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- Date and Time Functions- Pyspark Dataframes & Pyspark Sql Queries
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- Memory Management in PySpark- Scenario 1, 2
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- Complete guide to building and managing data workflows in Azure Data Factory (ADF)
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Category: Tutorials
Absolutely! Let’s walk through all major PySpark data structures and types that are commonly used in transformations and aggregations — especially: 🧱 1. Row — Spark’s Internal Data Holder Example: Used when creating small DataFrames manually. 🏗 2. StructType / StructField — Schema Definition Objects Example: Used with: 🧱 3. struct() — Row-like object inside…
PySpark Control Statements Vs Python Control Statements- Conditional, Loop, Exception Handling, UDFs
Python control statements like if-else can still be used in PySpark when they are applied in the context of driver-side logic, not in DataFrame operations themselves. Here’s how the logic works in your example: Understanding Driver-Side Logic in PySpark Breakdown of Your Example This if-else statement works because it is evaluated on the driver (the main control point of…
Q1.–We are working with large datasets in PySpark, such as joining a 30GB table with a 1TB table or Various Transformation on 30 GB Data, we have 100 cores limit to use per user , what can be best configuration and Optimization strategy to use in pyspark ? will 100 cores are enough or should…
Data cleaning in SQL is a crucial step in data preprocessing, especially when working with real-world messy datasets. Below is a structured breakdown of SQL data cleaning steps, methods, functions, and complex use cases you can apply in real projects or interviews. ✅ Common SQL Data Cleaning Steps & Methods Step Method / Function Example…
Hive a Data warehouse infra Hive is an open-source data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis. It allows users to query and manage large datasets residing in distributed storage using a SQL-like language called HiveQL. Here’s an overview of Hive: Features of Hive: Components of Hive: Use…
Understanding how an SQL query executes in a database is essential for performance tuning and system design. Here’s a step-by-step breakdown of what happens under the hood when you run an SQL query like: 🧭 0. Query Input (Your SQL) You submit the SQL query via: ⚙️ Step-by-Step SQL Query Execution 🧩 Step 1: Parsing…
Here’s a comprehensive guide to important and tricky conceptual issues in SQL, including NULL behavior, joins, filters, grouping, ordering, and subqueries. ✅ 1. NULLs: The #1 source of confusion a. NULL ≠ NULL b. NOT IN with NULL c. Arithmetic with NULL ✅ 2. JOIN Issues a. INNER JOIN drops unmatched rows. b. LEFT JOIN…
Where to Use Python Traditional Coding in PySpark Scripts Using traditional Python coding in a PySpark script is common and beneficial for handling tasks that are not inherently distributed or do not involve large-scale data processing. Integrating Python with a PySpark script in a modular way ensures that different responsibilities are clearly separated and the…
Here’s a complete Azure Databricks tutorial roadmap (Beginner → Advanced), tailored for Data Engineering interviews in India, including key concepts, technical terms, use cases, and interview Q&A: ✅ What is Azure Databricks? Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for the Microsoft Azure cloud. 🔗 How Azure Databricks integrates…
Spark SQL supports several types of joins, each suited to different use cases. Below is a detailed explanation of each join type, including syntax examples and comparisons. Types of Joins in Spark SQL 1. Inner Join An inner join returns only the rows that have matching values in both tables. Syntax: Example: 2. Left (Outer)…