Rename integrations to resources on the SDK and example notebooks

examples/churn_prediction/Customer Churn Prediction.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "3637c5d6-6d94-4db5-ba31-5cea5f80b5b3",
    "metadata": {},
@@ -779,7 +778,7 @@
    "source": [
     "### Connecting to Your Data\n",
     "\n",
-    "Workflows access and publish to data integrations that are configured on the Integrations Page.  In this demo we will connect to the demo data integration which is a Postgres database containing several standard [sample datasets](https://docs.aqueducthq.com/example-workflows/demo-data-warehouse) including some synthetic customer churn data.  Each kind of data integration may offer different functionality.  Here we are using a relational integration which support general SQL expressions.\n"
+    "Workflows access and publish to data resource that are configured on the Resources Page.  In this demo we will connect to the demo data resource which is a Postgres database containing several standard [sample datasets](https://docs.aqueducthq.com/example-workflows/demo-data-warehouse) including some synthetic customer churn data.  Each kind of data resource may offer different functionality.  Here we are using a relational resource which support general SQL expressions.\n"
    ]
   },
   {
@@ -797,7 +796,7 @@
     }
    ],
    "source": [
-    "warehouse = client.integration(name=\"aqueduct_demo\")\n",
+    "warehouse = client.resource(name=\"aqueduct_demo\")\n",
     "\n",
     "# customers_table is an Aqueduct TableArtifact, which is a wrapper around\n",
     "# a Pandas DataFrame. A TableArtifact can be used as argument to any operator\n",
@@ -1505,9 +1504,9 @@
    "id": "1d92df34-608b-401d-aff7-f7cb8a9a4bc7",
    "metadata": {},
    "source": [
-    "### Saving Tables Artifacts to Integrations\n",
+    "### Saving Tables Artifacts to Data Resources\n",
     "\n",
-    "So far we have defined a workflow to build the `churn_table` containing our churn predictions.  We now want to publish this table where others can use it.  We do this by `saving` the table to various integrations.  \n",
+    "So far we have defined a workflow to build the `churn_table` containing our churn predictions.  We now want to publish this table where others can use it.  We do this by `saving` the table to various resources.\n",
     "\n",
     "First we save the table back to the data warehouse that contains the original customer data. "
    ]
@@ -1528,7 +1527,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "e20c2fbe-e201-4a92-a50f-8d82aa771c19",
    "metadata": {
@@ -1582,7 +1580,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1596,7 +1594,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.8.13"
   },
   "vscode": {
    "interpreter": {

examples/diabetes-classifier/Classifying Diabetes Risk.ipynb

@@ -1169,7 +1169,7 @@
     }
    ],
    "source": [
-    "demodb = client.integration(\"aqueduct_demo\")\n",
+    "demodb = client.resource(\"aqueduct_demo\")\n",
     "\n",
     "# mpg_data is an Aqueduct TableArtifact, which is a wrapper around\n",
     "# a Pandas DataFrame. A TableArtifact can be used as argument to any operator\n",
@@ -1327,7 +1327,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1341,7 +1341,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.8.13"
   },
   "vscode": {
    "interpreter": {

