Tag: big_data_interview
How to Manage Dependencies in AWS Glue Jobs
AWS Glue empowers organizations to build robust data pipelines for ETL (Extract, Transform, Load) tasks in the cloud. However, as…
AWS Glue’s Integration with Amazon Athena and Amazon Redshift
AWS Glue, a fully managed extract, transform, and load (ETL) service, plays a pivotal role in orchestrating data workflows. Let’s…
PySpark : Getting int representing the number of array dimensions
In the realm of data analysis and manipulation with Pandas API on Spark, understanding the structure of data arrays is…
PySpark : Creation of data series with customizable parameters
Series() enables users to create data series akin to its Pandas counterpart. Let’s delve into its functionality and explore practical…
PySpark : generate fixed frequency TimedeltaIndex
timedelta_range() stands out, enabling users to effortlessly generate fixed frequency TimedeltaIndex. Let’s explore its intricacies and applications through practical examples….
Spark : Converting argument into a timedelta object
to_timedelta(), proves invaluable for handling time-related data. Let’s delve into its workings and explore its utility with practical examples. Understanding…
Duplicate Removal in PySpark
Duplicate rows in datasets can often skew analysis results and compromise data integrity. PySpark, a powerful Python library for big…
Handling Complex Transformations in AWS Glue Scripts
AWS Glue provides powerful capabilities for orchestrating extract, transform, and load (ETL) workflows in the cloud. However, handling complex transformations…
Dynamic vs. Static Frames in AWS Glue
AWS Glue, a fully managed extract, transform, and load (ETL) service, offers two distinct types of frames: dynamic and static….
PySpark with Pandas API : How to generates a fixed frequency DatetimeIndex : date_range()
In PySpark, the Pandas API offers powerful functionalities for working with time series data. One such function is date_range(), which…
