As a Data Engineer, I collect, extract and transform raw data in order to provide clean, reliable and usable data.
Introduction In order to work with data in Microsoft Fabric, you first need to get data into it. There are several ways to do this. One way is to use a data pipeline. In this tutorial, we will explain step-by-step how to ingest data into a Fabric warehouse using a...
Introduction One fundamental part of Microsoft Fabric is transforming data. Whether filtering, joining, merging or grouping data, there are several options available in Fabric to perform these operations. In this tutorial, we will explain step-by-step how to group data and apply an aggregation function using a dataflow. Goal A delta...
Introduction In this tutorial, we will show you how to group and concatenate strings in a PySpark DataFrame. In order to do this, we will use the groupBy() method in combination with the functions concat_ws(), collect_list() and array_distinct() of PySpark. Import Libraries First, we import the following...
Introduction In order to work with data in Microsoft Fabric, you first need to get data into it. There are several ways to do this. One way is to use a dataflow. In this tutorial, we will explain step-by-step how to ingest data into a Fabric lakehouse using a dataflow....
Introduction In order to work with data in Microsoft Fabric, you first need to get data into it. There are several ways to do this. One way is to use a data pipeline. In this tutorial, we will explain step-by-step how to ingest data into a Fabric lakehouse using a...
Introduction In order to work with data in Microsoft Fabric, you first need to get data into it. There are several ways to do this. One way is to manually upload files or folders from your local drive into a Fabric lakehouse. In this tutorial, we will explain step-by-step how...
Introduction Data transformation is a fundamental task in any data analysis or processing pipeline. In the realm of big data processing, Apache Spark has emerged as a powerful framework for handling large-scale data processing tasks efficiently. When it comes to transforming data within Spark, developers often have to choose between...
Introduction In the realm of big data processing, PySpark has emerged as a powerful tool for handling large-scale datasets. Its distributed computing framework allows for efficient processing of massive volumes of data. However, despite its capabilities, performing certain data transformations in PySpark can sometimes be cumbersome and complex. That'...
Introduction Data manipulation tasks often involve converting column data types to ensure consistency and accuracy in analysis. In this tutorial, we will show you how to change column types of a PySpark DataFrame. In order to do this, we will use the cast() function of PySpark. Import Libraries First, we...