This page contains PySpark tutorials. Dive into the world of PySpark, the powerful Python API for Apache Spark, designed for big data processing and analytics. Our hands-on tutorials equip you with the skills to handle large-scale data and perform distributed computing with ease. Learn step-by-step how to leverage PySpark's rich ecosystem to build data pipelines, execute complex transformations, and perform machine learning on big datasets. Our hands-on tutorials will help you master PySpark.
📘 Introduction Renaming columns is one of the most common transformations you’ll perform when cleaning or standardizing data in PySpark. Whether you’re aligning tables from different systems, preparing data for machine learning, or simply making column names more readable, updating many column names at once can quickly become tedious...
📘 Introduction In many real-world datasets, the same type of information can appear in more than one column. A customer may provide an email address, a phone number, or a backup contact, and different systems may populate different fields. When you want to select the first available non-null value from several...
📘 Introduction Real-time data ingestion is a critical part of modern data architectures. Organizations need to process and store continuous streams of information for analytics, monitoring, and machine learning. Databricks, with the combined power of PySpark and Delta Lake, provides an efficient way to build end-to-end streaming pipelines that handle data...
📘 Introduction When processing massive datasets in PySpark, it’s often necessary to uniquely identify rows or efficiently detect changes across records. Using multiple columns as a composite key can quickly become cumbersome and inefficient — especially during joins or deduplication. A better solution is to generate a single hash value derived...
📘 Introduction When running Spark jobs, you expect every task to share the workload evenly — but that’s not always the case. Sometimes, a few tasks take far longer than the rest, keeping the entire stage waiting. This imbalance, known as data skew, is one of the most common causes of...
📘 Introduction Every Spark application tells a story — a story of how your code travels from a high-level command in Python or Scala to a fully distributed computation running across dozens or even hundreds of executors. Behind the scenes, Spark organizes this work into jobs, stages, and tasks — the building blocks...
📘 Introduction In Apache Spark, performance often hinges on one crucial process — shuffle. Whenever Spark needs to reorganize data across the cluster (for example, during a groupBy, join, or repartition), it triggers a shuffle: a costly exchange of data between executors. Shuffle is what makes distributed computation possible — but it’s...
📘 Introduction Apache Spark is a distributed data processing framework designed for speed and scalability. When you run a Spark job, it doesn’t just run on your laptop — it coordinates multiple machines working together to process massive datasets in parallel. To understand how Spark does this so efficiently, you need...
📘 Introduction When you run a PySpark job, Spark doesn’t immediately execute each transformation. Instead, it constructs something called a DAG (Directed Acyclic Graph) — a roadmap of all the operations that need to happen. This DAG is the heart of Spark’s execution engine. It tells Spark how your data...