we have created three ready to go containers with Pyspark and Python. Connect to the Jupyter notebook server and run sample code to verify the environment.
The pgAdmin login credentials are in the Docker stack file. This short and easy article to help you do it. The script then performs a simple Spark SQL query, calculating the total quantity of each type of bakery item sold, sorted in descending order.
The server name, postgres, is the name of the PostgreSQL Docker container.
This post will demonstrate the creation of a containerized data analytics environment using Jupyter Docker Stacks. For this demonstration, we will use the Transactions from a bakery dataset from Kaggle. Setting up a Docker container on your local machine is pretty simple. Also, the pgadmin doesnt seem to persist the bakery database as mentioned in your article. For this post, I am using the current stable version of Docker Desktop Community version for macOS, as of March 2020. This can be done by passing the environment variable JUPYTER_ENABLE_LAB=yes at container startup, we copy the full url of the docker and enter to our browser and wuala. This is the domain name the Jupyter container will use to communicate with the PostgreSQL container. http://www.daniellesplace.com/html/under_the_sea.html#fishpuppet, http://www.curbly.com/users/indymogul/posts/1699-how-to-make-a-muppet-style-puppet, http://www.dosmallthingswithlove.com/2012/09/handmade-felt-finger-puppets.html, consider supporting our work with a contribution to wikiHow. The CPU and memory output show spikes, but both appear to be within acceptable ranges. Simply we run the following command in the terminal: you will see the following in the Docker container, and you click on on it and you can see the link, we copy the token number for example http://127.0.0.1:8888/?token=823cd22311d5315dd6c266c4c16211e83158cb9ef052c4f7 then navigate to http://localhost:8888 in your browser and you will see the following screen, We can create new notebook, let us play, let us create a dataframe in Spark and then parallize numbers and print the version of spark. This should result in the following output, if successful. Excuse docker run command to pull the image from docker pull. Assuming your development machine is robust, it is easy to allocate and deallocate additional compute resources to Docker if required. You should observe log output similar to the following. 2) Pulling A PySpark-Notebook Docker Image, [Assuming you have already successfully installed Docker Desktop].
The table should contain 21,293 rows, each with 5 columns of data. Change), You are commenting using your Facebook account.
Notebooks can also be viewed using nbviewer, an open-source project under Project Jupyter. [Assuming you already have the PySpark-Notebook container is running in Docker Desktop). For example, we can execute the htop command from a Jupyter terminal, sorting processes by CPU % usage. We will choose Swarm for this demonstration. I am reaching out to see if you have any advice for a problem that I am having with persisting the postgresql server and the docker stack. Installed notebook extensions can be enabled, either by using built-in Jupyter commands, or more conveniently by using the jupyter_nbextensions_configurator server extension. Fill in your details below or click an icon to log in: You are commenting using your WordPress.com account. After performing operations and transformations on the data, the data is persisted to a datastore, such as a file or a database, or conveyed to another system for further processing.
In the same directory create a requirements.txt file and add the packages your notebooks depend on.
Then, place the character facedown, and position the end of a plastic straw in the middle of the paper. To confirm the stack deployed successfully, run the following docker command. The first notebooks, 04_notebook.ipynb, demonstrates typical PySpark functions, such as loading data from a CSV file and from the PostgreSQL database, performing basic data analysis with Spark SQL including the use of PySpark user-defined functions (UDF), graphing the data using BokehJS, and finally, saving data back to the database, as well as to the fast and efficient Apache Parquet file format. Although certainly not big data, the dataset is large enough to test out the Spark Jupyter Docker Stack functionality. The stack uses an alternative Docker image, garystafford/all-spark-notebook-nbext:latest, This Dockerfile, which builds it, uses the jupyter/all-spark-notebook:latest image as a base image. This is typically how Spark is used in a Production for performing analysis on large datasets, often on a regular schedule, using tools such as Apache Airflow. This image is not licensed under the Creative Commons license applied to text content and some other images posted to the wikiHow website. How To Make Animal Sock Puppets Ana Diy Crafts Play | Download, How To Make A Unicorn Paper Hand Puppet Play | Download, How To Make A Live Hand Puppet Part 9 Building 101 Play | Download, How To Make A Cat Sock Puppet Ana Diy Crafts Play | Download, How To Make Hand Puppets Tutorial Part 2 Play | Download, Diy Puppets Foam And Sock Puppet Ana Crafts Play | Download, How To Draw Bonnie Hand Puppet Fnaf Sister Location Play | Download, How To Make A Paper Bunny Hand Puppet Easter Craft For Kids Play | Download, How To Make Puppet Hands Building 101 Play | Download. Here, the four CPUs at the top left of the htop window are the CPUs assigned to Docker.
