Administration of Apache Spark Training Course

This instructor-led live training in Malaysia (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use 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 (online or onsite) targets 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 offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process vast datasets efficiently by applying distributed computing principles.

This intensive three-day practical course is dedicated to constructing and refining high-efficiency data-processing workloads leveraging PySpark, Pandas, and Polars within Kubernetes-based environments.

Learners will gain a pragmatic insight into how Spark applications function on Kubernetes and how specific application-level configuration choices impact performance, scalability, resource utilisation, and operational costs. Key optimisation domains covered include executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, the small-file problem, and efficient Parquet processing.

The curriculum also tackles frequent challenges encountered when using Pandas, such as memory constraints and out-of-memory failures, while presenting Polars as a high-performance alternative for targeted data-processing tasks. Through interactive exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration strategies, and implement optimisation techniques in realistic ETL and machine learning contexts.

Throughout the course, the focus remains on practical decision-making: mastering the ability to pinpoint bottlenecks, choose the right tool, configure Spark effectively, and strike a balance between performance, infrastructure resource consumption, and cost efficiency.

This instructor-led, live training in Malaysia (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark systems for processing very large amounts of data.

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

• Install and configure Apache Spark.
• Quickly process and analyze very large datasets.
• Understand the differences between Apache Spark and Hadoop MapReduce and when to use each.
• Integrate Apache Spark with other machine learning tools.

In this live, instructor-led training held in Malaysia, participants will explore how to combine Python and Spark for big data analysis while engaging in hands-on exercises.

By the conclusion of this program, attendees will be able to:

• Utilize Spark alongside Python to analyze Big Data.
• Practice with exercises designed to mirror real-world applications.
• Employ diverse tools and techniques for big data analysis via PySpark.

This instructor-led, live training in Malaysia (online or on-site) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyse, and transform large and complex datasets.

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.