examples/finetune-resnet-50/FineTune ResNet-50.ipynb

@@ -26,13 +26,13 @@
     "import aqueduct as aq\n",
     "from aqueduct import op, metric\n",
     "\n",
-    "K8S_INTEGRATION_NAME = 'eks-us-east-2' # REPLACE ME!\n",
+    "K8S_RESOURCE_NAME = 'eks-us-east-2' # REPLACE ME!\n",
     "\n",
     "client = aq.Client()\n",
     "\n",
     "# This line sets Aqueduct to run in lazy mode, since some of our compute can be expensive,\n",
     "# and it sets all functions to run on the EKS cluster we've connected to.\n",
-    "aq.global_config({\"lazy\": True, \"engine\": K8S_INTEGRATION_NAME})"
+    "aq.global_config({\"lazy\": True, \"engine\": K8S_RESOURCE_NAME})"
    ]
   },
   {
@@ -53,7 +53,7 @@
     "DATASET_BUCKET_NAME = 'datasets' # REPLACE ME!\n",
     "DATASET_PATH = 'resnet-data/resnet.zip' # REPLACE ME!\n",
     "\n",
-    "datasets = client.integration(DATASET_BUCKET_NAME)\n",
+    "datasets = client.resource(DATASET_BUCKET_NAME)\n",
     "\n",
     "# Due to the way S3 works, it's more efficient for us to load a large zipped file \n",
     "# rather than many small files. As a result, we load a zipfile.\n",
@@ -397,7 +397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

examples/house-price-prediction/House Price Prediction.ipynb

@@ -1,7 +1,6 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "a047703f",
    "metadata": {},
@@ -918,7 +917,7 @@
     }
    ],
    "source": [
-    "demo_db = client.integration(\"aqueduct_demo\")\n",
+    "demo_db = client.resource(\"aqueduct_demo\")\n",
     "raw_data = demo_db.sql(\"select * from house_prices;\")\n",
     "\n",
     "filled_data = fill_missing_data(raw_data)\n",
@@ -991,7 +990,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1005,7 +1004,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.8.13"
   },
   "vscode": {
    "interpreter": {
@@ -1015,4 +1014,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

examples/mpg-regressor/Predicting MPG.ipynb

@@ -52,7 +52,7 @@
    "id": "3ec5e482",
    "metadata": {},
    "source": [
-    "Once we have our client, the first thing we'll do is load our data. Aqueduct has the ability to most common databases and storage systems (check out the Integrations page on the Aqueduct UI). Here, we'll load a connection to the default `aqueduct_demo` database, which comes preloaded with a number of [common datasets](https://docs.aqueducthq.com/example-workflows/demo-data-warehouse). \n",
+    "Once we have our client, the first thing we'll do is load our data. Aqueduct has the ability to most common databases and storage systems (check out the Resources page on the Aqueduct UI). Here, we'll load a connection to the default `aqueduct_demo` database, which comes preloaded with a number of [common datasets](https://docs.aqueducthq.com/example-workflows/demo-data-warehouse). \n",
     "\n",
     "Once we have a connection to the demo DB, we can run a SQL query to retrieve our base data."
    ]
@@ -73,7 +73,7 @@
     }
    ],
    "source": [
-    "demodb = client.integration(\"aqueduct_demo\")\n",
+    "demodb = client.resource(\"aqueduct_demo\")\n",
     "\n",
     "# mpg_data is an Aqueduct TableArtifact, which is a wrapper around\n",
     "# a Pandas DataFrame. A TableArtifact can be used as argument to any operator\n",
@@ -1976,7 +1976,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1990,7 +1990,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.2"
+   "version": "3.8.13"
   },
   "vscode": {
    "interpreter": {

examples/predicting-march-madness/MarchMadnessWorkflow.ipynb

@@ -33,7 +33,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "warehouse = client.integration(\"postgres_integration\")"
+    "warehouse = client.resource(\"postgres_integration\")"
    ]
   },
   {

