Create a JupyterLab notebook for Spark (2023)

Create a JupyterLab notebook for Spark (1)

This is the third article in a series to build a Big Data development environment in AWS. Before you proceed further, ensure you’ve completed the first and second articles.

What is Jupyterlab?

Imagine this – you’ve created a pipeline to clean your company’s raw data and enrich it according to business requirements. You’ve documented each table and column in excruciating detail. Finally you built a dashboard brimming with charts and insights which tell a compelling narrative of the business’ health and direction.

How do you share and present your work?

If you’ve worked as a Data Engineer or Data Scientist before, this probably sounds familiar. Your work exists as a bundle of Python/Scala scripts on your laptop. Its logic is scattered across multiple files. The visualizations exported as PNG images in another directory.

Sure, you could commit the code to version control but that doesn’t help you present the data. Git repos are designed to display text not graphs and charts.

Here’s where a notebook comes in handy. To quote Project Jupyter

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

You tell a story when you write a notebook. Code shows how you accomplished something but the narrative text says why. Embbed visualizations transform an inscrutable 12 column table into a easily-digestible bar chart. Further, notebooks are compatible with version control: notebooks can be exported as JSON objects.

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Jupyter supports 3 languages at its core: Julia, Python, and R – all languages heavily used in numerical and scientific computing. Its interactive web interface permits rapid code prototyping, saving of user sessions, and display of graphics. At its heart, it uses a kernel to interact with a programming language.

In this article, I will demonstrate

  • How to prepare data for use with Spark and Jupyter notebooks
  • How to install Jupyterhub
  • Install the Spark 3 kernel in Jupyterhub
  • Use Jupyterhub to explore data

Download and load Airlines dataset

Start your EC2 instance in the AWS console and note down the public IP address.

SSH in

ssh ubuntu@{public-ip-address}
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Start the DFS, YARN, and Hive metastore DB using the script you created previously

source /home/ubuntu/
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The first thing we will do is download the Airline Dataset. I got it from by its origin is RITA. The full dataset contains a list of US flights from 1987 to 2008 and information such as their Departure Times and Arrival Times as well as delays.

The full dataset is 12 GB but we use a truncated version which contains 2k rows per year. Download it to your home directory using wget

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Now that you have the data saved at /home/ubuntu/allyears2k.csv , we will send the data to HDFS using the put command. A multi-node Spark cluster can read the data files from HDFS in parallel and speed up data loading.

(Video) How to Integrate PySpark with Jupyter Notebook

hdfs dfs -mkdir -p /data/airlineshdfs dfs -put allyears2k.csv /data/airlineshdfs dfs -ls /data/airlines
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Now let’s use the Hive CLI to create a new database called airlines_db . Notice that we are using Hive and not Spark to run the table DDL. Because we configured Spark to use the Hive data catalog, we can also access the airlines_db database and its child tables in Spark

Launch the Hive CLI.


Create the new database and use it. This sets airlines_db as the session database, meaning that queries that don’t specify a database assume the use of airlines_db.

CREATE DATABASE airlines_db;USE airlines_db;
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Now that you set airlines_db as the session database, create the airlines table inside of it

CREATE TABLE `airlines`( `year` int, `month` int, `dayofmonth` int, `dayofweek` int, `deptime` int, `crsdeptime` int, `arrtime` int, `crsarrtime` int, `uniquecarrier` string, `flightnum` int, `tailnum` string, `actualelapsedtime` int, `crselapsedtime` int, `airtime` int, `arrdelay` int, `depdelay` int, `origin` string, `dest` string, `distance` int, `taxiin` int, `taxiout` int, `cancelled` int, `cancellationcode` int, `diverted` int, `carrierdelay` int, `weatherdelay` int, `nasdelay` int, `securitydelay` int, `lateaircraftdelay` int, `isarrdelayed` string, `isdepdelayed` string) ROW FORMAT DELIMITED FIELDS TERMINATED BY ','TBLPROPERTIES ('skip.header.line.count' = '1');
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The table DDL consists of 3 parts,

First we have a list of column names and column types e.g. DayofMonth INT, IsArrDelayed STRING etc.

