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#6: Translate README to rst and split up sections (#5)
* Translated Readme to rst and split into different section files * Shorten Readme and point from it to github pages
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‎README.md‎

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<h1 align="center">Developer Documentation</h1>
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<h1 align="center">exasol/developer-documentation</h1>
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<p align="center">
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Documentation and resources for data scientists and programmatic users to perform analytics with Exasol and to build applications on top.
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Documentation and resources for developing software and data science solutions and applications on top of the Exasol Analytics Engine.
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</p>
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# Getting Started
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There are three ways to get started with Exasol:
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- Download the [community edition](https://www.exasol.com/free-signup-community-edition/)
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- Use the [docker version](https://github.com/exasol/docker-db)
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- Signup for a free [SaaS trial](https://cloud.exasol.com/signup)
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In the following we are going to use the SaaS version.
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## Setting up the db
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Our [main documentation](https://docs.exasol.com/saas/get_started/saas_first_steps.htm) shows you how to create a SaaS account and setup your first database and cluster.
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## Installing pyexasol
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Once your database is up and running you can connect via any client. From Python we use [PyExasol](https://github.com/exasol/pyexasol).
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It can be easily installed via
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```
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pip install pyexasol[pandas]
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```
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## Connecting to the db
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Connecting to an Exasol database is performed via:
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```python
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import pyexasol
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C = pyexasol.connect(dsn='<host:port>', user='sys', password='exasol')
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```
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It is slightly different when connecting to a SaaS database as we need a [personal access token](https://docs.exasol.com/saas/administration/access_mngt/access_token.htm):
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```python
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import pyexasol
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C = pyexasol.connect(dsn='<host:port>', user='sys', password='<token>')
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```
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Finally you can also wrap all credentials into a [local config file](https://exasol.github.io/pyexasol/master/user_guide/local_config.html):
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```python
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C = pyexasol.connect_local_config('my_exasol')
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```
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## Executing SQL
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Running your first query is pretty straightforward:
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```python
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stmt = C.execute("SELECT * FROM EXA_ALL_USERS")
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for row in stmt:
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print(row)
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```
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It also allows you to load resultsets into a pandas dataframe:
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```python
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C = pyexasol.connect(dsn='<host:port>', user='sys', password='exasol', compression=True)
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df = C.export_to_pandas("SELECT * FROM EXA_ALL_USERS")
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print(df.head())
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```
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# Data Ingestion
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## CSV Files
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## Parquet Files
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## Import from external sources
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## HTTP Transport
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# Distributed Python (UDFs)
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## Intro to UDFs
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## Creating and running UDFs
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## Debugging UDFs
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# Advanced
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## move all the other stuff here
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# Examples
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# Environments
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## Jupyter Notebooks
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## VSCode
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## Positron
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# Integrations
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## JupySQL
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## Pandas
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## Ibis
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## SQLAlchemy
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Checkout the latest [documentation](https://exasol.github.io/developer-documentation/).

‎doc/api.rst‎

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‎doc/changes/unreleased.md‎

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## Features
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* #1: Added initial Project Setup
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* #4; Initial documentation version with outline and first fragments
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* #6: Translate README to rst and split up sections

‎doc/data_ingestion.rst‎

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Data Ingestion
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==============
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CSV Files
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---------
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Parquet Files
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-------------
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Import from external sources
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----------------------------
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HTTP Transport
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--------------

‎doc/developer_guide.rst‎

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‎doc/distributed_python/advanced.rst‎

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Advanced
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--------

‎doc/distributed_python/debugging.rst‎

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Debugging
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---------

‎doc/distributed_python/index.rst‎

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Distributed Python (UDFs)
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=========================
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.. toctree::
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:maxdepth: 1
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intro
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usage
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debugging
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advanced

‎doc/distributed_python/intro.rst‎

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Intro to UDFs
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-------------

‎doc/distributed_python/usage.rst‎

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Creating and running UDFs
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-------------------------

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