> ## Documentation Index
> Fetch the complete documentation index at: https://docs.unstructured.io/llms.txt
> Use this file to discover all available pages before exploring further.
# IBM watsonx.data
The IBM watsonx.data destination connector relies on an Apache Iceberg-based catalog within the watsonx.data data store instance.
Apache Iceberg is suitable for managed data storage and cataloging, but not for embedding storage or semantic similarity
queries. For embedding storage and semantic similarity queries, Unstructured recommends that you use the following destination connectors
instead:
* [Astra DB](/api-reference/workflow/destinations/astradb)
* [Milvus](/api-reference/workflow/destinations/milvus) on IBM watsonx.data
If you're new to Unstructured, read this note first.
Before you can create a destination connector, you must first sign in to your Unstructured account:
* If you do not already have an Unstructured account, [sign up for free](https://unstructured.io/?modal=try-for-free).
After you sign up, you are automatically signed in to your new Unstructured **Let's Go** account, at [https://platform.unstructured.io](https://platform.unstructured.io).
To sign up for a **Business** account instead, [contact Unstructured Sales](https://unstructured.io/?modal=contact-sales), or [learn more](/api-reference/overview#pricing).
* If you already have an Unstructured **Let's Go**, **Pay-As-You-Go**, or **Business SaaS** account and are not already signed in, sign in to your account at
[https://platform.unstructured.io](https://platform.unstructured.io). For other types of **Business** accounts, see your Unstructured account administrator for sign-in instructions,
or email Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).
After you sign in, the [Unstructured user interface](/ui/overview) (UI) appears, which you use to get your Unstructured API key.
1. After you sign in to your Unstructured **Let's Go**, **Pay-As-You-Go**, or **Business** account, click **API Keys** on the sidebar.
For a **Business** account, before you click **API Keys**, make sure you have selected the organizational workspace you want to create an API key
for. Each API key works with one and only one organizational workspace. [Learn more](/ui/account/workspaces#create-an-api-key-for-a-workspace).
2. Click **Generate API Key**.
3. Follow the on-screen instructions to finish generating the key.
4. Click the **Copy** icon next to your new key to add the key to your system's clipboard. If you lose this key, simply return and click the **Copy** icon again.
After you create the destination connector, add it along with a
[source connector](/api-reference/workflow/sources/overview) to a [workflow](/api-reference/workflow/overview).
Then run the worklow as a [job](/api-reference/workflow/jobs). To learn how, try out the
the notebook [Dropbox-To-Pinecone Connector API Quickstart for Unstructured](https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Dropbox_To_Pinecone_Connector_Quickstart.ipynb),
or watch the two 4-minute video tutorials for the [Unstructured Python SDK](/api-reference/workflow/overview#unstructured-python-sdk).
You can also create destination connectors with the Unstructured user interface (UI).
[Learn how](/ui/destinations/overview).
If you need help, email Unstructured Support at [support@unstructured.io](mailto:support@unstructured.io).
You are now ready to start creating a destination connector! Keep reading to learn how.
Send processed data from Unstructured to IBM watsonx.data.
## Requirements
You will need:
* An [IBM Cloud account](https://cloud.ibm.com/login). [Create an IBM Cloud account](https://cloud.ibm.com/registration) if you do not already have one.
* An API key for the IBM Cloud account. If you do not have one already, create one as follows:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. In the top navigation bar, click **Manage** and then, under **Security and access**, click **Access (IAM)**.
3. On the sidebar, under **Manage identities**, click **API keys**.
4. With the **View** list showing **My IBM Cloud API keys**, click **Create**.
5. Enter some **Name** and an optional **Description** for the API key.
6. Leave **Leaked action** set to **Disable the leaked key** and **Session management** set to **No**.
7. Click **Create**.
8. Click **Copy** or **Download** to copy or save the API key to a secure location. You won't be able to access this API key from this screen again.
* An IBM Cloud Object Storage (COS) instance in the account, and a bucket within that instance. If you do not have them already,
create them as follows:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. Click **Create resource**.
4. With **IBM Cloud catalog** selected, search for and select **Object Storage**.
5. Complete the on-screen instructions to finish creating the COS instance.
6. With the COS instance's settings page shown, on the **Buckets** tab, click **Create bucket**.
7. Complete the on-screen instructions to finish creating the bucket.
* The name, region, and public endpoint for the target bucket within the target Cloud Object Storage (COS) instance. To get these:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. In the list of resources, expand **Storage**, and then click the target COS instance.
