FabricOfData

Output the employee details whose salary is second highest in their department. Note, Include all employees tied for second place Input Data Tap For Closer Look …

Python-PySpark Puzzles

SAS

Track customers who made a transaction greater than 50000 exactly on the 10th day after account creation. Use PySpark filtering, date difference functions, and DataFrame operations to solve this real-world query puzzl…

Python-PySpark Puzzles

SAS

Using the provided population dataset, write a PySpark query to find and list all the countries where the female population is greater than the male population. …

Python-PySpark Puzzles

SAS

Using the provided customer orders data, write a PySpark query that calculates the total number of orders placed by each customer. Additionally, in the next column, list all the distinct item names ordered by that customer, separated by…

Python-PySpark Puzzles

SAS

PySpark Puzzle: Retrieve Orders Within 50% of Highest Order Amount Retrieve the order details of each customer where the order amount of each order is within 50% of the highest ordered amount for that customer. …

Python-PySpark Puzzles

SAS

Apache Spark: Difference Between cache() and persist() Explained Difference Between cache() and persist() in Apache Spark Feature cache() persist() …

PySpark-Q&A

SAS

How to Unpersist DataFrames and RDDs in Apache Spark You can uncache a DataFrame or RDD by using the .unpersist() method. This frees up memory or other storage resources. Copy fabricofdata_d…

PySpark-Q&A

SAS

When to Avoid Caching in Apache Spark Avoid caching when: The dataset is used only once. Memory constraints exist, and caching large datasets may lead to disk spills or OutOfMemory errors. The cost of…

PySpark-Q&A

SAS

We use caching and persisting to improve performance and execution time. When a dataset is used multiple times, these methods help Spark avoid recomputing the same data repeatedly. This is especially useful in complex transformation pipelines. Whil…

PySpark-Q&A

SAS

Caching in Spark is a method used to store intermediate results of a DataFrame or RDD in memory, which helps avoid recomputation during future transformations. In PySpark, calling .cache() stores the data u…

PySpark-Q&A

SAS

In PySpark, cache() and persist() are powerful optimization techniques used with DataFrames, Datasets, and RDDs. These methods allow you to store intermediate results in memory or other storage levels, reducing…

pySpark

SAS

Just like the SQL Server 'ORDER BY' clause, PySpark provides the orderBy() and sort() functions to sort data within RDDs and DataFrames. Since PySpark provides two functions for the same functionality, t…

pySpark

SAS

When the dataset is too large to fit entirely in memory. MEMORY_AND_DISK prevents re-computation by saving the overflow to disk.

PySpark-Q&A

SAS

Spark provides several storage levels to help control how data is cached across memory and disk. Each level offers a different trade-off between performance, memory usage, and fault tolerance. Below are the m…

PySpark-Q&A

SAS

When working with PySpark, writing code might come easy—but understanding how Spark handles data behind the scenes is just as important. Specifically, knowing how Spark stores data internally can help you wri…

pySpark

SAS