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- Date and Time Functions- Pyspark Dataframes & Pyspark Sql Queries
- Memory Management in PySpark- CPU Cores, executors, executor memory
- 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)
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Author: lochan2014
RDD (Resilient Distributed Dataset) is the fundamental data structure in Apache Spark. It is an immutable, distributed collection of objects that can be processed in parallel across a cluster of machines. Purpose of RDD How RDD is Beneficial RDDs are the backbone of Apache Spark’s distributed computing capabilities. They enable scalable, fault-tolerant, and efficient processing…
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…