Online or onsite, instructor-led live PySpark training courses demonstrate through hands-on practice how to use Python and Spark together to analyze big data.
PySpark training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live PySpark training can be carried out locally on customer premises in Trojmiasto or in NobleProg corporate training centers in Trojmiasto.
NobleProg -- Your Local Training Provider
Gdynia
Hotel Nadmorski, Ejsmonda 2, Gdynia, Poland, 81-409
The training room is located just 3 kilometers from the PKP/PKS Station in Gdynia, making it easily accessible for participants traveling by train or bus. Additionally, it is only 400 meters away from the bus stop, facilitating access even for those using public transportation. It is equipped with necessary training tools such as a projector, screen, and flipchart, providing comfortable conditions for both participants and the trainer.
Gdańsk
Hotel Fahrenheit, Grodzka 19, Gdańsk, Poland, 80-841
The training room is located in the very heart of the picturesque Gdansk Old Town, making the surroundings not only inspiring but also exceptionally attractive for participants. Within close proximity, you can find the railway and bus stations, facilitating arrival for those traveling by both train and bus. Additionally, the airport and port are also within reach, making this location convenient for individuals coming from distant places, both domestically and internationally.
This instructor-led live training in Trojmiasto covers the Stratio platform, focusing on the Rocket and Intelligence modules with PySpark. Participants will master data ingestion, transformation, and advanced analytics, gaining practical skills in loops, UDFs, and enterprise data workflows.
In this instructor-led, live training in Trojmiasto, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.
By the end of this training, participants will be able to:
Learn how to use Spark with Python to analyze Big Data.
Work on exercises that mimic real world cases.
Use different tools and techniques for big data analysis using PySpark.
This instructor-led, live training (online or onsite) is aimed at data engineers, data analysts, and data professionals who wish to use Databricks and PySpark to build scalable data pipelines and migrate existing SQL workflows.
This training provides a practical introduction to building scalable data processing and Machine Learning workflows using PySpark. Participants learn how Apache Spark operates within modern Big Data ecosystems and how to efficiently process large datasets using distributed computing principles.
This three-day practical course focuses on building and optimising efficient data-processing workloads using PySpark, Pandas and Polars in Kubernetes-based environments.
Participants will develop a practical understanding of how Spark applications execute on Kubernetes and how application-level configuration decisions influence performance, scalability, resource consumption and cost. The course covers key optimisation areas including executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle behaviour, small-file problems and efficient Parquet processing.
The course also addresses common challenges when working with Pandas, including memory limitations and out-of-memory failures, and introduces Polars as a high-performance alternative for selected data-processing workloads. Through hands-on exercises, participants will diagnose performance and memory issues, compare different configuration strategies and apply optimisation techniques to realistic ETL and machine learning scenarios.
The emphasis throughout the course is on practical decision-making: understanding how to identify bottlenecks, select the appropriate tool, configure Spark efficiently and balance performance with infrastructure resource consumption and cost.
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