Setup Postgres Database - Jupyter Lab and Postgresql

Summary

This video demonstrates how to integrate Jupyter Lab with PostgreSQL for practicing SQL queries in an interactive environment. It explains the prerequisites, including setting up Python 3, Jupyter Lab, and necessary libraries such as IPython-SQL, SQLAlchemy, and Psycopg2 for database connectivity. The guide covers activating a virtual environment, installing libraries, and connecting to PostgreSQL running in a Docker container. The video emphasizes the benefits of using Jupyter for SQL practice, while also recommending SQL Workbench for project-related tasks.

Takeaways

• 🖥️ Jupyter Lab can be used to practice SQL in an intuitive and interactive way, integrating with Postgres for a seamless environment.
• 🐍 Python 3 and Jupyter Lab should be installed for this setup, with optional use of SQL Workbench for query execution.
• ⚙️ ipython-sql and SQLAlchemy are key libraries needed to connect Jupyter Notebooks to Postgres databases.
• 📦 psycopg2 is necessary for connecting to a Postgres database, and you need to install the appropriate OS-level binaries if using Ubuntu.
• 🐳 Docker is used to run Postgres in a container, making it easier to manage database instances within the setup.
• 🔧 A virtual environment is recommended for installing and managing dependencies, ensuring that the libraries don't interfere with the system-level packages.
• 💻 The script explains how to install and activate the virtual environment, install necessary libraries, and configure Jupyter Lab for SQL query execution.
• 📝 The SQL Magic extension in Jupyter allows users to run SQL queries directly in notebooks without needing to write Python code.
• 🌐 You can connect to a Postgres database running in Docker by ensuring it’s properly started and configuring Jupyter to communicate with it.
• 🔍 This method is preferred for learning SQL interactively, but SQL Workbench can also be used in project settings for greater productivity.

Q & A

What is the primary goal of integrating Jupyter Lab with PostgreSQL as discussed in the video?

The goal is to create an intuitive and interactive environment to practice SQL using Jupyter Lab or other tools like SQL Workbench. This setup allows users to write and execute SQL queries within Jupyter notebooks without writing Python code.

What are the prerequisites for setting up the Jupyter Lab environment to work with PostgreSQL?

The prerequisites include having Python 3 installed, setting up Jupyter Lab, and having a virtual machine (VM) provisioned, such as on Google Cloud Platform (GCP). Additionally, PostgreSQL must be set up, either via Docker or directly installed on the system.

Which Python libraries are necessary to connect Jupyter Lab with PostgreSQL?

The necessary libraries include `ipython-sql` and `SQLAlchemy` for database connectivity, and `psycopg2` for connecting specifically to PostgreSQL.

How can you activate the virtual environment that was set up for Jupyter Lab?

You can activate the virtual environment by running the command `source demogel/bin/activate`, where 'demogel' is the name of the virtual environment.

What steps are involved in verifying if SQLAlchemy is installed?

After installing `ipython-sql`, you can run the command `pip list` to check the installed packages. SQLAlchemy should appear in the list if it's properly installed.

Why might you need to install PostgreSQL binaries, and how can you do this on Ubuntu or macOS?

PostgreSQL binaries are needed if you plan to run a PostgreSQL server locally. On macOS, you can install these binaries using the command `brew install postgresql`. On Ubuntu, additional system-level libraries like `postgresql-common` may need to be installed for successful setup.

What should you do if the installation of `psycopg2` fails?

If `psycopg2` fails to install, you might need to install PostgreSQL binaries or dependencies such as `postgresql-common` on Ubuntu or `postgresql` via Homebrew on macOS. Then you can try reinstalling `psycopg2`.

How can you confirm that PostgreSQL is running in a Docker container?

You can confirm that PostgreSQL is running by using the command `docker ps`. This shows a list of active Docker containers. If the PostgreSQL container is not running, you can start it with the command `docker start <container_name>`.

What is the purpose of using the `SQL Magic` extension in Jupyter Lab?

The `SQL Magic` extension allows users to write and execute SQL queries directly in Jupyter notebooks without needing to write Python code. It simplifies interaction with the PostgreSQL database through SQL commands.

Why might Jupyter Lab be preferred over other tools like SQL Workbench for practicing SQL?

Jupyter Lab offers a seamless experience by integrating SQL queries within notebooks, allowing users to write code and document results in one place. It's also useful for combining Python scripts with SQL queries in learning and development environments.

Outlines

00:00

🔗 Integrating Router Lab, Postgres, and Jupyter for SQL Practice

This paragraph explains how to integrate Router Lab and Postgres with an intuitive, interactive YouTube-based environment to practice SQL. The use of Jupyter Lab or Notebook is optional, though recommended over other tools like SQL Workbench or PSQL, which can be more time-consuming. To set up Jupyter Lab for SQL, certain Python libraries are necessary, and prerequisites include Python 3 and a configured Jupyter Lab environment. It also references a playlist that provides a step-by-step guide to setting up Jupyter Lab and Postgres on an Ubuntu VM in GCP, which can be adapted for other platforms like AWS.

