Here are the pre-requisites to setup the lab.

• Memory: 8 GB RAM
• CPU: At least Quadcore
• If you are using Windows or Mac, make sure to setup Docker Desktop.
• If your system does not meet the requirement, you need to setup environment using AWS Cloud9.
• Even if you have 16 GB RAM and the Quadcore CPU, the system might slow down over the usage. You can always using AWS Cloud9 as fallback option.

If you are using Windows or Mac, you need to change the settings to use as much resources as possible.

• Go to Docker Desktop preferences.
• Change memory to 4 GB.
• Change CPUs to the maximum number.

Here are the steps one need to follow to setup the lab.

• Clone the repository by running git clone https://github.com/itversity/data-engineering-spark.

Here are the steps to start the environment.

• Run docker-compose up -d --build itvdflab.
• It will set up environment with Jupyter Lab and Postgresql. You can validate by running docker-compose ps.
• You can run docker-compose logs -f to review the progress.
• You can stop the environment using docker-compose stop command.

Here are the steps to access the lab.

• Make sure both Postgres and Jupyter Lab containers are up and running by using docker-compose ps
• Get the token from the Jupyter Lab container using below command.

docker-compose exec itvdflab \
  sh -c "cat .local/share/jupyter/runtime/jpserver-*.json"

• Use the token to login using http://localhost:8888/lab

Once you login, you should be able to go through the first major module under itversity-material to access the content.

Here are the pre-requisites to setup the lab.

• Memory: 16 GB RAM
• CPU: At least Quadcore
• If you are using Windows or Mac, make sure to setup Docker Desktop.
• If your system does not meet the requirement, you need to setup environment using AWS Cloud9.
• Even if you have 16 GB RAM and the Quadcore CPU, the system might slow down once we start the docker containers due to the requirements of the resources. You can always use AWS Cloud9 as fallback option.
• In my case, I will be demonstrating using Cloud9.

If you are using Windows or Mac, you need to change the settings to use as much resources as possible.

• Go to Docker Desktop preferences.
• Change memory to 12 GB.
• Change CPUs to the maximum number.

Here are the steps one need to follow to setup the lab.

• Clone the repository by running git clone https://github.com/itversity/data-engineering-spark.

Hadoop and Spark require more horse power. Also, there is no need to keep containers related to Python and SQL up and running while going through Hadoop and Spark material.

• We can tear down containers related to Python and SQL by running below command.

docker-compose rm -v itvdflab
docker-compose rm -v pg.itversity.com

Hadoop and Spark image is quite big. It is close to 1.5 GB.

• Make sure to pull it before running docker-compose command to setup the lab.
• You can pull the image using docker pull dgadiraju/itvdelab.
• You can validate if the image is successfully pulled or not by running docker images command.

Here are the steps to start the environment.

• Run docker-compose up -d --build itvdelab.
• It will set up single node Hadoop, Hive and Spark Environment along with metastore for hive.
• You can run docker-compose logs -f itvdelab to review the progress. It will take some time to complete the setup process.
• You can stop the environment using docker-compose stop command.

Here are the steps to access the lab.

• Make sure both Postgres and Jupyter Lab containers are up and running by using docker-compose ps
• Get the token from the Jupyter Lab container using below command.

docker-compose exec itvdelab \
  sh -c "cat .local/share/jupyter/runtime/jpserver-*.json"

• Use the token to login using http://localhost:8888/lab

Once you login, you should be able to go through the third major module under itversity-material to access the content.