4. On the **Buckets** tab, click the target bucket.
5. On the **Configuration** tab, note the following:
* Under **Bucket details**, note the **Bucket name**. This is the bucket's name.
* Under **Bucket details** section, note the value inside of the parentheses inside **Location**, for example `us-east`. This is the bucket's region.
* Under **Endpoints**, note the value of **Public**, for example `s3.us-east.cloud-object-storage.appdomain.cloud`. (Ignore the values of
**Private** and **Direct**). This is the bucket's public endpoint.
* An HMAC access key ID and secret access key for the target Cloud Object Storage (COS) instance. If you do not have them already,
get or create them as follows:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. In the list of resources, expand **Storage**, and then click the target COS instance.
4. On the **Service credentials** tab, if there is a credential that you want to use in the list, expand the credential, and copy the following values to a secure location:
* `access_key_id` under `cos_hmac_keys`, which represents the HMAC access key ID.
* `secret_access_key` under `cos_hmac_keys`, which represents the HMAC secret access key.
After you have copied the preceding values, you have completed this procedure.
5. If there is not a credential that you want to use, or there are no credentials at all, click **New Credential**.
6. Enter some **Name** for the credential.
7. For **Role**, select at least **Writer**, leave **Select Service ID** set to **Auto Generated**,
switch on **Include HMAC Credential**, and then click **Add**.
8. In the list of credentials, expand the credential, and copy the following values to a secure location:
* `access_key_id` under `cos_hmac_keys`, which represents the HMAC access key ID.
* `secret_access_key` under `cos_hmac_keys`, which represents the HMAC secret access key.
* An IBM watsonx.data data store instance in the IBM Cloud account. If you do not have one already, create one as follows:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. Click **Create resource**.
4. With **IBM Cloud catalog** selected, search for and select **watsonx.data**.
5. Complete the on-screen instructions to finish creating the watsonx.data data store instance.
* An Apache Iceberg-based catalog within the watsonx.data data store instance. If you do not have one already, create one as follows:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. In the list of resources, expand **Databases**, and then click the target watsonx.data data store instance.
4. Click **Open web console**.
5. If prompted, log in to the web console.
6. On the sidebar, click **Infrastructure manager**. If the sidebar is not visible, click the **Global navigation** icon to the far left of the
top navigation bar.
7. Click **Add component**.
8. Under **Storage**, click **IBM Cloud Object Storage**, and then click **Next**.
9. Complete the on-screen instructions to finish creating the Iceberg catalog. This includes providing the following settings:
If you select **Discover COS instance**, you must provide the following settings:
* The name of the target COS instance.
* The name of the target storage object (such as the target bucket) within the COS instance.
* Some display name for the component.
* After you provide this information, do the following:
a. Check the box labelled **Associate Catalog**.
b. For **Catalog type**, select **Apache Iceberg**.
c. Enter some **Catalog name**.
d. Click **Associate**.
If you select **Register my own**, you must provide the following settings:
* Some display name for the component.
* The name of the target bucket within the target Cloud Object Storage (COS) instance that you noted earlier.
* The region for the target bucket, which you noted earlier.
* The public endpoint for the target bucket, which you noted earlier. For this screen only, be sure to prefix the public endpoint with `https://`.
* The HMAC access key ID for the target COS instance, which you noted earlier.
* The HMAC secret access key for the target COS instance, which you noted earlier.
* After you provide this information, do the following:
a. Check the box labelled **Associate Catalog**.
b. For **Catalog type**, select **Apache Iceberg**.
c. Enter some **Catalog name**.
d. Click **Associate**.
10. On the sidebar, click **Infrastructure manager**. Make sure the catalog is associated with the appropriate engines. If it is not, rest your mouse
on an unassociated target engine, click the **Manage associations** icon, check the box next to the target catalog's name, and then
click **Save and restart engine**.
To create an engine if one is not already shown, click **Add component**, and follow the on-screen to add an appropriate engine from the list of available **Engines**
(for example, an **IBM Presto** engine).
* The catalog name and metastore REST endpoint for the target Iceberg catalog. To get this:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. In the list of resources, expand **Databases**, and then click the target watsonx.data data store instance.
4. Click **Open web console**.
5. If prompted, log in to the web console.
6. On the sidebar, click **Infrastructure manager**. If the sidebar is not visible, click the **Global navigation** icon to the far left of the
top navigation bar.