05:00

💻 Setting Up Jupyter Lab and Installing Required Libraries

In this paragraph, the process of setting up the Jupyter Lab environment is detailed. The speaker explains the steps to install the necessary libraries like 'ipython-sql', which includes SQLAlchemy for SQL database connectivity. The virtual environment 'demogel' is activated, and additional library installations like 'psycopg2' are discussed for Postgres connectivity. The speaker provides commands and validates the installations of these libraries, ensuring that the environment can connect to Postgres. The setup for Jupyter Lab on Mac and Ubuntu is covered, with specific instructions for Postgres binaries and handling installation issues.

10:01

⚙️ Running Postgres in Docker and Validating Connectivity

This paragraph covers the validation of the Postgres database running within a Docker container. After ensuring that all required libraries are installed, the speaker verifies if the Postgres container is running. Once confirmed, they explain how to launch the Jupyter Lab environment, access it via a web console, and connect to the Postgres database. The setup process involves creating a new notebook, loading the SQL extension, and establishing environment variables. The speaker also demonstrates a query to validate the connectivity, ensuring that the SQL queries run successfully in the Jupyter Lab environment.

📝 SQL Workbench vs. Jupyter for SQL Learning

The speaker discusses the differences between using SQL Workbench and Jupyter-based environments for learning and practicing SQL. They explain that while Jupyter is their preferred tool due to its ability to create content and queries together, SQL Workbench is often used in project settings. The speaker recommends becoming familiar with both tools, as each has its own advantages. They emphasize the benefits of the Jupyter environment for learning Python and SQL together but encourage parallel learning of SQL Workbench for project productivity.

Keywords

💡Jupyter Lab

Jupyter Lab is an open-source web-based environment that allows users to work with Jupyter notebooks, code, and data. In the video, Jupyter Lab is recommended as an intuitive and interactive platform for practicing SQL queries. It integrates well with PostgreSQL, allowing users to run SQL commands directly within notebooks.

💡PostgreSQL

PostgreSQL is an open-source relational database management system (RDBMS) used to store and retrieve data. The video discusses setting up PostgreSQL on a virtual machine (VM) and connecting it to Jupyter Lab to practice SQL queries, using libraries like SQLAlchemy and psycopg2 for integration.

💡Docker

Docker is a platform that automates the deployment of applications inside lightweight containers. In the video, Docker is used to run PostgreSQL in a containerized environment. This allows users to practice SQL in an isolated and controlled environment that can easily be set up on different cloud platforms such as GCP, AWS, or on a local machine.

💡SQLAlchemy

SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that facilitates interaction with databases. In the context of the video, SQLAlchemy is installed as part of Jupyter Lab to enable smooth integration with PostgreSQL. It serves as a bridge between Python code and the SQL database but is not used directly for practicing SQL queries in this setup.

💡psycopg2

psycopg2 is a PostgreSQL adapter for Python, allowing Python programs to connect to PostgreSQL databases. The video emphasizes the importance of installing psycopg2 for establishing a connection between Jupyter Lab and PostgreSQL. Without this library, the user wouldn’t be able to run SQL queries from the Jupyter notebook.

💡Virtual Environment

A virtual environment in Python is an isolated environment where specific versions of libraries and dependencies can be installed without affecting the global system. In the video, the user activates a virtual environment (called 'demogel') to manage the installation of libraries like iPython-SQL and SQLAlchemy for use in Jupyter Lab.

💡Google Cloud Platform (GCP)

Google Cloud Platform (GCP) is a suite of cloud computing services. In the video, GCP is used to provision a virtual machine that runs Jupyter Lab and PostgreSQL via Docker. The speaker also mentions that while GCP is used in this demo, users can follow the same instructions on other cloud platforms like AWS.

💡ipython-sql

ipython-sql is a Python library that allows SQL queries to be run within Jupyter notebooks without needing to write Python code. The video outlines how to install ipython-sql as part of the Jupyter Lab environment to connect to PostgreSQL and execute SQL commands directly from within the notebook.

💡SQL Workbench

SQL Workbench is a database client that allows users to manage and query SQL databases. The video contrasts SQL Workbench with Jupyter Lab as another option for practicing SQL. While Jupyter Lab offers an integrated learning environment, SQL Workbench is noted for its widespread use in real-world projects.

💡Object Relational Mapping (ORM)

Object Relational Mapping (ORM) is a programming technique that converts data between incompatible systems using object-oriented programming languages. In the video, SQLAlchemy is described as an ORM, though the video does not focus on ORM for practicing SQL but mentions it as a useful tool for interacting with databases.

