Tag: big_data_interview
Pandas API on Spark : Merging DataFrame objects with a database-style join operation : merge
Apache Spark has emerged as a powerhouse, offering unparalleled scalability and performance. Leveraging the familiar syntax of Pandas API on…
PySpark : Unpivot a DataFrame from wide format to long format : melt
Apache Spark has emerged as a dominant force in the realm of big data processing, offering unparalleled scalability and performance….
Pandas API on Spark for JSON to DataFrame Conversion : read_json()
In the realm of big data analytics, the ability to seamlessly integrate and analyze data from various sources is paramount….
Transforming Spark DataFrame to HTML Tables with Pandas API : to_html()
In the realm of big data analytics, effective data visualization is paramount for conveying insights and facilitating decision-making. While Apache…
Pandas API on Spark for HTML Table Extraction
In today’s data-driven world, extracting valuable insights from diverse sources is paramount. However, handling HTML tables efficiently within big data…
Effortless ORC Data Integration: Reading ORC Files into PySpark DataFrames
In the realm of big data processing, PySpark stands out for its ability to handle large datasets efficiently. One common…
Efficiently Managing PySpark Jobs: Submission via REST API
Apache Spark has become a go-to solution for big data processing, thanks to its robust architecture and scalability. PySpark, the…
Distinction Between dense_rank() and row_number() in PySpark
PySpark, a Python library for Apache Spark, offers a powerful set of functions for data manipulation and analysis. Two commonly…
Hive Bucketing: Concepts and Real-World Examples
Hive is a powerful data warehousing and SQL-like query language system built on top of Hadoop. It is widely used…
Understanding the Limitations of AWS Glue
AWS Glue is a fully managed extract, transform, and load (ETL) service provided by Amazon Web Services (AWS), designed to…
