Working on Databricks Community Edition

By Freshlyne

  •  After logging in, the first thing to do is to create a cluster (If a cluster has already been created, proceed to the next step).
    • For cluster creation, click the “Clusters” menu from the side ribbon, or use the “New Cluster” from “Common Tasks”:



  • Enter the cluster name and keep the rest of the settings as it is , for now, and click “Create Cluster”. This will create the cluster and spin it up. Starting a cluster can take some time, so be patient.


  •            The next step is to create a notebook where the magic happens.   For this, Go to Workspace > YourUsername > Create (From the Drop-down menu) > Notebook.



  • Give the notebook a name, select the default language (Don’t worry, any of the four languages can be used inside the notebook regardless of the default language), and choose the newly created cluster:



  • Once that’s done, it’ll look something like this (Default Notebook theme can be Light, can be changed from View):


  •      On top right hand, make sure the cluster is attached to the notebook. (In above step, it is Detached)
  •           Now, %python, %scala, %sql, %r can be used in different cell blocks to run the commands from those languages:



Note: Variables from one cell can be shared with others, but variables can’t be shared directly between two different languages.

  • Once we are done with our task, it is a good practice to terminate the cluster (The Square icon): 


Comments

Post a Comment

Popular posts from this blog

Cursor And It's Life Cycle

By Freshlyne
Image
What is Cursor in SQL Server? Cursor can be seen as a way to implement looping to SQL , which isn't a programming language, so much so it is a transactional language. SQL Cursor Life Cycle The following steps are involved in a SQL cursor life cycle.  1. Declaring Cursor At this stage, we define the SQL statement for the cursor, in turn defining the data set on which we are supposed to loop. 2. Opening Cursor Once we know the dataset, it's time to actually fetch that dataset for our cursor to run through. 3. Fetching Cursor This is the main step which is being looped over, in a sense. It fetches one row at a time from our cursor so that operations can be performed on the data fetched. 4. Closing Cursor The cursor should be closed explicitly after data manipulation. 5. Deallocating Cursor Cursors should be deallocated to delete cursor definition and release all the system resources associated with the cursor. In upcoming blog we will have an example on CURSOR.
Read more

Reading a file into a dataframe using PySpark in Databricks

By
Image
The very first thing we need is a file. For this demo, this is our sample file :  A comma-separated file with 3 columns and 3 rows. So now that we have that, the first step would be to actually upload the file over to databricks. Get the cluster running. Go to Data tab Click on "Create Table" Drag the file to the specified space. At this point, the file will be uploaded to the DBFS storage. After this, we can work with the file. Copy the location of the file. (Here: /FileStore/tables/SampleData-1.csv) Now, let's get back to the notebook to work with the file.  Creating a dataframe from the file dataframe_var=spark.read.option("header","true").csv('/FileStore/tables/SampleData-1.csv')   Display the dataframe    We can see that the file data is successfully displayed in the dataframe. We will take a look at how to load files to tables in the next blog.
Read more