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- Memory Management in PySpark- Scenario 1, 2
- Develop and maintain CI/CD pipelines using GitHub for automated deployment, version control
- Complete guide to building and managing data workflows in Azure Data Factory (ADF)
- Complete guide to architecting and implementing data governance using Unity Catalog on Databricks
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Author: lochan2014
String manipulation is a common task in data processing. PySpark provides a variety of built-in functions for manipulating string columns in DataFrames. Below, we explore some of the most useful string manipulation functions and demonstrate how to use them with examples. Common String Manipulation Functions Example Usage 1. Concatenation Syntax: 2. Substring Extraction Syntax: 3.…
✅ What is a DataFrame in PySpark? A DataFrame in PySpark is a distributed collection of data organized into named columns, similar to a table in a relational database or a Pandas DataFrame. It is built on top of RDDs and provides: 📊 DataFrame = RDD + Schema Under the hood: So while RDD is…
Big Data Lake: Data Storage HDFS is a scalable storage solution designed to handle massive datasets across clusters of machines. Hive tables provide a structured approach for querying and analyzing data stored in HDFS. Understanding how these components work together is essential for effectively managing data in your BDL ecosystem. HDFS – Hadoop Distributed File…
Ordered Guide to Big Data, Data Lakes, Data Warehouses & Lakehouses 1 The Modern Data Landscape — Bird’s‑Eye View Every storage paradigm slots into this flow at the Storage layer, but each optimises different trade‑offs for the rest of the pipeline. 2 Foundations: What Is Big Data? 5 Vs Meaning Volume Petabytes+ generated continuously Velocity Milliseconds‑level arrival & processing Variety Structured, semi‑structured, unstructured Veracity Data quality…