7. In the **Catalogs** section, click the target Iceberg catalog.
8. On the **Details** tab, note the value of **Name** representing the catalog name, and **Metastore REST endpoint** representing the metastore REST endpoint. (Ignore the **Metastore Thrift endpoint** value.)
* A namespace (also known as a schema) and a table in the target catalog. If you do not have these already, create them as follows:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. In the list of resources, expand **Databases**, and then click the target watsonx.data data store instance.
4. Click **Open web console**.
5. If prompted, log in to the web console.
6. On the sidebar, click **Data manager**. If the sidebar is not visible, click the **Global navigation** icon to the far left of the
top navigation bar.
7. On the **Browse data** tab, under **Catalogs associated**, click the target catalog.
8. Click the ellipses, and then click **Create schema**.
9. Enter some **Name** for the schema, and then click **Create**.
10. On the sidebar, click **Query workspace**.
11. In the SQL editor, enter and run a table creation statement such as the following one that uses
[Presto SQL](https://prestodb.io/docs/current/connector/iceberg.html) syntax, replacing `` with the name of the target
catalog and `` with the name of the target schema, with at minimum the fields `id`, `type`, `text`, `element_id`, and `record_id`:
```sql theme={null}
CREATE TABLE ..elements (
id varchar,
type varchar,
text varchar,
element_id varchar,
record_id varchar,
)
WITH (
delete_mode = 'copy-on-write',
format = 'PARQUET',
format_version = '2'
)
```
The `record_id`, `element_id`, and `id` fields are closely related, but each has a distinct purpose.
The `record_id` identifies the source document.
* For S3 and Azure Blob connectors, the record ID is a [Version 5 UUID](https://docs.python.org/3/library/uuid.html#uuid.uuid5) generated from the namespace and file path of the document. This ensures that the ID is deterministic and unique at the file level.
* For all other blob connectors, the record ID is a Version 4 UUID representing a random 32-character hexadecimal string.
* For SQL connectors, the record ID is generated from the table name and record ID of the database table.
The `element_id` identifies the specific element. Source connectors generate element ID in one of the following ways, depending on the source connector:
* A SHA-256 hash of the element's text, its position on the page, the page number it's on,
and the name of the related file. This is to ensure that the ID is deterministic and unique at the file level.
* A [Version 4 UUID](https://docs.python.org/3/library/uuid.html#uuid.uuid4) generated using random numbers.
Older connectors generate SHA-256 hashes for element IDs, while more modern connectors generate UUIDs. Going forward, older connectors will be converted to using UUIDs as well.
Each element from the same document contains that document's record ID; this enables Unstructured to identify all the elements generated from a given document.
If a source connector has been set to not reprocess all documents each time a workflow runs, Unstructured uses the record ID (along with the record version) to determine which documents are unchanged and should not be processed again.
The `id` represents the actual row that Unstructured writes into the destination location. The ID is a [Version 5 UUID](https://docs.python.org/3/library/uuid.html#uuid.uuid5) generated from the namespace, element ID and record ID of the source document. The ID is deterministic and unique at the row level.
Destination connectors process document updates in one of the following ways, depending on the connector:
* Use the record ID to identify and delete all elements from a given document, prior to writing new elements from that document into the destination.
* Use the ID to perform upsert operations without generating duplicate rows, ensuring that reprocessing documents is idempotent.
