by Team AHT | Aug 16, 2024 | SQL
We are to discuss these Datawarehouse terms regulary being asked in Data Related Job Interviews :-1. Data Warehouse 2. Data Mart 3.OLTP, OLAP and their differences 4.Fact and Dimension Tables 6. Difference between fact and Dimension tables 7.Star and Snowflake Schema... by Team AHT | Oct 11, 2024 | Pyspark
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... by Team AHT | Oct 6, 2024 | Pyspark
When working with PySpark, there are several common issues that developers face. These issues can arise from different aspects such as memory management, performance bottlenecks, data skewness, configurations, and resource contention. Here’s a guide on troubleshooting... by Team AHT | Oct 2, 2024 | SQL
Partitioning in SQL, HiveQL, and Spark SQL is a technique used to divide large tables into smaller, more manageable pieces or partitions. These partitions are based on a column (or multiple columns) and help improve query performance, especially when dealing with... by Team AHT | Oct 2, 2024 | SAS, SQL
This is how PIVOT and UNPIVOT work in PySpark with Spark SQL using official syntax. 1. PIVOT in Spark SQL The PIVOT operation in Spark SQL allows you to convert rows into columns based on values in a specific column. It helps to summarize and reshape the data in a... by Team AHT | Sep 6, 2024 | SQL
SQL query flows through the Oracle engine in the following steps: Step 1: Parsing The SQL query is parsed to check syntax and semantics. The parser breaks the query into smaller components, such as keywords, identifiers, and literals. Step 2: Optimization The parsed... by Team AHT | Aug 29, 2024 | Pyspark
PySpark is a powerful Python API for Apache Spark, a distributed computing framework that enables large-scale data processing. Spark was initially started by Matei Zaharia at UC Berkeley’s AMPLab in 2009, and open sourced in 2010 under a BSD license. In 2013,... by Team AHT | Aug 28, 2024 | Pyspark
PySpark, as part of the Apache Spark ecosystem, follows a master-slave architecture(Or Driver- Executor Architecture) and provides a structured approach to distributed data processing. Here’s a breakdown of the PySpark architecture with diagrams to illustrate... by Team AHT | Aug 28, 2024 | Pyspark
Yup. We will discuss- Memory Management through Hadoop Traditional map reduce vs Pyspark- explained with example of Complex data pipeline used for Both. Let’s delve into a detailed comparison of memory management between Hadoop Traditional MapReduce and PySpark,... by Team AHT | Aug 26, 2024 | Pyspark
In a complex ETL (Extract, Transform, Load) environment, the spark-submit command can be customized with various options to optimize performance, handle large datasets, and configure the execution environment. Here’s a detailed example of a spark-submit command... by Team AHT | Aug 26, 2024 | Pyspark
Deploying a PySpark job can be done in various ways depending on your infrastructure, use case, and scheduling needs. Below are the different deployment methods available, including details on how to use them: 1. Running PySpark Jobs via PySpark Shell How it Works:... by Team AHT | Aug 26, 2024 | Tutorials
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... by Team AHT | Aug 26, 2024 | Pyspark
PySpark scripts can be executed in various environments and through multiple methods, each with its own configurations and settings. Here’s a detailed overview of the different ways to execute PySpark scripts: 1. Using spark-submit Command The spark-submit command is... by Team AHT | Aug 24, 2024 | Pyspark
DAG Scheduler in Spark: Detailed Explanation The DAG (Directed Acyclic Graph) Scheduler is a crucial component in Spark’s architecture. It plays a vital role in optimizing and executing Spark jobs. Here’s a detailed breakdown of its function, its place in... by Team AHT | Aug 24, 2024 | Pyspark
To determine the optimal number of CPU cores, executors, and executor memory for a PySpark job, several factors need to be considered, including the size and complexity of the job, the resources available in the cluster, and the nature of the data being processed.... by Team AHT | Aug 24, 2024 | Pyspark
In PySpark, jobs, stages, and tasks are fundamental concepts that define how Spark executes distributed data processing tasks across a cluster. Understanding these concepts will help you optimize your Spark jobs and debug issues more effectively. Let’s break down how... by Team AHT | Aug 24, 2024 | Pyspark
We know a stage in Pyspark is divided into tasks based on the partitions of the data. But Big Question is How these partions of data is decided? This post is succesor to our DAG Scheduler in Spark: Detailed Explanation, How it is involved at architecture Level. In...
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