Then, we specify what the storage format is. In this case, it’s ROW FORMAT DELIMITED FIELDS TERMINATED BY ‘,’. By default, Hive uses TEXTFILE, and in this textfile we expect the data to be laid out in rows where each field in a row is terminated by the ‘,’ character and each row is terminated by the newline ‘\n’ character

At this point, the airlines table is empty. We want to load the data from allyears2k.csv in HDFS

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Note that Hive deletes /data/airlines/allyears2k.csv upon completing the load operation. If you want to keep the input file, use an external table to load data.

We can now explore the data in the airlines table

SELECT * FROM airlines LIMIT 10
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We can even count the rows in airlines

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I mentioned previously that, we are using Hive as a data catalog for Spark. This means Spark can access tables defined in the Hive metastore. Let’s verify this.

Exit the Hive CLI


Then launch the Pyspark shell.

pyspark --master yarn
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We can view the databases accessible to Spark using SHOW DATABASES. show() is added so the results are printed in the console

spark.sql("SHOW DATABASES").show()
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(Video) My recommendation: JupyterLab for Python and PySpark coding

Many commands (but not all) Hive commands also work in Spark SQL. For example we will use the USE command to set airlines_db as the current database

spark.sql("USE airlines_db")
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We then use SHOW TABLES to list the tables in airlines_db. If we did not specify airlines_db as the current database, we would use SHOW TABLES FROM airlines_db. As above, we add .show() to print the results to console

spark.sql("SHOW TABLES").show()
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We can perform the same operations we did in Hive in Spark. Let’s select 10 rows and count the rows in airlines.

spark.sql("SELECT * FROM airlines LIMIT 10").show
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spark.sql("SELECT COUNT(1) FROM airlines").show()
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You may have noticed the commands run faster in Spark compared to Hive. This is because Spark uses an in-memory execution model. For this reason, we prefer doing data processing in Spark over Hive.

Install Jupyterlab and Run Jupyterlab

In this section we’re going to install the Jupyterlab server. Jupyterlab has a web-based development environment for Jupyter notebook that also supports plugins to add functionality.

Fortunately, Jupyterlab is available through pip. However, installing Jupyterlab entails installing a long list of Python dependencies. To avoid dependency conflicts, we will install Jupyterlab in a virtual environment called venv

First, we use apt-get to install venv for Python 3

sudo apt-get install python3-venv
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Then we create a directory for the virtual environment called dl-venv. This is done using the venv command

python3 -m venv dl-venv
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The venv command creates a directory called dl-venv in /home/ubuntu

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This directory contains a Python interpreter, libraries and scripts which are isolated from the rest of the system. When you activate the virtual environment and install a new Python library, the library is installed here instead of the system’s Python directory. When you deactivate the virtual environment, these libraries are not longer accessible to the system’s Python interpreter.

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Let’s activate the dl-venv virtual environment. venv provides a script to do so

source dl-venv/bin/activate
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Now that dl-venv is active, we can proceed to install Jupyterlab is an isolated Python environment

pip3 install jupyterlab
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Once Jupyterlab and Jupyter notebook are installed, we must generate a config file. Many important settings such as the hashed user password, Jupyterlab’s IP bindings and remote access control are located in the config file.

(Video) Big Data: Using Spark from Python and Jupyter

jupyter notebook --generate-config
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Now set a password (and don’t forget it!). But even if you do – you can always set a new one using this command

jupyter notebook password
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Now open up /home/ubuntu/ and uncomment and change 2 configs.

The first is to make the notebook server listen on all IPs

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The second is to allow access from remote hosts

c.NotebookApp.allow_remote_access = True
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Here’s what /home/ubuntu/.jupyter contains after configuration

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Now for the moment of truth! Let’s start JupyterLab

jupyter lab
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Now I hope you remember your EC2 instance’s public IP – if not you can get it from the AWS Console. Open you web browser and navigate to the following URL


You may be prompted for you password. Enter the password you set.

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Set up the Pyspark kernel

Great stuff. You’ve successfully launched Jupyterlab and accessed the web development environment. You may have noticed the option to launch Python 3 notebooks and consoles. But we’re missing the option to launch the same for Pyspark.

Don’t worry, we’re going to install the kernel for Pyspark 3 in this section. What’s a kernel? Well,

Kernels areprogramming language specificprocesses that run independently and interact with the Jupyter Applications and their user interfaces.IPythonis the reference Jupyter kernel, providing a powerful environment for interactive computing in Python.