```mermaid theme={null}
flowchart LR
subgraph Source
subgraph Document
RID[Record ID]
end
end
subgraph el1[Element]
EID1[Element ID]
end
subgraph el2[Element]
EID2[Element ID]
end
subgraph el3[Element]
EID3[Element ID]
end
subgraph Destination
subgraph row1[Row]
ID1[ID]
end
subgraph row2[Row]
ID2[ID]
end
subgraph row3[Row]
ID3[ID]
end
end
RID --> el1
RID --> el2
RID --> el3
el1 --> row1
el2 --> row2
el3 --> row3
```
For objects in the `metadata` field that Unstructured produces and that you want to store in an Apache Iceberg table in IBM watsonx.data,
you must create fields in your table schema that follows Unstructured's `metadata` field naming convention. For example, if Unstructured
produces a `metadata` field with the following child objects:
```json theme={null}
"metadata": {
"is_extracted": "true",
"coordinates": {
"points": [
[
134.20055555555555,
241.36027777777795
],
[
134.20055555555555,
420.0269444444447
],
[
529.7005555555555,
420.0269444444447
],
[
529.7005555555555,
241.36027777777795
]
],
"system": "PixelSpace",
"layout_width": 1654,
"layout_height": 2339
},
"filetype": "application/pdf",
"languages": [
"eng"
],
"page_number": 1,
"image_base64": "aaa...bbb...111",
"image_mime_type": "image/jpeg",
"filename": "realestate.pdf",
"data_source": {
"url": "file:///home/etl/node/downloads/00000000-0000-0000-0000-000000000001/7458635f-realestate.pdf",
"record_locator": {
"protocol": "file",
"remote_file_path": "file:///home/etl/node/downloads/00000000-0000-0000-0000-000000000001/7458635f-realestate.pdf"
}
},
"entities": {
"items": [
{
"entity": "HOME FOR FUTURE",
"type": "ORGANIZATION"
},
{
"entity": "221 Queen Street, Melbourne VIC 3000",
"type": "LOCATION"
}
],
"relationships": [
{
"from": "HOME FOR FUTURE",
"relationship": "based_in",
"to": "221 Queen Street, Melbourne VIC 3000"
}
]
}
}
```
You could create corresponding fields in your table schema with the following field names and data types:
| **Field name** | **Apache Iceberg data type** |
| ----------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
| `is_extracted` | `varchar` |
| `points` | `varchar` |
| `system` | `varchar` |
| `layout_width` | `bigint` |
| `layout_height` | `bigint` |
| `filetype` | `varchar` |
| `languages` | `array(varchar)` |
| `page_number` | `varchar` |
| `image_base64` | `varchar` |
| `image_mime_type` | `varchar` |
| `filename` | `varchar` |
| `url` | `varchar` |
| `record_locator` | `varchar` |
| `entities` | `row(items array(row(entity varchar, type varchar)), relationships array(row("from" varchar, relationship varchar, "to" varchar)))` |
Incoming elements that do not have matching column
names will be dropped upon record insertion. For example, if the incoming data has an element named `sent_from` and there is no
column named `sent_from` in the table, the `sent_from` element will be dropped upon record insertion. You should modify the preceding
sample table creation statement to add columns for any additional elements that you want to be included upon record
insertion.
To increase query performance, Iceberg uses [hidden partitioning](https://iceberg.apache.org/docs/latest/partitioning/) to
group similar rows together when writing. You can also
[explicitly define partitions](https://prestodb.io/docs/current/connector/iceberg.html#create-table) as part of the
preceding `CREATE TABLE` statement.
* The name of the target namespace (also known as a schema) within the target catalog, and name of the target table within that schema. To get these:
1. [Log in to your IBM Cloud account](https://cloud.ibm.com/login).
2. On the sidebar, click the **Resource list** icon. If the sidebar is not visible, click the **Navigation Menu** icon to the far left of the
top navigation bar.
3. In the list of resources, expand **Databases**, and then click the target watsonx.data data store instance.
4. Click **Open web console**.
5. If prompted, log in to the web console.
6. On the sidebar, click **Data manager**. If the sidebar is not visible, click the **Global navigation** icon to the far left of the
top navigation bar.
7. On the **Browse data** tab, expand the name of the target catalog, and note the names of the target schema and target table.
* The name of the column in the target table that uniquely identifies each of the records in the table.
* To improve performance, the target table should be set to regularly remove old metadata files. To do this, run the following Python script.
(You cannot use the preceding `CREATE TABLE` statement, or other SQL statements such as `ALTER TABLE`, to set this behavior.) To get the
values for the specified environment variables, see the preceding instructions.