examples/roberta-sentiment-analysis/RoBERTa Sentiment.ipynb

@@ -32,7 +32,7 @@
     "\n",
     "client = aq.Client()\n",
     "\n",
-    "# This config tells Aqueduct to run every operator on the integration named \"eks-us-east-2\".\n",
+    "# This config tells Aqueduct to run every operator on the resource named \"eks-us-east-2\".\n",
     "# It also activates \"lazy\" mode, meaning that we will only trigger compute operations\n",
     "# when data is requested since some of the functions below can be expensive.\n",
     "aq.global_config({\"lazy\": True, 'engine': 'eks-us-east-2'})"
@@ -116,7 +116,7 @@
    "source": [
     "# Load the data from the S3 bucket and see a preview of the table.\n",
     "# This is about ~100MB of data and takes about ~10s to load.\n",
-    "datasets_bucket = client.integration('datasets')\n",
+    "datasets_bucket = client.resource('datasets')\n",
     "tweets = datasets_bucket.file('got_s8_tweets.csv', artifact_type=\"table\", format=\"csv\")\n",
     "\n",
     "tweets.get().head()"
@@ -134,7 +134,7 @@
     "We first create batches of 1K tweets to tokenize at a time. To keep our tensors uniform, we pad them with 0s as necessary and then concatenate them. \n",
     "\n",
     "The `@op` decorator here has a few configuration parameters:\n",
-    "* First, the `engine` parameter tells us that we're going to be running on our EKS cluster in `us-east-2`; you can see the configuration for this integration on the Aqueduct UI. \n",
+    "* First, the `engine` parameter tells us that we're going to be running on our EKS cluster in `us-east-2`; you can see the configuration for this resource on the Aqueduct UI. \n",
     "* Second, we specify the requirements needed to run this function (`torch` and `transformers`); if necessary, we could specify the required versions as well. \n",
     "* Finally, we tell Aqueduct to give this container 15GB of RAM."
    ]
@@ -1058,7 +1058,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

examples/sentiment-analysis/Sentiment Model.ipynb

@@ -78,7 +78,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "warehouse = client.integration(\"aqueduct_demo\")\n",
+    "warehouse = client.resource(\"aqueduct_demo\")\n",
     "\n",
     "# reviews_table is an Aqueduct TableArtifact, which is a wrapper around\n",
     "# a Pandas DataFrame. A TableArtifact can be used as argument to any operator\n",

examples/tutorials/Parameters Tutorial.ipynb

@@ -58,7 +58,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "db = client.integration(\"aqueduct_demo\")\n",
+    "db = client.resource(\"aqueduct_demo\")\n",
     "\n",
     "# reviews_table is an Aqueduct TableArtifact, which is a wrapper around\n",
     "# a Pandas DataFrame. A TableArtifact can be used as argument to any operator\n",

examples/tutorials/Quickstart Tutorial.ipynb

@@ -222,7 +222,7 @@
     }
    ],
    "source": [
-    "demo_db = client.integration(\"aqueduct_demo\")\n",
+    "demo_db = client.resource(\"aqueduct_demo\")\n",
     "reviews_table = demo_db.sql(\"select * from hotel_reviews;\")\n",
     "\n",
     "# You will see the type of `reviews_table` is an Aqueduct TableArtifact.\n",
@@ -529,7 +529,7 @@
     "\n",
     "---\n",
     "### Saving Data\n",
-    "Finally, we can save the transformed table `strlen_table` back to the Aqueduct demo database. See [here](https://docs.aqueducthq.com/integrations/using-integrations) for more details around using integration objects."
+    "Finally, we can save the transformed table `strlen_table` back to the Aqueduct demo database. See [here](https://docs.aqueducthq.com/integrations/using-integrations) for more details around using resource objects."
    ]
   },
   {
@@ -599,8 +599,16 @@
     "\n",
     "---\n",
     "\n",
-    "There is a lot more you can do with Aqueduct, including having flows run automatically on a cadence, parameterizing flows, and reading to and writing from many different integrations (S3, Postgres, etc.). Check out the other tutorials and examples [here](https://docs.aqueducthq.com/example-workflows) for a deeper dive!"
+    "There is a lot more you can do with Aqueduct, including having flows run automatically on a cadence, parameterizing flows, and reading to and writing from many different data resources (S3, Postgres, etc.). Check out the other tutorials and examples [here](https://docs.aqueducthq.com/example-workflows) for a deeper dive!"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80d87b8a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

