In the previous post, we discussed how to extract the non-null values per group from a spark dataframe. Now, in this post, we will learn how to fill the null values with the previous not-null value in a spark dataframe using the backfill method. To demonstrate this with the help of an example, we will […]
Fill null with the previous not null value – Spark Dataframe Read More »
In this post, we will discuss an error/warning message “java.io.IOException: Failed to connect to”. This error keeps coming when we try to execute a hive query from spark-shell using spark SQL. This error occurs when Spark tries to execute a task in local mode (pseudo-distributed mode). It is caused because of a connection exception. The
ERROR Utils: Aborting task java.io.IOException: Failed to connect to – Local Spark Read More »
Suppose, we need to get the first non-null value from a Dataframe from each partition. Certainly, we want to get only the first not null value from each column regardless of the rows. That means a not-null value from column A from row 5 can be stitched with another not-null value of column B from
Get the first non-null value per group Spark dataframe Read More »
In this post, we will learn how we can download a file from DBFS i.e. Databricks File System to the Local machine. DBFS is the File system that Databricks uses to store its files. It is a distributed file system mounted into a Databricks workspace and it is available on Databricks clusters. To demonstrate how
Download a file from DBFS – Databricks to the local machine Read More »
In the previous post, we discussed the Try, Success, Failure exception handling method. Now, in this post, we will discuss the use of Scala’s Option, Some, None pattern and its usage. Scala is a high-level programming language combining object-oriented and functional programming in one place. It is a very powerful programming language that can be
Scala Option, Some, None – Exception and Null handling Read More »
In this post, we will discuss the Scala’s functional error handling method using Try, Success, Failure. We know that Scala is a high-level programming language that combines both object-oriented and functional programming in one place. It runs on JVM so it can be mixed seamlessly with Java. Scala’s static types helps to identify bugs at
Scala Try, Success, Failure – Functional error handling Read More »
This post will teach us how to execute a scala file in Spark without creating a jar file. We know that a scala source code file has an extension of .scala. Also, we need to create or package the source code into a jar file to execute an application written in Scala. We can create
Execute Scala file in Spark without creating a jar Read More »
Pandas is one of the most popular Python libraries used by Data Scientists/Data Engineers for data wrangling and data analysis. Also, Pandas provide DataFrames (a table-like structure that stores data in rows and columns) to deal with structured datasets. These DataFrames are very similar to Spark’s DataFrames. However, Pandas dataframes are limited to a single
Previously, we discussed how we can create an Azure function in Java using IntelliJ IDEA IDE. In this post, we will learn how to create an Azure function in Python using VS Code (Visual Studio Code IDE). We know that Visual Studio Code is an open-source IDE that can be used to build, run and
Create an Azure Function in Python using VS Code Read More »
Azure Functions are serverless compute that help orchestrate and automate complex problems in an event-driven way. We can choose any supported language to create and deploy an Azure Function in the cloud. The supported language as of date is C#, Java, Python, JavaScript, F#, Powershell, and TypeScript. This post will learn how we can create
Create an Azure Function in Java using IntelliJ IDEA Read More »