Our PostgreSQL data will also be persisted through a bind-mounted directory.
A popular option is jupyter-contrib-nbextensions. We get insight into the way Spark is using multiple CPUs, as well as other performance metrics, such as memory and swap. navigate to http://localhost:8888 in your browser with the token and you will see the following screen. Note this post uses the v2branch. We should observe the individual performance of each process running in the Jupyter container. Note: If your notebooks require packages that are not pre-installed on this image they need to pip-install them explicitly.
Adminer should be available on localhost port 8080. The PWD environment variable assumes you are working on Linux or macOS (CD on Windows). Retrieve the complete URL, for example, http://127.0.0.1:8888/?token=f78cbe, to access the Jupyter web-based user interface. Below, we see five new extension icons that have been added to the menu bar of 04_notebook.ipynb. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); This site uses Akismet to reduce spam. We will override that user with our own local hosts user account, as shown on line 21 of the Docker stack file, NB_USER: $USER. wikiHow, Inc. is the copyright holder of this image under U.S. and international copyright laws. Some of my favorite extensions include spellchecker, Codefolding, Code pretty, ExecutionTime, Table of Contents, and Toggle all line numbers. The notebook expects to find the two environment variables, which it uses to authenticate with Plotly. To review, open the file in an editor that reveals hidden Unicode characters. Since Jupyter would need to be restarted after nbextensionsis deployed, it cannot be done from within a running jupyter/all-spark-notebook container. Government Engineering Colleges In Thrissur, The Jupyter containers working directory is set on line 15 of the stack.yml file, working_dir: /home/$USER/work. The chart uses SciPy and NumPy to construct a linear fit (regression) and plot a line of best fit for the bakery data and overlaying the vertical bars. Change), You are commenting using your Twitter account. The following technologies are featured prominently in this post. This repository has three containers ready to go: select just one of the following commands: Container 1:Jupyter Notebook with Apache Spark with Python : This container can be used when you wants to work in Jupyter Notebook with Pyspark ,the image is stored at the Docker Hub here. Files from our GitHub project will be shared with the Jupyter application container through a bind-mounted directory. Kaggle is an excellent open-source resource for datasets used for big-data and ML projects. To enable quick and easy access to Jupyter Notebooks, Project Jupyter has created Jupyter Docker Stacks. Be careful not to allocate excessive resources to Docker, starving your host machine of available compute resources for other applications. The real power of the Jupyter Docker Stacks containers is Jupyter Notebooks. Note that the .env file is part of the .gitignore file and will not be committed to back to git, potentially compromising the credentials. Its running so I can play! To start, create the$HOME/data/postgresdirectory to store PostgreSQL data files. We should have discovered that coffee is the most commonly sold bakery item with 5,471 sales, followed by bread with 3,325 sales. Thanks to Rackspace hosting, the nbviewer instance is a free service. Similar to Apache Zeppelin and the newly open-sourced Netflixs Polynote, Jupyter Notebooks enables data-driven, interactive, and collaborative data analytics. Although limited to PostgreSQL, the user interface and administrative capabilities of pgAdmin is superior to Adminer, in my opinion. The Jupyter Docker container exposes Sparks monitoring and instrumentation web user interface. Perhaps this is something youre familiar with and will offer feedback. Modify and run the following two commands from a Jupyter terminal to create the .env file and set you Plotly username and API key credentials. Typically, you would submit the Spark job using the