Data catalogs provide an abstraction that allows you to externally define, and optionally share, descriptions of datasets, called catalog entries. A catalog entry for a dataset includes information like:

  • The name of the Intake driver that can load the data

  • Arguments to the __init__() method of the driver

  • Metadata provided by the catalog author (such as field descriptions and types, or data provenance)

In addition, Intake allows the arguments to data sources to be templated, with the variables explicitly expressed as “user parameters”. The given arguments are rendered using jinja2, the values of named user parameters, and any overrides. The parameters are also offer validation of the allowed types and values, for both the template values and the final arguments passed to the data source. The parameters are named and described, to indicate to the user what they are for. This kind of structure can be used to, for example, choose between two parts of a given data source, like “latest” and “stable”, see the entry1_part entry in the example below.

The user of the catalog can always override any template or argument value at the time that they access a give source.

The Catalog class

In Intake, a Catalog instance is an object with one or more named entries. The entries might be read from a static file (e.g., YAML, see the next section), from an Intake server or from any other data service that has a driver. Drivers which create catalogs are ordinary DataSource classes, except that they have the container type “catalog”, and do not return data products via the read() method.

For example, you might choose to instantiate the base class and fill in some entries explicitly in your code

from intake.catalog import Catalog
from intake.catalog.local import LocalCatalogEntry
mycat = Catalog.from_dict({
    'source1': LocalCatalogEntry(name, description, driver, args=...),

Alternatively, subclasses of Catalog can define how entries are created from whichever file format or service they interact with, examples including RemoteCatalog and SQLCatalog. These generate entries based on their respective targets; some provide advanced search capabilities executed on the server.

YAML Format

Intake catalogs can most simply be described with YAML files. This is very common in the tutorials and this documentation, because it simple to understand, but demonstrate the many features of Intake. Note that YAML files are also the easiest way to share a catalog, simply by copying to a publicly-available location such as a cloud storage bucket. Here is an example:

  version: 1
      type: str
      description: default file name for child entries
      default: example_file_name
    description: test
    driver: random
    args: {}

    description: entry1 full
      foo: 'bar'
      bar: [1, 2, 3]
    driver: csv
    args: # passed to the open() method
      urlpath: '{{ CATALOG_DIR }}/entry1_*.csv'

    description: entry1 part
    parameters: # User parameters
        description: section of the data
        type: str
        default: "stable"
        allowed: ["latest", "stable"]
    driver: csv
      urlpath: '{{ CATALOG_DIR }}/entry1_{{ part }}.csv'

    description: entry2
    driver: csv
      # file_name parameter will be inherited from file-level parameters, so will
      # default to "example_file_name"
      urlpath: '{{ CATALOG_DIR }}/entry2/{{ file_name }}.csv`


Arbitrary extra descriptive information can go into the metadata section. Some fields will be claimed for internal use and some fields may be restricted to local reading; but for now the only field that is expected is version, which will be updated when a breaking change is made to the file format. Any catalog will have .metadata and .version attributes available.

Note that each source also has its own metadata.

The metadata section an also contain parameters which will be inherited by the sources in the file (note that these sources can augment these parameters, or override them with their own parameters).

Extra drivers

The driver: entry of a data source specification can be a driver name, as has been shown in the examples so far. It can also be an absolute class path to use for the data source, in which case there will be no ambiguity about how to load the data. That is the the preferred way to be explicit, when the driver name alone is not enough (see Driver Selection, below).

    - module: intake.catalog.tests.example1_source

However, you do not, in general, need to do this, since the driver: field of each source can also explicitly refer to the plugin class.


The majority of a catalog file is composed of data sources, which are named data sets that can be loaded for the user. Catalog authors describe the contents of data set, how to load it, and optionally offer some customization of the returned data. Each data source has several attributes:

  • name: The canonical name of the source. Best practice is to compose source names from valid Python identifiers. This allows Intake to support things like tab completion of data source names on catalog objects. For example, monthly_downloads is a good source name.

  • description: Human readable description of the source. To help catalog browsing tools, the description should be Markdown.

  • driver: Name of the Intake Driver to use with this source. Must either already be installed in the current Python environment (i.e. with conda or pip) or loaded in the plugin section of the file. Can be a simple driver name like “csv” or the full path to the implementation class like “package.module.Class”.

  • args: Keyword arguments to the init method of the driver. Arguments may use template expansion.

  • metadata: Any metadata keys that should be attached to the data source when opened. These will be supplemented by additional metadata provided by the driver. Catalog authors can use whatever key names they would like, with the exception that keys starting with a leading underscore are reserved for future internal use by Intake.

  • direct_access: Control whether the data is directly accessed by the client, or proxied through a catalog server. See Server Catalogs for more details.

  • parameters: A dictionary of data source parameters. See below for more details.

