Databricks Certified Associate Spark Developer preparation toolkit to setup single node Standalone Spark Cluster along with material in the form of Jupyter Notebooks.

Overview

Databricks Certification Spark

Databricks Certified Associate Spark Developer preparation toolkit to setup single node Standalone Spark Cluster along with material in the form of Jupyter Notebooks. This is extensively used as part of our Udemy courses as well as our upcoming guided programs related to Databricks Certified Associate Spark Developer.

Udemy Courses

This GitHub repository can be leveraged to setup Single Node Spark Cluster using Standalone along with Jupyterlab to prepare for the Databricks Certified Associate Developer - Apache Spark. They are available at a max of $25 and we provide $10 coupons 2 times every month. Also, these courses are part of Udemy for business.

Technologies Covered

As part of this custom image built by us, we have included the following as a preparation toolkit for Databricks Certified Associate Developer - Apache Spark.

  • Apache Spark 3 using Spark Stand Alone Cluster
  • Jupyter based environment along with material for the preparation towards Databricks Certified Associate Developer - Apache Spark
  • If you set up the environment as instructed as part of our courses then you will also get the data sets as well as material in the form of Jupyter Notebooks.

For all video lectures, up-to-date material, live support - feel free to sign up for our Udemy courses or our upcoming guided programs.

Setup Spark Lab for Databricks Certified Associate Developer - Apache Spark

Pre-requisites

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.

Configure Docker Desktop

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.

Setup Environment

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

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

Pull the Image

Spark image is of moderate size. 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 itversity/itvspark3.
  • You can validate if the image is successfully pulled or not by running docker images command.

Start Environment

Here are the steps to start the environment.

  • Run docker-compose up -d --build itvspark3.
  • It will set up single node Stand Alone Spark Cluster.
  • You can run docker-compose logs -f itvspark3 to review the progress. It will take some time to complete the setup process.
  • You can stop the environment using docker-compose stop command.

Access the Lab

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 itvspark3 \
  sh -c "cat .local/share/jupyter/runtime/jpserver-*.json"

Access Databricks Certified Associate Developer - Apache Spark Material

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

Predict the output which should give a fair idea about the chances of admission for a student for a particular university

Predict the output which should give a fair idea about the chances of admission for a student for a particular university.

ArvindSandhu 1 Jan 11, 2022
Winning solution for the Galaxy Challenge on Kaggle

Winning solution for the Galaxy Challenge on Kaggle

Sander Dieleman 483 Jan 02, 2023
Python 3.6+ toolbox for submitting jobs to Slurm

Submit it! What is submitit? Submitit is a lightweight tool for submitting Python functions for computation within a Slurm cluster. It basically wraps

Facebook Incubator 768 Jan 03, 2023
End to End toy example of MLOps

churn_model MLOps Toy Example End to End You might find below links useful Connect VSCode to Git MLFlow Port Heroku App Project Organization ├── LICEN

Ashish Tele 6 Feb 06, 2022
Napari sklearn decomposition

napari-sklearn-decomposition A simple plugin to use with napari This napari plug

1 Sep 01, 2022
🌊 River is a Python library for online machine learning.

River is a Python library for online machine learning. It is the result of a merger between creme and scikit-multiflow. River's ambition is to be the go-to library for doing machine learning on strea

OnlineML 4k Jan 03, 2023
Greykite: A flexible, intuitive and fast forecasting library

The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.

LinkedIn 1.7k Jan 04, 2023
K-means clustering is a method used for clustering analysis, especially in data mining and statistics.

K Means Algorithm What is K Means This algorithm is an iterative algorithm that partitions the dataset according to their features into K number of pr

1 Nov 01, 2021
Dive into Machine Learning

Dive into Machine Learning Hi there! You might find this guide helpful if: You know Python or you're learning it 🐍 You're new to Machine Learning You

Michael Floering 11.1k Jan 03, 2023
MLReef is an open source ML-Ops platform that helps you collaborate, reproduce and share your Machine Learning work with thousands of other users.

The collaboration platform for Machine Learning MLReef is an open source ML-Ops platform that helps you collaborate, reproduce and share your Machine

MLReef 1.4k Dec 27, 2022
Meerkat provides fast and flexible data structures for working with complex machine learning datasets.

Meerkat makes it easier for ML practitioners to interact with high-dimensional, multi-modal data. It provides simple abstractions for data inspection, model evaluation and model training supported by

Robustness Gym 115 Dec 12, 2022
Machine-learning-dell - Repositório com as atividades desenvolvidas no curso de Machine Learning

📚 Descrição Neste curso da Dell aprofundamos nossos conhecimentos em Machine Learning. 🖥️ Aulas (Em curso) 1.1 - Python aplicado a Data Science 1.2

Claudia dos Anjos 1 Jan 05, 2022
A collection of neat and practical data science and machine learning projects

Data Science A collection of neat and practical data science and machine learning projects Explore the docs » Report Bug · Request Feature Table of Co

Will Fong 2 Dec 10, 2021
Visualize classified time series data with interactive Sankey plots in Google Earth Engine

sankee Visualize changes in classified time series data with interactive Sankey plots in Google Earth Engine Contents Description Installation Using P

Aaron Zuspan 76 Dec 15, 2022
Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

Call of Duty World League: Search & Destroy Outcome Predictions Growing up as an avid Call of Duty player, I was always curious about what factors led

Brett Vogelsang 2 Jan 18, 2022
Confidence intervals for scikit-learn forest algorithms

forest-confidence-interval: Confidence intervals for Forest algorithms Forest algorithms are powerful ensemble methods for classification and regressi

272 Dec 01, 2022
Markov bot - A Writing bot based on Markov Chain for Data Structure Lab

基于马尔可夫链的写作机器人 前端 用html/css完成 Demo展示(已给出文本的相应展示) 用户提供相关的语料库后训练的成果 后端 要完成的几个接口 解析文

DysprosiumDy 9 May 05, 2022
PySpark ML Bank Churn Prediction

PySpark-Bank-Churn Surname: corresponds to the record (row) number and has no effect on the output. CreditScore: contains random values and has no eff

kemalgunay 2 Nov 11, 2021
Iris species predictor app is used to classify iris species created using python's scikit-learn, fastapi, numpy and joblib packages.

Iris Species Predictor Iris species predictor app is used to classify iris species using their sepal length, sepal width, petal length and petal width

Siva Prakash 5 Apr 05, 2022