Aside from the core languages of Julia, Python and R, Jupyter supports other languages through adding more kernels. In our case, we’ll be creating a custom one since ours is a simple one-node Spark cluster. For a more complex and production-ready kernel, check out sparkmagic.

Ok, first let’s shutdown the Jupyter Server using Control + C and type y to the prompt

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We will need to add a kernel in the dl-venv directory. For full complete treatment of creating kernels see this

(Video) Install Spark on Mac + Configure Jupyter Notebook (Python)

Otherwise, create a new file /home/ubuntu/dl-venv/share/jupyter/kernels/pyspark3/kernel.json with the following text

{ "argv": [ "python", "-m", "ipykernel_launcher", "-f", "{connection_file}" ], "display_name": "Pyspark 3", "language": "python", "env": { "PYSPARK_PYTHON": "/usr/bin/python3", "SPARK_HOME": "/opt/spark3/", "SPARK_OPTS": "--master yarn --conf spark.ui.port=0", "PYTHONPATH": "/opt/spark3/python/lib/" }}

The argv section contains the command line arguments used to launch the kernel. The display_name is what will be shown in the UI as the kernel name. The language is the programming language used in the kernel. env is a dictionary of environment variables initialized when a kernel is launched. In our case, we set env relevant for Spark.

Once we have placed kernel.json, we are ready to install the kernel

jupyter kernelspec install /home/ubuntu/dl-venv/share/jupyter/kernels/pyspark3 --user
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Verify the kernel is installed by listing all installed kernels

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Go ahead and restart Jupyterlab and login at the public ip http://{ec2-public-ip}:8888/lab

jupyter lab

You will now see Pyspark 3 listed as a kernel option under Notebook and Console. Let’s create a new Pyspark 3 notebook. Click on Pyspark 3

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Let’s try running the Spark SQL code we tested earlier. Previously, the Pyspark shell created the SparkSession automatically for us. We must do this manually when using Jupyterlab

from pyspark.sql import SparkSessionspark = SparkSession. \ builder. \ enableHiveSupport(). \ appName('my-demo-spark-job'). \ master('yarn'). \ getOrCreate()spark.sql('SHOW DATABASES').show()spark.sql('SELECT count(1) FROM airlines_db.airlines').show()

The first 8 lines of the notebook are dedicated to creating the SparkSession. You can learn more about the SparkSession.builder here. Notice the use of enableHiveSupport() so we can access tables defined in the Hive metastore and the use of appName() to which sets the application’s name in YARN

Past the code above into the notebook’s first cell and click the play button to run the code. Wait awhile as it takes time to start the Pyspark kernel.

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Observe that the results of the query are printed directly below the cell containing code. The format distinction between input and results helps in the presentation of code.

If you are watching the logging in the Jupyterlab application, you can observe it starting an instance of the Pyspark kernel. This instance is used to execute the Spark code and Jupyterlab prints the results in the notebook

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Nice work. We have a full functional Jupyterlab deployment on which we can use a notebook to develop Pyspark code. The notebook also provides formatting of results and storage of notebooks so you can return to work-in-progress. If you’re interested in providing Jupyter notebooks in a multi-user environment, checkout Jupyterhub

Now, let’s begin shutting down the Jupyterlab and Spark server. First, shutdown Jupyterlab but entering Control + C in the command line and enter Y when prompted

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Next, stop the HDFS, YARN and Hive metastore DB using the script

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Finally, shutdown the EC2 instance in the AWS Console. This step is critical to avoid being charged unnecessary hours for the EC2 instance. t2.xlarge costs 0.2336 USD / hour and is expensive to run for extended periods of time.

Closing remarks

Notebooks are an indispensable part of the modern Data Engineer’s and Data Scientist’s toolkit. Notebooks like Jupyterlab weave the data narrative and data plots into the code in way that a traditional IDE does not. The web development interface opens the possibility for remote development and even sharing of content in a richer way than a traditional version control system.

We’ve successfully set up HDFS, YARN, Hive, Spark, and Jupyterlab. This stack is sufficient for a Data Engineer developing a batch ETL job.