spark-submit command. There are many ways to extend the Jupyter Docker Stacks. Run the script directly from a Jupyter terminal using the following command. The particular environment will be suited for learning and developing applications for Apache Spark using the Python, Scala, and R programming languages. Government Engineering Colleges In Thrissur. To begin, we will run a SQL script, written in Python, to create our database schema and some test data in a new database table. Running Spark on a single node within the Jupyter Docker container on your local development system is not a substitute for a true Spark cluster, Production-grade, multi-node Spark clusters running on bare metal or robust virtualized hardware, and managed with Apache Hadoop YARN, Apache Mesos, or Kubernetes. The jupyter/all-spark-notebook Docker image is large, approximately 5 GB. Is there a command you recommend or a concept you suggest I study? Container 3: JupyterLab Apache Spark with Python and Elyra : This container can be used when you wants to work in JupyterLab with Pyspark and Elyra, This is helpful when you wants to create a Pipeline with Apache Airflow or Kubernet Flow. You will not realistically replace the need to process big data and execute jobs requiring complex calculations on a Production-grade, multi-node Spark cluster. Open a terminal to the location where youve created the Dockerfile and requirements.txt. Learn how your comment data is processed. https://hub.docker.com/r/jupyter/pyspark-notebook/, https://github.com/jupyter/docker-stacks/tree/master/pyspark-notebook, https://github.com/jupyter/docker-stacks/tree/master/pyspark-notebook#notebook-options, https://charlesreid1.com/w/index.php?title=Docker/Jupyter_PySpark&oldid=21429, Creative Commons Attribution-NonCommercial 4.0 License. Use a Jupyter terminal to run the following command. The HOMEenvironment variable assumes you are working on Linux or macOS and is equivalent to HOMEPATH on Windows. This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. The Spark Python template image serves as a base image to build your own Python application to run on a Spark cluster. The dataset is available as a single CSV-format file. Run Docker run pull image container, Jupyter notebook server PySpark sample code . Learn more about bidirectional Unicode characters. Rukhsati Se Pehle Humbistari, O seu endereo de email no ser publicado. The data consists of 9,531 customer transactions for 21,294 bakery items between 2016-10-30 and 2017-04-09 (gist). According to Docker, the cluster management and orchestration features embedded in the Docker Engine are built usingswarmkit. Nemo Dragonfly Canada, Installing Bluetooth 5 dongles on Ubuntu LTS 18 and 20 and other Linux kernels before 5.8, SSH into EC2 instance on different Operating System, Sharing a Project: Team accounts on Simon Says, Easy Local PySpark Environment Setup Using Docker, PySpark UDFs, Spark-NLP, and scrapping HTML files on spark clustersa complete ETL process for, 2020/09/13 add a docker command with volumes function, docker run -it --rm -p 8888:8888 jupyter/pyspark-notebook, http://localhost:8888/?token=e144d004f6652ae6406a78adf894621e62fdeb1fc57d02e8, https://spark.apache.org/docs/latest/running-on-mesos.html, Successfully running Docker in your machine, Basic knowledge for Jupyter notebook and Docker. The Transactions from a bakery dataset contains 21,294 rows with 4 columns of data. It was installed in the container by the bootstrap script. First, Gary, excellent write up (as usual). From a Jupyter terminal window, use the following command to run the script. Licensed under the Creative Commons Attribution-NonCommercial 4.0 License. View this notebook, here, using nbviewer. The stacks are ready-to-run Docker images containing Jupyter applications, along with accompanying technologies. To view or add a comment, sign in, https://www.docker.com/products/docker-desktop, https://hub.docker.com/r/jupyter/pyspark-notebook. PyCharm has built-in language support for Jupyter Notebooks, as shown below. Use the following command to clone the project.
