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The PySpark Development course is designed to equip learners with the skills necessary to harness the power of Apache Spark using the Python API, PySpark. This comprehensive PySpark certification course delves into the fundamentals and advanced features of PySpark, enabling data professionals to process large-scale data efficiently.
Starting with Module 1, students receive a thorough primer on PySpark, exploring the Spark ecosystem, execution processes, and the latest features. As learners progress, they build foundational knowledge of resilient distributed datasets (RDDs) in Module 2, understanding their creation, transformations, and actions in Module 3. Module 4 introduces DataFrames, a powerful abstraction in Spark for structured data processing, along with various DataFrame transformations. Module 5 then focuses on advanced data processing techniques with Spark DataFrames.
Upon completion of this pyspark certification, participants will be proficient in developing scalable data processing pipelines in PySpark, setting a foundation for tackling complex data challenges in real-world scenarios.
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Classroom Training price is on request
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Classroom Training price is on request
USD 199+
USD 19+
USD 59+
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Flexi FAQ'sFlexi Demo Video
To ensure a successful learning experience in the PySpark Development course, the following prerequisites are recommended:
These prerequisites are intended to provide a foundation that will help students grasp the course material more effectively. However, the course is designed to accommodate learners with varying skill levels, and instructors will guide students through the complexities of PySpark development.
PySpark Development is a course designed to educate professionals on distributed data processing using Apache Spark with Python.
In this PySpark Development course, students will gain practical skills in data processing with PySpark, understanding RDDs, DataFrames, and performance optimization techniques.
map
, filter
, flatMap
, distinct
, sample
, join
, and repartition
to process large datasets.collect
, reduce
, count
, foreach
, aggregate
, and save
to extract results and perform aggregations.