errors.append()

     errors:
        ()
         e  errors:
            ()
    :
        ()

     (errors) == 

7. Batch Migration Pipelines

import xarray as xr
from pathlib import Path
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def batch_migrate(
    source_dir: str,
    zarr_path: str,
    pattern: str = "*.nc",
    concat_dim: str = "time",
    encoding: dict | None = None,
):
    """Migrate a directory of NetCDF files to a single Zarr store."""
    source_files = sorted(Path(source_dir).glob(pattern))
    if not source_files:
        raise FileNotFoundError(f"No files matching {pattern} in {source_dir}")

    logger.info(f"Found {len(source_files)} files to migrate")

    # Open all files lazily
    ds = xr.open_mfdataset(
        source_files,
        combine="nested",
        concat_dim=concat_dim,
        chunks={concat_dim: "auto"},
    )
    logger.info(f"Combined dataset: {dict(ds.dims)}")

    # Write to Zarr
    ds.to_zarr(zarr_path, mode="w", encoding=encoding, consolidated=True)
    logger.info(f"Wrote to {zarr_path}")

    # Validate
    ds_zarr = xr.open_zarr(zarr_path)
    assert dict(ds.dims) == dict(ds_zarr.dims), "Dimension mismatch!"
    logger.info()

     ds_zarr