Caching Source Files Locally

This method of defining the cache with a dedicated block is deprecated, see the Remote Access section, below

To enable caching on the first read of remote data source files, add the cache section with the following attributes:

  • argkey: The args section key which contains the URL(s) of the data to be cached.

  • type: One of the keys in the cache registry [intake.source.cache.registry], referring to an implementation of caching behaviour. The default is “file” for the caching of one or more files.


  description: cache a csv file from the local filesystem
  driver: csv
    - argkey: urlpath
      type: file
    urlpath: '{{ CATALOG_DIR }}/cache_data/states.csv'

The cache_dir defaults to ~/.intake/cache, and can be specified in the intake configuration file or INTAKE_CACHE_DIR environment variable, or at runtime using the "cache_dir" key of the configuration. The special value "catdir" implies that cached files will appear in the same directory as the catalog file in which the data source is defined, within a directory named “intake_cache”. These will not appear in the cache usage reported by the CLI.

Optionally, the cache section can have a regex attribute, that modifies the path of the cache on the disk. By default, the cache path is made by concatenating cache_dir, dataset name, hash of the url, and the url itself (without the protocol). regex attribute allows to remove part of the url (the matching part).

Caching can be disabled at runtime for all sources regardless of the catalog specification:

from intake.config import conf

conf['cache_disabled'] = True

By default, progress bars are shown during downloads if the package tqdm is available, but this can be disabled (e.g., for consoles that don’t support complex text) with

conf[‘cache_download_progress’] = False

or, equivalently, the environment parameter INTAKE_CACHE_PROGRESS.

The “types” of caching are that supported are listed in intake.source.cache.registry, see the docstrings of each for specific parameters that should appear in the cache block.

It is possible to work with compressed source files by setting type: compression in the cache specification. By default the compression type is inferred from the file extension, otherwise it can be set by assigning the decomp variable to any of the options listed in intake.source.decompress.decomp. This will extract all the file(s) in the compressed file referenced by urlpath and store them in the cache directory.

In cases where miscellaneous files are present in the compressed file, a regex_filter parameter can be used. Only the extracted filenames that match the pattern will be loaded. The cache path is appended to the filename so it is necessary to include a wildcard to the beginning of the pattern.


  driver: csv
    urlpath: 'compressed_file.tar.gz'
    - type: compressed
      decomp: tgz
      argkey: urlpath
      regex_filter: '.*data.csv'


Intake catalog files support Jinja2 templating for driver arguments. Any occurrence of a substring like {{field}} will be replaced by the value of the user parameters with that same name, or the value explicitly provided by the user. For how to specify these user parameters, see the next section.

Some additional values are available for templating. The following is always available: CATALOG_DIR, the full path to the directory containing the YAML catalog file. This is especially useful for constructing paths relative to the catalog directory to locate data files and custom drivers. For example, the search for CSV files for the two “entry1” blocks, above, will happen in the same directory as where the catalog file was found.

The following functions may be available. Since these execute code, the user of a catalog may decide whether they trust those functions or not.

  • env("USER"): look in the set environment variables for the named variable

  • client_env("USER"): exactly the same, except that when using a client-server topology, the value will come from the environment of the client.

  • shell("get_login thisuser -t"): execute the command, and use the output as the value. The output will be trimmed of any trailing whitespace.

  • client_shell("get_login thisuser -t"): exactly the same, except that when using a client-server topology, the value will come from the system of the client.

The reason for the “client” versions of the functions is to prevent leakage of potentially sensitive information between client and server by controlling where lookups happen. When working without a server, only the ones without “client” are used.

An example:

    description: This source needs your username
      url: "http://server:port/user/{{env(USER)}}"

Here, if the user is named “blogs”, the url argument will resolve to "http://server:port/user/blogs"; if the environment variable is not defined, it will resolve to "http://server:port/user/"

Parameter Definition

Source parameters

A source definition can contain a “parameters” block. Expressed in YAML, a parameter may look as follows:

    description: name to use  # human-readable text for what this parameter means
    type: str  # one of bool, str, int, float, list[str | int | float], datetime, mlist
    default: normal  # optional, value to assume if user does not override
    allowed: ["normal", "strange"]  # optional, list of values that are OK, for validation
    min: "n"  # optional, minimum allowed, for validation
    max: "t"  # optional, maximum allowed, for validation

A parameter, not to be confused with an argument, can have one of two uses:

  • to provide values for variables to be used in templating the arguments. If the pattern “{{name}}” exists in any of the source arguments, it will be replaced by the value of the parameter. If the user provides a value (e.g., source = cat.entry(name='something")), that will be used, otherwise the default value. If there is no user input or default, the empty value appropriate for type is used. The default field allows for the same function expansion as listed for arguments, above.