According to the Jupyter Project, the notebook extends the console-based approach to interactive computing in a qualitatively new direction, providing a web-based application suitable for capturing the whole computation process, including developing, documenting, and executing code, as well as communicating the results. All source code for this post can be found on GitHub. In a couple of minutes, you can whip up a tree, a rock, some flowers, or whatever might be found in your puppet show setting. Below, we see the stats from the Docker stacks three containers showing little or no activity. Learn on the go with our new app. # scheduling relates to the user-scheduler pods and user-placeholder pods. Currently, the Jupyter Docker Stacks focus on a variety of specializations, including the r-notebook, scipy-notebook, tensorflow-notebook, datascience-notebook, pyspark-notebook, and the subject of this post, the all-spark-notebook. AWS Senior Solutions Architect | 8x AWS Certified Pro | DevOps | Data/ML | Serverless | Polyglot Developer | Former ThoughtWorks and Accenture, Insights on Software Development, Cloud, DevOps, Data Analytics, and More, $HOME/data/postgres:/var/lib/postgresql/data, 'host=postgres port=5432 dbname=bakery user=postgres password=postgres1234', 'SELECT COUNT(*) FROM public.transactions;', Click to share on Twitter (Opens in new window), Click to share on LinkedIn (Opens in new window), Click to share on Facebook (Opens in new window), Click to share on Reddit (Opens in new window), Click to share on Tumblr (Opens in new window), Click to email a link to a friend (Opens in new window), Getting Started with Apache Zeppelin on Amazon EMR, using AWS Glue, RDS, and S3: Part2, Getting Started with Data Analysis on AWS using AWS Glue, Amazon Athena, and QuickSight: Part1, https://programmaticponderings.files.wordpress.com/2019/12/jupyter-docker-v4.mp3, Learn more about bidirectional Unicode characters, Permission Denied When Using Docker Command to Retrieve Jupyter Token for Pyspark Demo Docker Questions, Developing Spring Boot Applications for Querying Data Lakes on AWS using AmazonAthena, Building and Deploying Cloud-Native Quarkus-based Java Applications toKubernetes, Monolith to Microservices: Refactoring RelationalDatabases, End-to-End Data Discovery, Observability, and Governance on AWS with LinkedIns Open-sourceDataHub, Data Preparation on AWS: Comparing Available ELT Options to Cleanse and NormalizeData, Install Latest Node.js and npm in a Docker Container, Running PySpark Applications on Amazon EMR: Methods for Interacting with PySpark on Amazon Elastic MapReduce, Spring Integration with Eclipse Using Maven, DevOps for DataOps: Building a CI/CD Pipeline for Apache AirflowDAGs, BLE and GATT for IoT: Getting Started with Bluetooth Low Energy and the Generic Attribute Profile Specification for IoT, python3 -m pip install user upgrade pip, python3 -m pip install -r requirements.txt upgrade, sudo cp log4j.properties /usr/local/spark/conf/log4j.properties, && jupyter contrib nbextension install system \, && pip install jupyter_nbextensions_configurator \, && jupyter nbextensions_configurator enable system \, Unfortunately, a quick Zoom meeting and glance at your complex, distributed, legacy platform, based on a diagram in, Performance considerations for loading data into BigQuery. Great article that demonstrates the need to plan how you. The idea for these tut came to me by This image may not be used by other entities without the express written consent of wikiHow, Inc.\n, \n"}, {"smallUrl":"https:\/\/www.wikihow.com\/images\/thumb\/3\/32\/Make-Puppets-Step-25-Version-2.jpg\/v4-460px-Make-Puppets-Step-25-Version-2.jpg","bigUrl":"\/images\/thumb\/3\/32\/Make-Puppets-Step-25-Version-2.jpg\/aid198046-v4-728px-Make-Puppets-Step-25-Version-2.jpg","smallWidth":460,"smallHeight":345,"bigWidth":"728","bigHeight":"546","licensing":", \u00a9 2020 wikiHow, Inc. All rights reserved. At the time of this post, LinkedIn, alone, had approximately 3,500 listings for jobs that reference the use of Apache Spark, just in the United States. The image is stored at the Docker Hub here, copy the command and paste in the terminal and press enter, Step 3. The stacks include a wide variety of well-known packages to extend their functionality, such as scikit-learn, pandas, Matplotlib,Bokeh, NumPy, and Facets. For brevity, I chose not to include pgAdmin in this post. We could use this same stack to learn and perform machine learning using Scala and R. Extending the stacks capabilities is as simple as swapping out this Jupyter image for another, with a different set of tools, as well as adding additional containers to the stack, such as MySQL, MongoDB, RabbitMQ, Apache Kafka, and Apache Cassandra. The password credentials, shown below, are located in the stack.yml file. Source code samples are displayed as GitHubGists, which may not display correctly on some mobile and social media browsers. Every time I log out of the computer the docker stack seems to go idle, and when I return to the notebook all the nbextension configurations are gone and so are the additional pip installs I entered in Jupiter terminal. As mentioned in the Introduction, the Jupyter Docker Stacks come ready-to-run, with a wide variety of Python packages to extend their functionality. The Docker stack consists of a new overlay network, jupyter_demo-net, and three containers. 2. (LogOut/ Simply download docker from the docker website and run it. Several of those options are used on lines 1722 of the Docker stack file (gist).
