Files (CSV, JSON, Excel, Feather, Parquet)

This page contains the setup guide and reference information for different files.

Prerequisites

• A file hosted on AWS S3, GCS, HTTPS, an SFTP server, or locally

Features

Feature	Supported?
Full Refresh Sync	Yes
Incremental Sync	No
Replicate Incremental Deletes	No
Replicate Folders (multiple Files)	No
Replicate Glob Patterns (multiple Files)	No
Namespaces	No

Setup guide

Step 1: Set up the source in Daspire

1. Select Files (CSV, JSON, Excel, Feather, Parquet) from the Source list.

2. Enter a Source Name.

3. For Dataset Name, enter the name of the final table to replicate this file into (should include letters, numbers, dashes and underscores only).

4. For File Format, select the format of the file to replicate from the dropdown menu (Warning: some formats may be experimental. Please refer to the table of supported formats below).

Step 2: Select the provider and set provider-specific configurations

For Storage Provider, use the dropdown menu to select the Storage Provider or Location of the file(s) which should be replicated, then configure the provider-specific fields as needed:

HTTPS: Public Web [Default]

• User-Agent (Optional)

  Set this to active if you want to add the User-Agent header to requests (inactive by default).

GCS: Google Cloud Storage

• Service Account JSON (Required for private buckets)

  To access private buckets stored on Google Cloud, this integration requires a service account JSON credentials file with the appropriate permissions. A detailed breakdown of this topic can be found at theGoogle Cloud service accounts page. Please generate the "credentials.json" file and copy its content to this field, ensuring it is in JSON format. If you are accessing publicly available data, this field is not required.

S3: Amazon Web Services

• AWS Access Key ID (Required for private buckets)

• AWS Secret Access Key (Required for private buckets)

  To access private buckets stored on AWS S3, this integration requires valid credentials with the necessary permissions. To access these keys, refer to the AWS IAM documentation. More information on setting permissions in AWS can be found here. If you are accessing publicly available data, these fields are not required.

AzBlob: Azure Blob Storage

• Storage Account (Required)

  This is the globally unique name of the storage account that the desired blob sits within. See the Azure documentation for more details.

  If you are accessing private storage, you must also provide one of the following security credentials with the necessary permissions:

  • SAS Token: Find more information here.
  • Shared Key: Find more information here.

SSH: Secure Shell / SCP: Secure Copy Protocol / SFTP: Secure File Transfer Protocol

• Host (Required)

  Enter the hostname or IP address of the remote server where the file trasfer will take place.

• User (Required)

  Enter the username associated with your account on the remote server.

• Password (Optional)

  If required by the remote server, enter the password associated with your user account. Otherwise, leave this field blank.

• Port (Optional)

  Specify the port number number to use for the connection. The default port is usually 22. However, if your remote server uses a non-standard port, you can enter the appropriate port number here.

Local Filesystem

• Storage

  Note the local storage URL for reading must start with the local mount /local/.

  Please note that if you are replicating data from a locally stored file on Windows OS, you will need to open the .env file in your local Daspire root folder and change the values for:

  • LOCAL_ROOT
  • LOCAL_DOCKER_MOUNT
  • HACK_LOCAL_ROOT_PARENT

  Please set these to an existing absolute path on your machine. Colons in the path need to be replaced with a double forward slash, //. LOCAL_ROOT & LOCAL_DOCKER_MOUNT should be set to the same value, and HACK_LOCAL_ROOT_PARENT should be set to their parent directory.

Step 3: Select the provider and set provider-specific configurations

1. For URL, enter the URL path of the file to be replicated.

When connecting to a file located in Google Drive, please note that you need to utilize the Download URL format: https://drive.google.com/uc?export=download&id=[DRIVE_FILE_ID]. [DRIVE_FILE_ID] should be replaced with the unique string found in the Share URL specific to Google Drive. You can find the Share URL by visiting https://drive.google.com/file/d/[DRIVE_FILE_ID]/view?usp=sharing.

When connecting to a file using Azure Blob Storage, please note that we account for the base URL. Therefore, you should only need to include the path to your specific file (eg container/file.csv).

2. For Reader Options (Optional), you may choose to enter a string in JSON format. Depending on the file format of your source, this will provide additional options and tune the Reader's behavior. Please refer to the next section for a breakdown of the possible inputs. This field may be left blank if you do not wish to configure custom Reader options.

Normally, Daspire tries to infer the data type from the source, but you can use reader_options to force specific data types. If you input {"dtype":"string"}, all columns will be forced to be parsed as strings. If you only want a specific column to be parsed as a string, simply use {"dtype" : {"column name": "string"}}.

3. Click Save & Test.

Reader options

The Reader in charge of loading the file format is currently based on Pandas IO Tools. It is possible to customize how to load the file into a Pandas DataFrame as part of this Source. This is doable in the reader_options that should be in JSON format and depends on the chosen file format. See pandas' documentation, depending on the format:

For example, if the format CSV is selected, then options from the read_csv functions are available.

• It is therefore possible to customize the delimiter (or sep) to in case of tab separated files.
• Header line can be ignored with header=0 and customized with names
• Parse dates for in specified columns

We would therefore provide in the reader_options the following json:

{ "sep" : "\t", "header" : 0, "names": ["column1", "column2"], "parse_dates": ["column2"]}

In case you select JSON format, then options from the read_json reader are available.

For example, you can use the {"orient" : "records"} to change how orientation of data is loaded (if data is [{column -> value}, … , {column -> value}])

If you need to read Excel Binary Workbook, please specify excel_binary format in File Format select.

Supported File / Stream Compression

Compression	Supported?
Gzip	Yes
Zip	No
Bzip2	No
Lzma	No
Xz	No
Snappy	No

Supported Storage Providers

Storage Providers	Supported?
HTTPS	Yes
Google Cloud Storage	Yes
Amazon Web Services S3	Yes
SFTP	Yes
SSH / SCP	Yes
local filesystem	Yes

Supported File Formats

Format	Supported?
CSV	Yes
JSON/JSONL	Yes
HTML	No
XML	No
Excel	Yes
Excel Binary Workbook	Yes
Feather	Yes
Parquet	Yes
Pickle	No
YAML	Yes

Data type mapping

The Stripe API uses the same JSON Schema types that Daspire uses internally (string, date-time, object, array, boolean, integer, and number), so no type conversions are performed for the Stripe integration.

Performance Considerations and Notes

In order to read large files from a remote location, this connector uses the smart_open library. However, it is possible to switch to either GCSFS or S3FS implementations as it is natively supported by the pandas library. This choice is made possible through the optional reader_impl parameter.

• Note that for local filesystem, the file probably have to be stored somewhere in the /tmp/daspire_local folder, so the URL should also starts with /local/.

• Please make sure that Docker Desktop has access to /tmp (and /private on a MacOS, as /tmp has a symlink that points to /private. It will not work otherwise). You allow it with "File sharing" in Settings -> Resources -> File sharing -> add the one or two above folder and hit the "Apply & restart" button.

• The JSON implementation needs to be tweaked in order to produce more complex catalog and is still in an experimental state: Simple JSON schemas should work at this point but may not be well handled when there are multiple layers of nesting.