```python theme={null}
# Improves performance by setting the target table to regularly remove
# old metadata files.
#
# First, install the following dependencies into your Python virtual
# environment:
#
# pip install requests pyiceberg pyarrow
#
# Then, set the following environment variables:
#
# IBM_IAM_API_KEY - An API key value for the target IBM Cloud account.
# IBM_ICEBERG_CATALOG_METASTORE_REST_ENDPOINT - The metastore REST endpoint
# value for the target Apache Iceberg catalog in the target IBM watsonx.data
# data store instance.
# IBM_COS_BUCKET_PUBLIC_ENDPOINT - The target IBM Cloud Object Storage (COS)
# instance’s endpoint value.
# IBM_COS_ACCESS_KEY - An HMAC access key ID for the target COS instance.
# IBM_COS_SECRET_ACCESS_KEY - The associated HMAC secret access key ID for the
# target HMAC access key.
# IBM_COS_BUCKET_REGION - The target COS instance’s region short ID.
# IBM_ICEBERG_CATALOG - The name of the target Iceberg catalog.
# IBM_ICEBERG_SCHEMA - The name of the target namespace (also known as a schema)
# in the target catalog.
# IBM_ICEBERG_TABLE - The name of the target table in the target schema.
#
# To get these values, see the Unstructured documentation for the
# IBM watsonx.data connector.
import os
import requests
from pyiceberg.catalog import load_catalog
def main():
# Get a bearer token for the target IBM Cloud account.
bearer_token = requests.post(
url="https://iam.cloud.ibm.com/identity/token",
headers={
"Content-Type": "application/x-www-form-urlencoded",
"Accept": "application/json"
},
data={
"grant_type": "urn:ibm:params:oauth:grant-type:apikey",
"apikey": os.getenv("IBM_IAM_API_KEY")
}
).json().get("access_token")
# Connect to the target Iceberg catalog.
catalog = load_catalog(
os.getenv("IBM_ICEBERG_CATALOG"),
**{
"type": "rest",
"uri": f"https://{os.getenv("IBM_ICEBERG_CATALOG_METASTORE_REST_ENDPOINT")}/mds/iceberg",
"token": bearer_token,
"warehouse": os.getenv("IBM_ICEBERG_CATALOG"),
"s3.endpoint": os.getenv("IBM_COS_BUCKET_PUBLIC_ENDPOINT"),
"s3.access-key-id": os.getenv("IBM_COS_ACCESS_KEY"),
"s3.secret-access-key": os.getenv("IBM_COS_SECRET_ACCESS_KEY"),
"s3.region": os.getenv("IBM_COS_BUCKET_REGION")
},
)
# Load the target table.
table = catalog.load_table(f"{os.getenv("IBM_ICEBERG_SCHEMA")}.{os.getenv("IBM_ICEBERG_TABLE")}")
# Set the target table's properties to remove old metadata files.
with table.transaction() as transaction:
transaction.set_properties(
{
"commit.manifest.min-count-to-merge": 10,
"commit.manifest-merge.enabled": True,
"write.metadata.previous-versions-max": 10,
"write.metadata.delete-after-commit.enabled": True,
}
)
# Confirm that the target table's properties were set as expected.
print(table.metadata.properties)
if __name__ == "__main__":
main()
```
## Examples
To create an IBM watsonx.data destination connector, see the following examples.
For more information on working with destination connectors using the Unstructured API, see [Destination endpoints](/api-reference/api/destination/destination-apis).
```python Python SDK theme={null}
import os
from unstructured_client import UnstructuredClient
from unstructured_client.models.operations import CreateDestinationRequest
from unstructured_client.models.shared import CreateDestinationConnector
with UnstructuredClient(api_key_auth=os.getenv("UNSTRUCTURED_API_KEY")) as client:
response = client.destinations.create_destination(
request=CreateDestinationRequest(
create_destination_connector=CreateDestinationConnector(
name="",
type="ibm_watsonx_s3",
config={
"iceberg_endpoint": "",
"object_storage_endpoint": "",
"object_storage_region": "",
"iam_api_key": "",
"access_key_id": "",
"secret_access_key": "",
"catalog": "",
"namespace": "",
"table": "
",
"max_retries": ,
"max_retries_connection": ,
"record_id_key": ""
}
}'
```
## Configuration settings
Replace the preceding placeholders as follows:
A unique name for this connector.
The metastore REST endpoint for the target Apache Iceberg-based catalog within the IBM watsonx.data data store instance. Do not include `https://` in this value.
The public endpoint for the target bucket within the IBM Cloud Object Storage (COS) instance that is associated with the catalog. Do not include `https://` in this value.
The region short ID (such as us-east) for the bucket.
A valid API key value for the IBM Cloud account.
A valid hash-based message authentication code (HMAC) access key ID for the COS instance.
The HMAC secret access key for the access key ID.
The name of the target Apache Iceberg-based catalog within the IBM watsonx.data data store instance.
The name of the target namespace (also known as a schema) within the catalog.
The name of the target table within the namespace (schema).
The maximum number of retries for the upload process. Typically, an optimal setting is `150`. If specified, it must be a number between `2` and `500`, inclusive.
The maximum number of retries when connecting to the catalog. Typically, an optimal setting is `15`. If specified, it must be a number between `2` and `100`, inclusive.
The name of the column that uniquely identifies each record in the target table.
## Learn more
* [Unstructured + IBM watsonx.data: A New OEM Partnership Powering the Future of Enterprise AI](https://unstructured.io/blog/unstructured-ibm-watsonx-data-a-new-oem-partnership-powering-the-future-of-enterprise-ai)