Administration of Apache Spark Training Course

This instructor-led live training in Norway (online or onsite) is designed for intermediate-level data scientists and engineers who wish to utilise Google Colab and Apache Spark for big data processing and analytics.

By the end of this training, participants will be able to:

• Set up a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

This instructor-led live training (available online or onsite) is designed for data engineers, analysts, and professionals who wish to use Databricks and PySpark to build scalable data pipelines and migrate existing SQL workflows.

This training offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by leveraging distributed computing principles.

This intensive three-day course is dedicated to constructing and fine-tuning high-efficiency data processing workloads within Kubernetes-based environments, leveraging the combined power of PySpark, Pandas, and Polars.

Learners will cultivate a hands-on grasp of how Spark applications function on Kubernetes, exploring how specific configuration choices directly impact performance, scalability, resource utilization, and operational costs. The curriculum delves into critical optimization domains such as executor sizing, memory allocation, dynamic resource allocation, partitioning strategies, shuffle mechanics, the small-file challenge, and the efficient handling of Parquet data.

Additionally, the course tackles typical hurdles encountered when using Pandas, such as memory constraints and out-of-memory errors, while introducing Polars as a robust, high-performance alternative for specific processing tasks. Through practical, hands-on exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration approaches, and implement optimization techniques in realistic ETL and machine learning contexts.

The core focus of the course remains on practical decision-making: mastering the ability to pinpoint bottlenecks, choose the right tools, configure Spark effectively, and strike a balance between performance and infrastructure resource usage and cost.

This instructor-led live training in Norway (online or on-site) is designed for engineers aiming to set up and deploy Apache Spark for processing very large data sets.

By the end of this training, participants will be able to:

• Install and configure Apache Spark.
• Process and analyze massive data sets rapidly.
• Differentiate between Apache Spark and Hadoop MapReduce and know when to use each.
• Integrate Apache Spark with external machine learning tools.

In this instructor-led, live training held in Norway, participants will learn to integrate Python and Spark for big data analysis through hands-on exercises.

By the end of this training, participants will be able to:

• Utilize Spark with Python to analyze Big Data.
• Complete exercises that mimic real-world cases.
• Employ different tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Norway (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.

By the end of this training, participants will be able to:

• Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
• Understand the features, core components, and architecture of Spark and Hadoop.
• Learn how to integrate Spark, Hadoop, and Python for big data processing.
• Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
• Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.