What if you’re working with a streaming application? Stay tuned for the next installment where I will set up a single node Kafka cluster and ship logs to it

(Video) 5.1 - Jupyter on Dataproc | Apache Spark on Dataproc | Google Cloud Series


How do I run a Jupyter notebook on Spark cluster? ›

There are four key steps involved in installing Jupyter and connecting to Apache Spark on HDInsight.
  1. Configure Spark cluster.
  2. Install Jupyter Notebook.
  3. Install the PySpark and Spark kernels with the Spark magic.
  4. Configure Spark magic to access Spark cluster on HDInsight.
May 6, 2022

How do I create a new notebook in JupyterLab? ›

  1. Create a notebook by clicking the + button in the file browser and then selecting a kernel in the new Launcher tab:
  2. Tab completion (activated with the Tab key) can now include additional information about the types of the matched items:
  3. Note: IPython 6.3.

Can you run Spark in Jupyter notebook? ›

JupyterLab is the next-gen notebook interface that further enhances the functionality of Jupyter to create a more flexible tool that can be used to support any workflow from data science to machine learning. Jupyter also supports Big data tools such as Apache Spark for data analytics needs.

Can I run Scala in Jupyter notebook? ›

Jupyter notebook is widely used by almost everyone in the data science community. While it's a tool with extensive support for python-based development of machine learning projects, one can also use it for Scala development as well, using the spylon-kernel.

How do I connect Spark to Python? ›

Spark comes with an interactive python shell. The PySpark shell is responsible for linking the python API to the spark core and initializing the spark context. bin/PySpark command will launch the Python interpreter to run PySpark application. PySpark can be launched directly from the command line for interactive use.

Is JupyterLab same as Jupyter Notebook? ›

JupyterLab is the next generation of the Jupyter Notebook. It aims at fixing many usability issues of the Notebook, and it greatly expands its scope. JupyterLab offers a general framework for interactive computing and data science in the browser, using Python, Julia, R, or one of many other languages.

What is the difference between JupyterLab and notebook? ›

Jupyterlab can open multiple ". ipynb" files inside a single browser tab. Whereas, Jupyter Notebook will create new tab to open new ". ipynb" files every time.

How do I create a local Jupyter Notebook? ›

Jupyter Interface

To create a new notebook, go to New and select Notebook - Python 2. If you have other Jupyter Notebooks on your system that you want to use, you can click Upload and navigate to that particular file. Notebooks currently running will have a green icon, while non-running ones will be grey.

How do I know if Jupyter notebook has Spark? ›

You can check your Spark setup by going to the /bin directory inside {YOUR_SPARK_DIRECTORY} and running the spark-shell –version command. Here you can see which version of Spark you have and which versions of Java and Scala it is using. That's it!

Is Databricks same as Jupyter notebook? ›

Notebooks in Azure Databricks are similar to Jupyter notebooks, but they have enhanced them quite a bit. Due to these enhancements, exploring our data is much easier. To create a notebook, on the left navigation click on “Workspace”.

Is PySpark and Spark same? ›

Apache Spark is written in Scala programming language. PySpark has been released in order to support the collaboration of Apache Spark and Python, it actually is a Python API for Spark. In addition, PySpark, helps you interface with Resilient Distributed Datasets (RDDs) in Apache Spark and Python programming language.

Can we write PySpark code in Jupyter? ›

PySpark in Jupyter

There are two ways to get PySpark available in a Jupyter Notebook: Configure PySpark driver to use Jupyter Notebook: running pyspark will automatically open a Jupyter Notebook. Load a regular Jupyter Notebook and load PySpark using findSpark package.

How do I run JupyterLab notebook? ›

To open the classic Notebook from JupyterLab, select “Launch Classic Notebook” from the JupyterLab Help menu, or you can change the URL from /lab to /tree . JupyterLab runs on top of Jupyter Server, so see the security section of Jupyter Server's documentation for security-related information.

Is Scala losing popularity? ›

Scala's popularity shot up, becoming the 20th ranked language on the TIOBE index in 2018. Today it is lower, although it is No. 14 on Redmonk. According to Oliver White, chief editor at Lightbend, the company behind Scala, the programming language supports companies with valuations in the hundreds of billions.

Should I use Python or Scala for Spark? ›

Boring answer, but it depends on what your project needs are. If you want to work on a smaller project with less experienced programmers, then Python is the smart choice. However, if you have a massive project that needs many resources and parallel processing, then Scala is the best way to go.

Can I run Python code on Spark? ›

Standalone Programs

PySpark can also be used from standalone Python scripts by creating a SparkContext in your script and running the script using bin/pyspark . The Quick Start guide includes a complete example of a standalone Python application.