  • If an argument with the same name as the parameter exists, its value, after any templating, will be coerced to the given type of the parameter and validated against the allowed/max/min. It is therefore possible to use the string templating system (e.g., to get a value from the environment), but pass the final value as, for example, an integer. It makes no sense to provide a default for this case (the argument already has a value), but providing a default will not raise an exception.

  • the “mlist” type is special: it means that the input must be a list, whose values are chosen from the allowed list. This is the only type where the parameter value is not the same type as the allowed list’s values, e.g., if a list of str is set for allowed, a list of str must also be the final value.

Note: the datetime type accepts multiple values: Python datetime, ISO8601 string, Unix timestamp int, “now” and “today”.

Catalog parameters

You can also define user parameters at the catalog level. This applies the parameter to all entries within that catalog, without having to define it for each and every entry. Furthermore, catalogs dested within the catalog will also inherit the parameter(s).

For example, with the following spec

  version: 1
      type: str
      description: description
      default: test_bucket
    driver: parquet
    description: description
      urlpath: s3://{{bucket}}/file.parquet
    driver: yaml_file
    path: "{{CATALOG_DIR}}/other.yaml"

If cat is the corresponsing catalog instance, the URL of source cat.param_source will evaluate to “s3://test_bucket/file.parquet” by default, but the parameter can be overridden with cat.param_source(bucket="other_bucket"). Also, any entries of subcat, another catalog referenced from here, would also have the “bucket”-named parameter attached to all sources. Of course, those sources do no need to make use of the parameter.

To change the default, we can gerenate a new instance

cat2 = cat(bucket="production")  # sets default value of "bucket" for cat2
subcat = cat.subcat(bucket="production")  # sets default only for the nested catalog

Of course, in these situations you can still override the value of the parameter for any source, or pass explicit values for the arguments of the source, as normal.

For cases where the catalog is not defined in a YAML spec, the argument user_parameters to the constructor takes the same form as parameters above: a dict of user parameters, either as UserParameter instances or as a dictionary spec for each one.

Templating parameters

Template functions can also be used in parameters (see Templating, above), but you can use the available functions directly without the extra {{…}}.

For example, this catalog entry uses the env("HOME") functionality as described to set a default based on the user’s home directory.

    description: "This entry leads to an example csv file in the user's home directory by default, but the user can pass root="somepath" to override that."
    driver: csv
      path: "{{root}}/example.csv"
        description: "root path"
        type: str
        default: "env(HOME)"

Driver Selection

In some cases, it may be possible that multiple backends are capable of loading from the same data format or service. Sometimes, this may mean two drivers with unique names, or a single driver with a parameter to choose between the different backends.

However, it is possible that multiple drivers for reading a particular type of data also share the same driver name: for example, both the intake-iris and the intake-xarray packages contain drivers with the name "netcdf", which are capable of reading the same files, but with different backends. Here we will describe the various possibilities of coping with this situation. Intake’s plugin system makes it easy to encode such choices.

It may be acceptable to use any driver which claims to handle that data type, or to give the option of which driver to use to the user, or it may be necessary to specify which precise driver(s) are appropriate for that particular data. Intake allows all of these possibilities, even if the backend drivers require extra arguments.

Specifying a single driver explicitly, rather than using a generic name, would look like this:

    description: test
    driver: package.module.PluginClass
    args: {}

It is also possible to describe a list of drivers with the same syntax. The first one found will be the one used. Note that the class imports will only happen at data source instantiation, i.e., when the entry is selected from the catalog.

    description: test
      - package.module.PluginClass
      - another_package.PluginClass2
    args: {}

These alternative plugins can also be given data-source specific names, allowing the user to choose at load time with driver= as a parameter. Additional arguments may also be required for each option (which, as usual, may include user parameters); however, the same global arguments will be passed to all of the drivers listed.

    description: test
        class: package.module.PluginClass
          specific_thing: 9
        class: another_package.PluginClass2
    args: {}

Remote Access

(see also Remote Data for the implementation details)

Many drivers support reading directly from remote data sources such as HTTP, S3 or GCS. In these cases, the path to read from is usually given with a protocol prefix such as gcs://. Additional dependencies will typically be required (requests, s3fs, gcsfs, etc.), any data package should specify these. Further parameters may be necessary for communicating with the storage backend and, by convention, the driver should take a parameter storage_options containing arguments to pass to the backend. Some remote backends may also make use of environment variables or config files to determine thier default behaviour.

The special template variable “CATALOG_DIR” may be used to construct relative URLs in the arguments to a source. In such cases, if the filesystem used to load that catalog contained arguments, then the storage_options of that file system will be extracted and passed to the source. Therefore, all sources which can accept general URLs (beyond just local paths) must make sure to accept this argument.

