ERROR Utils: Aborting task java.io.IOException: Failed to connect to – Local Spark

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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 […]

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Get the first non-null value per group Spark dataframe

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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

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Download a file from DBFS – Databricks to the local machine

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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

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Scala Option, Some, None – Exception and Null handling

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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

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Scala Try, Success, Failure – Functional error handling

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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

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Using Pandas on Spark

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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

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Create an Azure Function in Java using IntelliJ IDEA

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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

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Use HDFS API to read Azure Blob files in Databricks

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Databricks provides a wrapper file system API named DBFS (Databricks File System) to perform any file-level operation such as read, write, move, delete, rename, etc. However, sometimes we may need to read the underlying file system objects directly without using the DBFS wrapper APIs. To do so, we can use HDFS APIs available through py4j

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