Is PySpark faster than Python? ›

Fast processing: The PySpark framework processes large amounts of data much quicker than other conventional frameworks. Python is well-suited for dealing with RDDs since it is dynamically typed.

Can I use Apache Spark with Python? ›

Apache Spark has APIs for Python, Scala, Java, and R, though the most used languages with Spark are the former two. In this tutorial, you will learn how to use Python API with Apache Spark.

Can you use JupyterLab without Anaconda? ›

JupyterLab can be installed using conda , mamba , pip , pipenv or docker .

Is Jupyter different from Jupyter Notebook? ›

JupyterLab runs in a single tab, with sub-tabs displayed within that one tab, Jupyter Notebook opens new notebooks in new tabs. So JupyterLab feels more like an IDE; in Notebook notebooks, it feels more standalone. All the files are opened as different tabs in your webbrowser. It depends on you what you prefer more.

What is a JupyterLab notebook? ›

JupyterLab: A Next-Generation Notebook Interface

JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning.

Will JupyterLab replace Jupyter Notebook? ›

JupyterLab will eventually replace the classic Jupyter Notebook.

Is Jupyter Notebook worth using? ›

A Jupyter Notebook provides you with an easy-to-use, interactive data science environment that doesn't only work as an integrated development environment (IDE), but also as a presentation or educational tool.

How do I create an empty Jupyter Notebook? ›

You can create a new Python 3 Jupyter Notebook file (. ipynb) by clicking on New and selecting Python 3 . A new notebook will open a new tab in your web browser. You can use the Jupyter Notebook dashboard menu to create new Jupyter Notebook files (.

Does JupyterLab run locally? ›

Even though JupyterLab is a web-based application, JupyterLab runs locally on your machine and does not require an internet connection.

How do I get a Jupyter Notebook? ›

Installing Jupyter using Anaconda and conda
  1. Download Anaconda. We recommend downloading Anaconda's latest Python 3 version (currently Python 3.9).
  2. Install the version of Anaconda which you downloaded, following the instructions on the download page.
  3. Congratulations, you have installed Jupyter Notebook.

How do you display Spark Dataframe in Jupyterlab? ›

You can visualize a Spark dataframe in Jupyter notebooks by using the display(<dataframe-name>) function. The display() function is supported only on PySpark kernels. The Qviz framework supports 1000 rows and 100 columns. By default, the dataframe is visualized as a table.

Do we need to install Spark for Pyspark? ›

If we have Apache Spark installed on the machine we don't need to install the pyspark library into our development environment. We need to install the findspark library which is responsible of locating the pyspark library installed with apache Spark.

How do I set up Pyspark in Anaconda? ›

  1. Step 1: Install Java conda install openjdk.
  2. Step 2: Install pyspark conda install pyspark.
  3. Step 3: Install findspark conda install -c conda-forge findspark.
  4. Step 7: Spark Commands from pyspark.sql import SparkSession. spark = SparkSession.builder.appName('pySparkSetup').getOrCreate()
Dec 10, 2022

What are the downsides of Jupyter notebook? ›

Jupyter can be slow to start up, and it can be slow to execute code. This is because Jupyter is an interactive tool, and it has to load the entire notebook in memory in order to provide the interactive features. If you're working with large data sets or large notebooks, this can be a major problem.

What are the cons of using Databricks? ›

  • "The lack of options of visualization and creation of dashboards."
  • "Databricks has a bad file management system and it is slow sometimes. ...
  • "Hard to manage notebook workspace. ...
  • "It can be extremely confusing given the sheer breadth of tools available."
Apr 25, 2022

Is Databricks notebook free? ›

All users can share and host their notebooks free of charge with Databricks. We hope this will enable everyone to create new and exciting content that will benefit the entire Apache Spark community.

Which is better Spark or PySpark? ›

Spark is an awesome framework and the Scala and Python APIs are both great for most workflows. PySpark is more popular because Python is the most popular language in the data community. PySpark is a well supported, first class Spark API, and is a great choice for most organizations.

What language is Spark written in? ›

Apache Spark

Why is Spark better than Pandas? ›

In very simple words Pandas run operations on a single machine whereas PySpark runs on multiple machines. If you are working on a Machine Learning application where you are dealing with larger datasets, PySpark is a best fit which could processes operations many times(100x) faster than Pandas.