As an example of using storage_options, the following two sources would allow for reading CSV data from S3 and GCS backends without authentication (anonymous access), respectively

    driver: csv
    description: "Publicly accessible CSV data on S3; requires s3fs"
      urlpath: s3://bucket/path/*.csv
        anon: true
    driver: csv
    description: "Publicly accessible CSV data on GCS; requires gcsfs"
      urlpath: gcs://bucket/path/*.csv
        token: "anon"

Using S3 Profiles

An AWS profile may be specified as an argument under storage_options via the following format:

  urlpath: s3://bucket/path/*.csv
    profile: aws-profile-name


URLs interpreted by fsspec offer automatic caching. For example, to enable file-based caching for the first source above, you can do:

    driver: csv
    description: "Publicly accessible CSV data on S3; requires s3fs"
      urlpath: simplecache::s3://bucket/path/*.csv
          anon: true

Here we have added the “simplecache” to the URL (this caching backend does not store any metadata about the cached file) and specified that the “anon” parameter is meant as an argument to s3, not to the caching mechanism. As each file in s3 is accessed, it will first be downloaded and then the local version used instead.

You can tailor how the caching works. In particular the location of the local storage can be set with the cache_storage parameter (under the “simplecache” group of storage_options, of course) - otherwise they are stored in a temporary location only for the duration of the current python session. The cache location is particularly useful in conjunction with an environment variable, or relative to “{{CATALOG_DIR}}”, wherever the catalog was loaded from.

Please see the fsspec documentation for the full set of cache types and their various options.

Local Catalogs

A Catalog can be loaded from a YAML file on the local filesystem by creating a Catalog object:

from intake import open_catalog
cat = open_catalog('catalog.yaml')

Then sources can be listed:


and data sources are loaded via their name:

data = cat.entry_part1

and you can optionally configure new instances of the source to define user parameters or override arguments by calling either of:

data = cat.entry_part1.configure_new(part='1')
data = cat.entry_part1(part='1')  # this is a convenience shorthand

Intake also supports loading a catalog from all of the files ending in .yml and .yaml in a directory, or by using an explicit glob-string. Note that the URL provided may refer to a remote storage systems by passing a protocol specifier such as s3://, gcs://.:

cat = open_catalog('/research/my_project/catalog.d/')

Intake Catalog objects will automatically reload changes or new additions to catalog files and directories on disk. These changes will not affect already-opened data sources.

Catalog Nesting

A catalog is just another type of data source for Intake. For example, you can print a YAML specification corresponding to a catalog as follows:

cat = intake.open_catalog('cat.yaml')

results in:

      path: cat.yaml
    description: ''
    driver: intake.catalog.local.YAMLFileCatalog
    metadata: {}

The point here, is that this can be included in another catalog. (It would, of course, be better to include a description and the full path of the catalog file here.) If the entry above were saved to another file, “root.yaml”, and the original catalog contained an entry, data, you could access it as:

root = intake.open_catalog('root.yaml')

It is, therefore, possible to build up a hierarchy of catalogs referencing each other. These can, of course, include remote URLs and indeed catalog sources other than simple files (all the tables on a SQL server, for instance). Plus, since the argument and parameter system also applies to entries such as the example above, it would be possible to give the user a runtime choice of multiple catalogs to pick between, or have this decision depend on an environment variable.

Server Catalogs

Intake also includes a server which can share an Intake catalog over HTTP (or HTTPS with the help of a TLS-enabled reverse proxy). From the user perspective, remote catalogs function identically to local catalogs:

cat = open_catalog('intake://catalog1:5000')

The difference is that operations on the catalog translate to requests sent to the catalog server. Catalog servers provide access to data sources in one of two modes:

  • Direct access: In this mode, the catalog server tells the client how to load the data, but the client uses its local drivers to make the connection. This requires the client has the required driver already installed and has direct access to the files or data servers that the driver will connect to.

  • Proxied access: In this mode, the catalog server uses its local drivers to open the data source and stream the data over the network to the client. The client does not need any special drivers to read the data, and can read data from files and data servers that it cannot access, as long as the catalog server has the required access.

Whether a particular catalog entry supports direct or proxied access is determined by the direct_access option:

  • forbid (default): Force all clients to proxy data through the catalog server

  • allow: If the client has the required driver, access the source directly, otherwise proxy the data through the catalog server.

  • force: Force all clients to access the data directly. If they do not have the required driver, an exception will be raised.

Note that when the client is loading a data source via direct access, the catalog server will need to send the driver arguments to the client. Do not include sensitive credentials in a data source that allows direct access.

Client Authorization Plugins

Intake servers can check if clients are authorized to access the catalog as a whole, or individual catalog entries. Typically a matched pair of server-side plugin (called an “auth plugin”) and a client-side plugin (called a “client auth plugin) need to be enabled for authorization checks to work. This feature is still in early development, but see module intake.auth.secret for a demonstration pair of server and client classes implementation auth via a shared secret.