Below, we see the output from the spark-submit command. TensorFlow. The script will install the required Python packages using pip, the Python package installer, and a requirement.txt file. It is my favorite tool for the administration of PostgreSQL. Below we see several notebook cells demonstrating these features. We can also review the task composition and timing of each event occurring as part of the stages of the Spark job. There is little question, big dataanalytics, data science, artificial intelligence (AI), and machine learning (ML), a subcategory of AI, have all experienced a tremendous surge in popularity over the last few years. In this demonstration, we will explore the capabilities of the Spark Jupyter Docker Stack to provide an effective data analytics development environment. Can pencils be used as an alternative to the handle? You can leave a response, or trackback from your own site. With Spark, you are load data from one or more data sources. The below Python script, 03_load_sql.py, will execute a set of SQL statements contained in a SQL file, bakery.sql, against the PostgreSQL container instance. (LogOut/ You can dress your puppets in anything you like. First, I couldnt get this to work on Windows, even running Cygwin. We will use the hosts USER environment variable value (equivalent to USERNAME on Windows). We will focus on Python and Spark, using PySpark. To access the Jupyter Notebook application, you need to obtain the Jupyter URL and access token. According to their website, thejupyter-contrib-nbextensions package contains a collection of community-contributed unofficial extensions that add functionality to the Jupyter notebook. Step 1.
The stack consists of a Jupyter All-Spark-Notebook, PostgreSQL (Alpine Linux version 12), and Adminer container. This imageincludes Python, R, and Scala support for Apache Spark, using Apache Toree. The PySpark development environment is up and running. Current versions of Docker include both a Kubernetes andSwarm orchestrator for deploying and managing containers. Build the container image by running the docker build command in the terminal window. Finally, the script writes the results of the query to a new CSV file, output/items-sold.csv. This allows us to persist data external to the ephemeral containers. pgAdmin should then be available on localhost port 81.
Spark includes libraries for Spark SQL (DataFrames and Datasets), MLlib (Machine Learning), GraphX (Graph Processing), and DStreams (Spark Streaming). The project contains an alternate Docker stack file, stack-nbext.yml. Although these tasks could also be done from a Jupyter terminal or from within a Jupyter notebook, using a bootstrap script ensures you will have a consistent work environment every time you spin up the Jupyter Docker stack. Notebook documents contain the inputs and outputs of an interactive session as well as additional text that accompanies the code but is not meant for execution. There are additional options for configuring the Jupyter container. Included in the project is a bootstrap script, bootstrap_jupyter.sh. According to their website, PostgreSQL comes with many features aimed to help developers build applications, administrators to protect data integrity and build fault-tolerant environments, and help manage data no matter how big or small the dataset. Thats it, our new Jupyter environment is ready to start exploring. \u00a9 2020 wikiHow, Inc. All rights reserved. Log in to Docker Hub using docker login and provide your Docker id and password. To do so, we will use the psycopg2, the PostgreSQL database adapter package for the Python, we previously installed into our Jupyter container using the bootstrap script. All opinions expressed in this post are my own and not necessarily the views of my current or past employers, their clients. When a notebook is processed as part of a pipeline the associated container image is downloaded from the container registry stated in the URL of the image. You can run Spark using its standalone cluster mode,Apache Hadoop YARN,Mesos, orKubernetes. Made from the command line with vim by # Copyright (c) Jupyter Development Team. We will be using the latest jupyter/all-spark-notebookDocker Image. We create a file with this Dockerfile and inside this folder we the container image by running the docker build command in the terminal window. Compare those stats with the ones shown below, recorded while a notebook was reading and writing data, and executing Spark SQL queries. This directory will be bind-mounted into the PostgreSQL container on line 41 of the stack.yml file, $HOME/data/postgres:/var/lib/postgresql/data. A copy is also included in the project. [The running container is your PySpark environment to run PySpark codes later]. Another option to examine container performance metrics is top, which is pre-installed in our Jupyter container. wikiHow, Inc. is the copyright holder of this image under U.S. and international copyright laws.