Highlights

Integration of Jupyter Lab and Postgres enables a seamless environment for practicing SQL in an interactive and intuitive way.

Using Jupyter Lab or SQL Workbench allows you to practice SQL with either Postgres or other databases, while leveraging the Jupyter environment is optional.

Setting up Jupyter Lab environment involves installing Python 3 and a virtual environment to work with the necessary libraries for SQL practice.

The iPython-SQL library needs to be installed in the Jupyter Lab environment to connect notebooks with Postgres for SQL queries.

SQLAlchemy and Psycopg2 libraries are essential for connecting the Jupyter Lab environment to a Postgres database.

The virtual environment (demogel) is activated to install the required SQL libraries like iPython-SQL and SQLAlchemy.

Validating SQLAlchemy installation is possible by running 'pip list' to ensure all necessary libraries are in place.

On Ubuntu, after installing the Python libraries, the OS-level binaries for Postgres must be set up for successful connection.

For Mac users, Postgres can be installed using binaries, but for Ubuntu users, installing Postgres binaries requires specific commands.

To test connectivity, the Postgres database must be running within a Docker container, which can be checked using the 'docker ps' command.

Once Jupyter Lab is running, you can validate the connection to the Postgres database by executing basic SQL queries in a notebook.

Jupyter-based environment is demonstrated to successfully run SQL queries without writing Python code.

Practicing SQL using Jupyter Lab offers the advantage of combining query writing and content creation in one interface.

Though Jupyter Lab is useful for learning, being familiar with SQL Workbench is essential for real-world project work.

Jupyter-based environments offer a smooth learning experience for both SQL and Python, making it a preferred tool for comprehensive education.