How do you add Spark? ›

Steps in Apache Spark Installation
  1. Prerequisites: A system running Windows 10. ...
  2. Step 1: Verifying Java Installation. ...
  3. Step 2: Verifying Scala Installation. ...
  4. Step 3: Downloading Scala. ...
  5. Step 4: Installing Scala. ...
  6. Step 5: Downloading Apache Spark. ...
  7. Step 6: Installing Spark. ...
  8. Step 7: Verifying the Spark Installation.
Dec 12, 2022

How to install FindSpark? ›

Click on Windows and search “Anacoda Prompt”. Open Anaconda prompt and type “python -m pip install findspark”. This package is necessary to run spark from Jupyter notebook.

How do I install and run PySpark in a Jupyter notebook on a Mac? ›

Steps to install PySpark & Jupyter on Mac OS
  1. Step 1 – Install Homebrew.
  2. Step 2 – Install Java.
  3. Step 3 – Install Scala (Optional)
  4. Step 4 – Install Python.
  5. Step 5 – Install PySpark.
  6. Step 6 – Install Jupyter.
  7. Step 7 – Run Example in Jupyter.

How to install and run PySpark in Jupyter notebook on windows? ›

  1. Download and Install JAVA. As Spark uses Java Virtual Machine internally, it has a dependency on JAVA. ...
  2. Download and Install Python. ...
  3. Download and unzip PySpark. ...
  4. Download winutils.exe. ...
  5. Set Environment variables. ...
  6. Let's fire PySpark! ...
  7. Jupyter Notebook integration with Python. ...
  8. Running a sample code on the Jupyter Notebook.
May 2, 2022

Can you run Spark locally? ›

It's easy to run locally on one machine — all you need is to have java installed on your system PATH , or the JAVA_HOME environment variable pointing to a Java installation. Spark runs on Java 8/11/17, Scala 2.12/2.13, Python 3.7+ and R 3.5+.

What is the difference between Apache Spark and PySpark? ›

Apache Spark is written in Scala programming language. PySpark has been released in order to support the collaboration of Apache Spark and Python, it actually is a Python API for Spark. In addition, PySpark, helps you interface with Resilient Distributed Datasets (RDDs) in Apache Spark and Python programming language.

Is Spark still in demand? ›

Apache Spark is a great tool to handle and process Big Data. It is a much-valued skill, and as the software industry marches towards better apps, the demand for these developers will keep growing.

What is the purpose of Findspark? ›

FindSpark is a community dedicated to setting up young professionals for career success and connecting employers to top, diverse young professional talent.

Why do we use Findspark? ›

Findspark can add a startup file to the current IPython profile so that the environment vaiables will be properly set and pyspark will be imported upon IPython startup. This file is created when edit_profile is set to true.

Can you run PySpark locally? ›

PySpark Install Using pip. Alternatively, you can install just a PySpark package by using the pip python installer. Note that using Python pip you can install only the PySpark package which is used to test your jobs locally or run your jobs on an existing cluster running with Yarn, Standalone, or Mesos.

How do I install PySpark Spark? ›

Installing Apache Spark
  1. Head over to the Spark homepage.
  2. Select the Spark release and package type as following and download the . tgz file.
  3. Save the file to your local machine and click 'Ok'.
  4. Let's extract the file using the following command. $ tar -xzf spark-2.4.6-bin-hadoop2.7.tgz.

Can I PIP install PySpark? ›

For Python users, PySpark also provides pip installation from PyPI. This is usually for local usage or as a client to connect to a cluster instead of setting up a cluster itself. This page includes instructions for installing PySpark by using pip, Conda, downloading manually, and building from the source.


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Author: Terence Hammes MD

Last Updated: 01/02/2023

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Name: Terence Hammes MD

Birthday: 1992-04-11

Address: Suite 408 9446 Mercy Mews, West Roxie, CT 04904

Phone: +50312511349175

Job: Product Consulting Liaison

Hobby: Jogging, Motor sports, Nordic skating, Jigsaw puzzles, Bird watching, Nordic skating, Sculpting

Introduction: My name is Terence Hammes MD, I am a inexpensive, energetic, jolly, faithful, cheerful, proud, rich person who loves writing and wants to share my knowledge and understanding with you.