examples/wine-ratings-prediction/Predict Missing Wine Ratings.ipynb

@@ -64,7 +64,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "demodb = client.integration(\"aqueduct_demo\")\n",
+    "demodb = client.resource(\"aqueduct_demo\")\n",
     "\n",
     "# wines is an Aqueduct TableArtifact, which is a wrapper around\n",
     "# a Pandas DataFrame. A TableArtifact can be used as argument to any operator\n",

examples/wine-ratings-prediction/Spark Wine Ratings Demo.ipynb

@@ -13,9 +13,9 @@
     "\n",
     "**Throughout this notebook, you'll see a decorator (`@aq.op`) above functions. This decorator allows Aqueduct to run your functions as a part of a workflow automatically.**\n",
     "\n",
-    "**To run this notebook, you will have to connect the following integrations:**\n",
-    "- A Databricks or Spark integration\n",
-    "- A data integration (ie Snowflake)\n",
+    "**To run this notebook, you will have to connect the following resources:**\n",
+    "- A Databricks or Spark compute resource\n",
+    "- A data resource (ie Snowflake)\n",
     "- S3 (must also be used as metadata store)"
    ]
   },
@@ -48,7 +48,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "aqueduct.global_config({'engine': '<spark or databricks integration>', 'lazy': True})"
+    "aqueduct.global_config({'engine': '<spark or databricks resource>', 'lazy': True})"
    ]
   },
   {
@@ -68,7 +68,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "snowflake_warehouse = client.integration(\"<snowflake integration>\")\n",
+    "snowflake_warehouse = client.resource(\"<snowflake resource>\")\n",
     "wine_table = snowflake_warehouse.sql(\"select * from wine;\")"
    ]
   },
@@ -371,7 +371,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.0"
+   "version": "3.8.13"
   }
  },
  "nbformat": 4,

integration_tests/backend/setup/changing_saves_workflow.py

@@ -11,7 +11,7 @@
 def setup_changing_saves(client: aqueduct.Client, integration_name: str) -> str:
     name = "Test: Changing Saves"
     n_runs = 4
-    integration = client.integration(name=integration_name)
+    integration = client.resource(name=integration_name)
 
     ###
     table = integration.sql(query="SELECT * FROM wine;")

integration_tests/backend/setup/flow_with_failure.py

@@ -9,7 +9,7 @@
 def setup_flow_with_failure(client: aqueduct.Client, integration_name: str) -> str:
     name = "Test: Flow with Failure"
     n_runs = 1
-    integration = client.integration(name=integration_name)
+    integration = client.resource(name=integration_name)
 
     @aqueduct.op
     def bad_op(df):

integration_tests/backend/setup/flow_with_metrics_and_checks.py

@@ -15,7 +15,7 @@ def setup_flow_with_metrics_and_checks(
 ) -> str:
     name = workflow_name if workflow_name else "Test: Flow with Metrics and Checks"
     n_runs = 2
-    integration = client.integration(name=integration_name)
+    integration = client.resource(name=integration_name)
 
     @aqueduct.metric
     def size(df):

integration_tests/backend/setup/flow_with_sleep.py

@@ -14,7 +14,7 @@
 def setup_flow_with_sleep(client: aqueduct.Client, integration_name: str) -> str:
     name = "Test: Flow with Sleep"
     n_runs = 1
-    integration = client.integration(name=integration_name)
+    integration = client.resource(name=integration_name)
 
     @aqueduct.op
     def sleeping_op(df):

integration_tests/backend/test_reads.py

@@ -35,7 +35,7 @@ class TestBackend:
     @classmethod
     def setup_class(cls):
         cls.client = aqueduct.Client(pytest.api_key, pytest.server_address)
-        cls.integration = cls.client.integration(name=pytest.integration)
+        cls.integration = cls.client.resource(name=pytest.integration)
         cls.flows = {
             "changing_saves": setup_changing_saves(cls.client, pytest.integration),
             "flow_with_failure": setup_flow_with_failure(cls.client, pytest.integration),