Transcripts

let us understand how we can integrate

router lab and postgres so that we can

leverage intuitive and interactive

youtube based environment to practice

sql

if you can take care of this integration

you should be able to use

jupyter environment as i'm demonstrating

here to practice sql

using jupyter lab or notebook is

optional you can leverage the sql

workbench or ps equal to practice

however using p sequel is a bit tricky

and can take a considerable amount of

time

i will recommend ids such as sql

workbench or environments like jupiter

lab to actually practice

sql with either porsche sql or any other

database

we need additional libraries to be set

up as part of jupyter environment for

integrating notebooks with postgres to

write queries

without writing any code before getting

into setup let us understand the

prequisites

you should have python 3 installed also

you should have set up jupyter lab

environment by now

if not you can follow our playlist it

will take care of

providing you step by step instructions

to set up jupiter lab on ubuntu vm on

gcp using

docker you can review this playlist it

covers

provisioning vm from gcp setup docker on

top of it

set up jupiter lab and also set up

postgres database

even though gcp is used for the

demonstration

if you have vm from aws or

seo you should be able to follow these

instructions and set up lab for yourself

including jupiter lab as well as

postgres

once jupiter lab is set up we need to

install the following to leverage

jupiter-based notebooks to practice sql

you need to install a library called as

ipython hyphen sql

using pip within the virtual environment

used to set up jupiter lab

so in our lab environment if you follow

the instructions to set up jupiter lab

we have created the virtual machine

we can leverage that virtual machine and

we should be able to set up ipython

hyphen sql

for that let's activate the virtual

environment

the virtual environment is nothing but

demogel so i can say source

demogel bin

activate it will take care of activating

the virtual environment for us you can

see the virtual environment

here now you should be able to set up

this ipython hyphen sql will come back

to it in a moment

you also need to install sql alchemy to

facilitate the connectivity between dr

notebooks

and the databases however it will be

installed along with ipython

hyphen sql you can run p placed to

validate whether sql in

alchemy is installed or not also we need

to install cyclops g2 to connect to

postgres database

if you do not have psycob g2 then you

will not be able to connect to postgres

database using sql alchemy

sql alchemy is a wrapper which can be

leveraged

using python to connect to database and

start

developing something called as urm

classes volume stands for

object relational mapping don't worry

too much about those things at this time

also will not be working on sql alchemy

while practicing sql however just to

make sure that our jupyter environment

can connect to database we need to take

care of setting up this ipython hyphen

sql which include sql alchemy

and also this library called as psycopd2

without these libraries you will not be

able to

execute sql queries directly without

writing python code

if you are setting up jupiter lab on top

of your mac you have to install

postgresql on your mac

you can run this command it will take

care of installing postgresql binaries

it will not start the postwar sql server

however the binaries will be installed

using jupyter lab along with this

postgresql binaries you should be able

to connect

to postgres that is running on docker

using this

library called as cyclops g2 via your

triple based environment

you will understand what i am talking

about in a moment you need to run these

four commands in this order on mac

if you are using ubuntu after running

this pip install command to install

ipython sql and validating

uh whether sql alchemy is installed or

not by running this pip list

before running this pip install cyclops

g2 you have to take care of

running these commands to install posts

with sql related binaries

most likely postgresql common library

should be good enough

if it failed then we have to install

postgresql

complete libraries let's run these

comments and we will see

so first i'm actually running this

command for this

you need to ensure that you are

connected to your

ubuntu bsd environment i have connected

to it

using

the tcp i just have to expand this

once it is opened then i can take it

further now it is launched

first thing we need to do is we have to

activate the virtual environment using

which we set up

jupiter lab environment we can take care

of it by running source

demogel and then bin activate like this

it will take care of activating the

virtual environment

in which jupiter lab related libraries

are installed

after activating this the next step is

to install

ipython hyphen sql let me actually copy

paste this command

this is what is supposed to be run now i

can

go to that page then paste it

you can see that it is installing all

the libraries related to ipython hyphen

sql it includes sql alchemy also

you can validate by running pip list you

should see

sql alchemy here you can see sql alchemy

is also installed

after this if you try to run this it

will fail

if you are using mac you can run this

first and then run this it should work

if you are using ubuntu based virtual

machine or

even ubuntu based server if you try to

run this

it will fail you can see that it is

failing

we need to install the uh os level

uh binaries related to postgres that's

why it is failing

even if you try to install psycopg2

binary

then also it will fail i just have to

say binary like this

you can see that oh it is successfully

installed

okay that's good so we don't need to

install the

uh postwar sql uh on this server

we can directly say cycopty to binary

and it should work

i thought we have to run these commands

if at all psycho due to binary

is the failed then you might have to

install these things

let me validate whether

we should be able to connect to postgres

running as part of the docker container

for that

we need to ensure that the postgas as

part of the docker container is running

we can actually run docker ps command

first

so all the required libraries are

installed as part of the jupyter lab

environment to connect to postgres

database using jupyter notebook

now we are going to validate for that

first i'm validating whether the

postgres database is running as part of

the docker container or not

i can say docker ps like this you can

see that nothing is running

we can run wps iphone here to ensure

that

the docker container which is running

process

is there using that we should be able to

run this

docker start then the container name

which is

nothing but it underscore pg now the

container is started you can run docker

ps command

and you can see that the docker

container is running process

is up now once it is up

you should be able to launch the jupiter

lab environment

using this command let me

get into the web console

let me paste this and hit enter now the

lab should come up now we can actually

take the ip address from the google

console if you set up on your mac

the jupiter will open automatically you

don't need to do this

process of launching the browser and

copying the ip address it should work

seamlessly as it is running on remote

server

it will not be able to open the jupyter

based

url using browser directly for that

reason we have to

paste this ip address and then colon

8888

is the port number now we are actually

connecting to the

jupiter-based environment using the

browser

and jupiter environment is actually

running on the ubuntu

vm which is provisioned from gcp

now we should be able to follow the

instructions to actually validate

first we have to load sql magic

for that we have to create a new

notebook so in this case i am saying

file new notebook

let me rename this i can right click on

this and then say

rename validate

postgres connectivity this is the

notebook name

we can actually load the sql extension

it is loaded successfully now we should

be able to create this environment

variable

however this information has to be

accurate already we have set up a

detailed database as part of the

process that is running as part of the

docker container using these credentials

you can follow the earlier videos and

you should be able to understand

now let me create this environment

variable once it is created

we should be able to validate by saying

percentage sql

select current underscore date like this

i think we don't need to specify the

circular brackets now you can see the

output

which means using different based

environment we are able to connect to

postgres database and run queries

without writing any python code now you

should be able to practice

all my content related to mastering

sequel using phosphorus

using jupiter based environment

keep in mind that setting up

jupiter-based environment is optional

either you can use the sql workbench or

jupiter-based environment to connect to

the database and

run the sql queries whatever is

comfortable with you you can follow that

approach

i use jupyter-based environment because

it actually

facilitates me to create the content and

queries together

and hence i'm using triple based

environment as part of the projects we

typically use the school workbench

for learning purpose you can leverage

the triple based environment if you set

up in this manner

or you can also use the school workbench

whatever is convenient to you

but i will be publishing content using

based environment

i would highly recommend you to get

comfortable with sql workbench as well

in parallel

so that you can use it a lot more

productively as part of the projects

so learning using jupiter based

environments have its own advantages

however you should be comfortable with

sql workbench also

the reason why i am using duplicate

based environment is even python related

stuff will be powered

using based environment jupiter-based

environment will provide you

seamless learning experience that's why

i am using based environment and i have

demonstrated how to set up the

jupiter-based environment not only to

practice python but also to connect to

the

databases and practice so feel free to

use this approach

or sql workbench either is fine to

actually learn

that being said if you have any issues

feel free to reach out to us we will be

there to support you