Category: spark
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Ensuring data integrity with PySpark’s crc32 function : Cyclic redundancy checks which detect accidental changes to raw data.
One popular method of ensuring integrity is through the use of Cyclic Redundancy Checks (CRC), which detect accidental changes to…
Calculating correlation between dataframe columns with PySpark : corr
In data analysis, understanding the relationship between different data columns can be pivotal in making informed decisions. Correlation is a…
Converting numerical strings from one base to another within DataFrames : conv
The conv function in PySpark simplifies the process of converting numerical strings from one base to another within DataFrames. With…
Loading JSON schema from a JSON string in PySpark
We want to load the JSON schema from a JSON string. In PySpark, you can do this by parsing the…
Optimizing PySpark queries with adaptive query execution – (AQE) – Example included
Spark 3+ brought numerous enhancements and features, and one of the notable ones is Adaptive Query Execution (AQE). AQE is…
PySpark : Calculate the Euclidean distance or the square root of the sum of the squares of its arguments using PySpark.
In PySpark, the hypot function is a mathematical function used to calculate the Euclidean distance or the square root of…
PySpark : How to perform compute covariance using covar_pop and covar_samp with PySpark
Covariance is a statistical measure that indicates the extent to which two variables change together. If the variables increase and…
Spark repartition() vs coalesce() – A complete information
In PySpark, managing data across different partitions is crucial for optimizing performance, especially for large-scale data processing tasks. Two methods…
Grouping and aggregating multi-column data with PySpark – Complete example included
The groupBy function is widely used in PySpark SQL to group the DataFrame based on one or multiple columns, apply…
Aggregating Insights: A deep dive into the fold function in PySpark with practical examples
Understanding spark RDDs RDDs are immutable, distributed collections of objects, and are the backbone of Spark. RDDs enable fault-tolerant parallel…
