Setup Postgres Database - Jupyter Lab and Postgresql
Summary
TLDRThis 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
🔗 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.
💻 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.
⚙️ 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.
Mindmap
Keywords
💡Jupyter Lab
💡PostgreSQL
💡Docker
💡SQLAlchemy
💡psycopg2
💡Virtual Environment
💡Google Cloud Platform (GCP)
💡ipython-sql
💡SQL Workbench
💡Object Relational Mapping (ORM)
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
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