PyExasol documentation added

doc/connect_to_exasol/connection.rst

@@ -0,0 +1,29 @@
+Establishing a Connection
+==============================
+
+
+
+The **connect()** method creates a connection to the database and returns an object **ExaConnection**.
+
+The following example show how to connect with Exasol.
+
+.. code-block:: python
+
+    import pyexasol
+
+    try:
+        # Connect to Exasol
+        connection = pyexasol.connect(dsn='127.0.0.1', 
+                             user='scott', 
+                             password='password')
+        print("Connected successfully to Exasol.")
+    except Exception as e:
+        print(f"Error: {e}")
+
+For additional parameter list please visit `ExaConnection API ref <https://exasol.github.io/pyexasol/master/api.html#pyexasol.ExaConnection>`_. 
+To handle connection errors, use the try statement and catch all errors using the errors.Error exception:
+
+
+
+
+

doc/connect_to_exasol/first-querry.rst

@@ -0,0 +1,65 @@
+Querying Using PyExasol
+=========================================
+
+In this section, we will create a table and insert data inside that table using PyExasol.
+The schema we are working with in this example is named 'SCHEMA'. You can replace it with your own schema name.
+
+
+Creating a Table
+----------------------
+
+Using the **execute** method of the connection object we created earlier in the :ref:`installation-pyexasol` section.
+We are creating a table called **Employee**.
+
+.. code-block:: python
+
+    create_table = connection.execute("CREATE OR REPLACE TABLE SCHEMA.Employee(employee_id INT PRIMARY KEY, " \
+    "name VARCHAR(50), " \
+    "hire_date DATE)")
+
+For more information about the **execute** method. Check out `ExaConnection.execute class <https://exasol.github.io/pyexasol/master/_modules/pyexasol/connection.html#ExaConnection.execute>`_.
+
+
+Inserting Data into the Table
+------------------------------------
+
+Using Pandas DataFrame
+^^^^^^^^^^^^^^^^^^^^^^^
+
+This is an example of inserting values into the "EMPLOYEE" table using pandas dataframe.
+
+.. code-block:: python
+
+    data = {
+        "employee_id": [15, 20, 30, 40, 50],
+        "name": ['John', 'Jane', 'Michael', 'Emily', 'David'],
+        "hire_date": ['2023-01-15', '2022-07-10', '2021-03-05', '2023-06-20', '2020-11-12'],
+    }
+    df_employees = pd.DataFrame(data)
+    connection.import_from_pandas(df_employees, ('SCHEMA', 'EMPLOYEE'))
+
+For custom params check out `ExaConnection.import_from_pandas <https://exasol.github.io/pyexasol/master/api.html#pyexasol.ExaConnection.import_from_pandas>`_
+
+Using a file
+^^^^^^^^^^^^^^
+
+.. code-block:: python
+
+    inserting_a_file = connection.import_from_file('file.csv', ('SCHEMA','EMPLOYEE'))
+
+Make sure to that your file has the same column names as your table.
+
+There are multiple options to import data from using PyExasol. Check out more options from `here <https://exasol.github.io/pyexasol/master/api.html#pyexasol.ExaConnection.import_from_callback>`_
+
+
+Retrieving Data From the Database
+------------------------------------
+
+Like importing there are multiple options of **retrieving** data using PyExasol.
+Here is an example of exporting data into a pandas dataframe.
+
+.. code-block:: python
+
+    retrieve_into_pandas = connection.export_to_pandas('SELECT * FROM SCHEMA.EMPLOYEE')
+    print(retrieve_into_pandas.shape)
+

doc/connect_to_exasol/index.rst

@@ -0,0 +1,16 @@
+
+PyExasol (Connect with Python)
+==============================================
+
+Welcome to PyExasol, Exasol's proprietary Python connector.
+
+.. toctree::
+   :maxdepth: 2
+
+   overview.rst
+   installation.rst
+   connection.rst
+   first-querry.rst
+
+
+

doc/connect_to_exasol/installation.rst

@@ -0,0 +1,28 @@
+.. _installation-pyexasol:
+
+Installation
+================
+
+PyExasol is distributed through `PyPI <https://pypi.org/project/pyexasol/>`_ . It can be installed via pip, poetry, or any other type of dependency management tool:
+
+.. code-block:: bash
+
+   pip install pyexasol
+
+
+Optional Dependencies
+------------------------
+
+PyExasol can also be installed with sets of optional dependencies to enable certain functionality.
+
+.. code-block:: bash
+
+   pip install pyexasol[optional-package-name]
+
+- ``orjson`` is required for ``json_lib=orjson`` to improve JSON parsing performance.
+- ``pandas`` is required for importing_and_exporting_data functions working with :class:`pandas.DataFrame`.
+- ``polars`` is required for importing_and_exporting_data functions working with :class:`polars.DataFrame`.
+- ``pyarrow`` is required for importing_and_exporting_data functions working with :class:`pyarrow.parquet`.
+- ``pproxy`` is used in the examples to test an HTTP proxy.
+- ``rapidjson`` is required for ``json_lib=rapidjson`` to improve JSON parsing performance.
+- ``ujson`` is required for ``json_lib=ujson`` to improve JSON parsing performance.

