Step 1: Signup to Databricks community edition – https://databricks.com/try-databricks. Fill in the details and you can leave your mobile number blank. Select “COMMUNITY EDITION” ==“GET STARTED“.
If you have a Cloud account then you can use it.
Step 2: Check your email and click the “link” in the email & reset your password.…
Prerequisite: Extends Databricks getting started – Spark, Shell, SQL.
Q: What is a Dataframe?
A: A DataFrame is a data abstraction or a domain-specific language (DSL) for working with structured and semi-structured data, i.e. datasets with a schema. Dataframes are immutable, stored in memory, resilient (i.e. fault-tolerant), distributed (i.e.…
Prerequisite: Extends Databricks getting started – Spark, Shell, SQL.
SCD stands for Slowly Changing Dimension, and it was explained in 10 Data warehouse interview Q&As.
Step 1: Remove all cells in the notebook with the “x” and then confirm or create a new Python notebook.…
Prerequisite: Extends 03: Databricks – Spark SCD Type 1. What is SCD Type 2 SCD stands for Slowly Changing Dimension, and it was explained in 10 Data warehouse interview Q&As….
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Prerequisite: Extends 03: Databricks – Spark SCD Type 1. What is SCD Type 1 SCD stands for Slowly Changing Dimension, and it was explained in 10 Data warehouse interview Q&As….
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Prerequisite:…
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Prerequisite: Extends Databricks getting started – Spark, Shell, SQL.
User-Defined Functions (aka UDF) is a feature of Spark SQL to define new Column-based functions that extend the vocabulary of Spark SQL’s DSL for transforming Datasets.
Step 1: Create a new Notebook in Databricks, and choose Python as the language.…
Prerequisite: Extends Databricks getting started – Spark, Shell, SQL. What is a window function? Q. What are the different types of functions in Spark SQL? A. There are 4 types…
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Prerequisite: Extends Databricks – Spark Window functions. Step 1: Create a new Python notebook, and attach it to a cluster. Step 2: Let’s create some data using pyspark.
Prerequisite: Extends Databricks – Spark Window functions.
Problem: Convert the below table
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+------+--------+---------+-------------+ |emp_id|emp_name| emp_city|emp_expertise| +------+--------+---------+-------------+ | 1| John| Sydney| Java| | 2| Peter|Melbourne| Scala| | 3| Sam| Brisbane| Python| | 4| David|Melbourne| Python| | 5| Elliot| Sydney| Java| +------+--------+---------+-------------+ |
to
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+-------------+----------------------------------------------------------+ |Column |Content | +-------------+----------------------------------------------------------+ |emp_id |[[1, 1], [3, 1], [5, 1], [4, 1], [2, 1]] | |emp_name |[[Elliot, 1], [John, 1], [Sam, 1], [Peter, 1], [David, 1]]| |emp_city |[[Sydney, 2], [Melbourne, 2], [Brisbane, 1]] | |emp_expertise|[[Python, 2], [Java, 2], [Scala, 1]] | +-------------+----------------------------------------------------------+ |
Where each column is counted for its occurrence.…