With speeds up to 100 times faster than Hadoop, Apache Spark achieves high performance for static, batch, and streaming data, using a state-of-the-art DAG (Directed Acyclic Graph) scheduler, a query optimizer, and a physical execution engine. We are not limited to Jupyter notebooks to interact with Spark. Data Engineer, focus on stream processing and IoT. After successfully executed the Docker Pull Command above, open your Docker Desktop, the image will be available as follows: Hit RUN and filling in the popped-up form as follows: Click RUN: You will need to run the PySpark-Notebook Image to initialize a docker container in Docker Desktop. Username.
Printing the results in the output is merely for the purposes of the demo. https://www.docker.com/products/docker-desktop, Step 2.
This image may not be used by other entities without the express written consent of wikiHow, Inc.\n, \n"}, {"smallUrl":"https:\/\/www.wikihow.com\/images\/thumb\/a\/a3\/Make-Puppets-Step-13-Version-2.jpg\/v4-460px-Make-Puppets-Step-13-Version-2.jpg","bigUrl":"\/images\/thumb\/a\/a3\/Make-Puppets-Step-13-Version-2.jpg\/aid198046-v4-728px-Make-Puppets-Step-13-Version-2.jpg","smallWidth":460,"smallHeight":345,"bigWidth":"728","bigHeight":"546","licensing":". Uses include data cleansing and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. (LogOut/ Although not required, I usually pull new Docker images in advance. Many open-source software projects are also lowering the barriers to entry into these technologies. To confirm the SQL scripts success, use Adminer. This image may not be used by other entities without the express written consent of wikiHow, Inc.\n, \n"}, {"smallUrl":"https:\/\/www.wikihow.com\/images\/thumb\/7\/75\/Make-Puppets-Step-27-Version-2.jpg\/v4-460px-Make-Puppets-Step-27-Version-2.jpg","bigUrl":"\/images\/thumb\/7\/75\/Make-Puppets-Step-27-Version-2.jpg\/aid198046-v4-728px-Make-Puppets-Step-27-Version-2.jpg","smallWidth":460,"smallHeight":345,"bigWidth":"728","bigHeight":"546","licensing":", \u00a9 2020 wikiHow, Inc. All rights reserved. This article has been viewed 451,719 times. You can follow any responses to this entry through RSS 2.0. When you create a container image using this Dockerfile the default Spark Docker is loaded and the requirements listed in requirements.txt pip-installed. # Default values can be overridden at build time, # (ARGS are in lower case to distinguish them from ENV), "2385CB772F21B014CE2ABD6B8F5E815721580D6E8BC42A26D70BBCDDA8D303D886A6F12B36D40F6971B5547B70FAE62B5A96146F0421CB93D4E51491308EF5D5", "https://archive.apache.org/dist/spark/spark-, "--driver-java-options=-Xms1024M --driver-java-options=-Xmx4096M --driver-java-options=-Dlog4j.logLevel=info", # Add a link in the before_notebook hook in order to source automatically PYTHONPATH, # Fix Spark installation for Java 11 and Apache Arrow library, # see: https://github.com/apache/spark/pull/27356, https://spark.apache.org/docs/latest/#downloading, 'spark.driver.extraJavaOptions -Dio.netty.tryReflectionSetAccessible=true', 'spark.executor.extraJavaOptions -Dio.netty.tryReflectionSetAccessible=true', Forecast of Natural Gas Price with Deep Learning, How to create a Neural Network Python Environment for multiclass classification, How to use the parameters in Neural Networks, Building Blocks of Neural Networks and TensorSpace. This Python package reads key/value pairs from a .env file, making them available as environment variables to our notebook. You can begin by drawing an oval shape for the head, then make the small space for the neck. If you use two, it gives ultimate volume. The current version is 4.7.6 (March 2020). In ordering to execute the docker containers we need to install Docker in your computer or cluster. Shown below, we use Plotly to construct a bar chart of daily bakery items sold. You will need to pull a docker image which is built and pushed to Docker Hub by Jupyter. We can also submit scripts directly to Spark from the Jupyter terminal. Complete this step in a command line terminal so you will need to open it and run the following Docker Pull Command: Note that by using the image, you already have Python 3, Apache Spark, Jupyter Notebook (and other python libraries) packed in. Check PRE-REQUISITES firstly, especially the ability to run docker. The alternate image adds in the jupyter-contrib-nbextensions and jupyter_nbextensions_configurator packages. #2 by crotunno92 on August 15, 2021 - 2:07 pm. Another excellent choice for interacting with PostgreSQL, in particular, is pgAdmin 4.