doc/connect_to_exasol/overview.rst

@@ -0,0 +1,32 @@
+===========
+Overview
+===========
+
+Exasol is compatible with a wide range of technologies.
+
+PyExasol is the officially supported Python connector for Exasol. It helps to handle massive volumes of data commonly associated with DBMS within python.
+
+PyExasol provides an API to read & write multiple data streams in parallel using separate processes, which is necessary to fully utilize hardware and achieve linear scalability. With PyExasol you are no longer limited to a single CPU core.
+
+
+PyExasol Main concepts
+-----------------------
+
+* Based on `WebSocket <https://github.com/exasol/websocket-api>`_ protocol.
+* Optimized for minimum overhead.
+* Easy integration with pandas, parquet, and polars via HTTP transport.
+* Compression to reduce network bottleneck.
+
+System Requirements
+---------------------
+
+* **Exasol >= 7.1**
+* **Python >= 3.9**
+
+PyExasol Versions
+---------------------------
+
+For the latest version and info about PyExasol versions, refer to the `version history <https://github.com/exasol/pyexasol/releases>`_
+
+
+

doc/data_science/ai_architecture/bfs.rst

@@ -1,3 +1,5 @@
+.. _bucket-fs:
+
 BucketFS
 ===================
 

doc/examples/sales_forecasting/index.rst

@@ -29,7 +29,7 @@ What You'll Need
 You’ll need to:
 
 * Set up `AI-Lab <https://github.com/exasol/ai-lab>`_ (We recommend the Docker version because it comes with the Docker database. Alternatively, you can use `Exasol SaaS <https://cloud.exasol.com>`_).
-* Download the .sql file.
+* Download and import the notebooks zip file from :ref:`setup-sf`.
 
 What You'll Learn
 -----------------
@@ -38,8 +38,8 @@ In this demo, you will learn how to:
 
 * **Build a forecasting model**: Train a sales forecasting model using XGBoost in JupyterLab.
 * **Integrate with Exasol**: Connect JupyterLab to Exasol and use it as both a data source and deployment target.
-* **Deploy ML models inside the database**: Upload a trained model into Exasol and register it as a User-Defined Function (UDF).
-* **Run predictions (UDFs) with SQL queries**: Execute forecasts directly within Exasol using standard SQL, without moving data outside the database.
+* **Deploy ML models inside the database**: Upload a trained model into :ref:`bucket-fs`.
+* **Run predictions (UDFs) with SQL queries**: Execute forecasts directly within Exasol using standard SQL, without moving data outside the database using :ref:`overview-label`.
 * **Leverage Exasol’s AI architecture**: Understand how Exasol enables in-database machine learning workflows for speed, scalability, and simplicity.
 
 What You'll Build
@@ -48,7 +48,7 @@ What You'll Build
 In this demo, you will build a complete in-database machine learning workflow for sales forecasting:
 
 * A **sales forecasting model** trained with XGBoost in JupyterLab.
-* A deployment pipeline to **upload the trained model into Exasol**.
-* An :ref:`overview-label` inside Exasol that encapsulates the model logic.
-* A way to run predictions directly in Exasol using SQL queries.
-* An end-to-end example of Exasol’s AI architecture in action — from model saving to in-database prediction.
+* A deployment pipeline to **upload the trained model** into :ref:`bucket-fs`.
+* A :ref:`overview-label` inside Exasol that encapsulates the model logic.
+* A way to run predictions directly in the database using SQL queries.
+* An end-to-end example of Exasol’s AI architecture in action — from model saving to in-database predictions.

doc/examples/sales_forecasting/setup.rst

@@ -1,7 +1,10 @@
+.. _setup-sf:
+
+
 Setup
 ======
 
-You will use `AI-Lab <https://github.com/exasol/ai-lab>`_ and an Exasol database. You can choose the Docker DB that comes with AI-Lab or
+You will be needing `AI-Lab <https://github.com/exasol/ai-lab>`_ and an Exasol database. You can choose the Docker DB that comes with AI-Lab or
 use `Exasol SaaS <https://cloud.exasol.com>`_ and start with the free trial.
 
 Once you have finished setting up with `AI-Lab <https://github.com/exasol/ai-lab>`_, we can proceed.
@@ -15,6 +18,8 @@ If this is your first time using `AI-Lab <https://github.com/exasol/ai-lab>`_, f
 * Go through all of the cells in the `main_config` file.
 * You have multiple database options: Docker (recommended), SaaS, and On-Prem.
 * The last cell creates a **schema** for you and ensures that you are connected to a database.
+* We can now proceed with our demo.
+
 
 Download the Sales Forecasting Demo File
 ------------------------------------------
@@ -23,7 +28,7 @@ Download the Sales Forecasting Demo File
 * **Extract** the contents of the .zip file using your file explorer.
 * **Create** a folder inside the root folder of AI-Lab and name it "sales_forecasting".
 * **Upload** the files inside extracted folder into the "sales_forecasting" folder. Using the import button on the top right. 
-* **Alternatively** you just upload the .zip file to JupyterLabs and unzip it using :ref:`below`.
+* **Alternatively** you can just upload the .zip file to JupyterLabs and unzip it using :ref:`below`.
 * **Congratulations!** You’re all set, and now **we shall meet in AI-Lab**.
 * The notebooks are **numbered** so you can know which one to visit **first**.
 

doc/index.rst

@@ -11,6 +11,7 @@ Documentation and resources for data scientists and programmatic users to perfor
    data_ingestion
    UDF/index.rst
    data_science/index.rst
+   connect_to_exasol/index.rst
    examples/index.rst
    environments
    integrations