Apache Spark Fundamentals Training Course

This instructor-led, live training in Thailand (online or onsite) is designed for intermediate-level data scientists and engineers looking to leverage Google Colab and Apache Spark for efficient big data processing and analytics.

By the end of the session, participants will be equipped to:

• Establish a big data environment using Google Colab and Spark.
• Process and analyze large-scale datasets effectively with Apache Spark.
• Visualize big data within a collaborative setting.
• Integrate Apache Spark with cloud-based solutions.

This live, instructor-led training, delivered online or onsite, targets data engineers, analysts, and professionals looking to utilize Databricks and PySpark to develop scalable data pipelines and transition existing SQL workflows.

This training delivers a practical entry point into developing scalable data processing and Machine Learning workflows using PySpark. Learners will understand how Apache Spark fits into modern Big Data ecosystems and how to leverage distributed computing to process large datasets efficiently.

This intensive three-day workshop is designed to help you build and fine-tune high-performance data-processing pipelines using PySpark, Pandas, and Polars within Kubernetes ecosystems.

Attendees will gain a practical grasp of Spark execution mechanics on Kubernetes, learning how application-level configuration impacts performance, scalability, resource efficiency, and operational costs. The curriculum addresses critical optimization strategies, such as executor sizing, memory management, dynamic allocation, partitioning techniques, shuffle optimization, and efficient Parquet handling, while also tackling the small-file problem.

The program also explores common pitfalls associated with Pandas, such as memory constraints and out-of-memory errors, and positions Polars as a high-performance alternative for specific workload scenarios. Through hands-on lab exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate different configuration strategies, and apply optimization techniques to real-world ETL and machine learning projects.

The core focus of the training is on practical decision-making: mastering the skills to identify performance bottlenecks, select the right tools, configure Spark effectively, and strike the ideal balance between processing performance and infrastructure resource consumption and cost.

Facilitated by an instructor, this live training session for Thailand (delivered online or onsite) targets beginner to intermediate system administrators looking to deploy, maintain, and optimize Spark clusters.

By the end of the program, participants will be capable of:

• Installing and configuring Apache Spark in multiple environments.
• Managing cluster resources and overseeing Spark application performance.
• Optimizing the efficiency of Spark clusters.
• Applying security protocols and ensuring system high availability.
• Diagnosing and fixing typical Spark-related issues.

In this live, instructor-led training in Thailand, participants will acquire the ability to use Python and Spark together for big data analysis, reinforced by hands-on exercises.

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

• Utilize Spark and Python for Big Data analysis.
• Complete exercises that reflect real-world applications.
• Employ diverse tools and techniques for big data analysis using PySpark.

This live, instructor-led training in Thailand (available online or onsite) is tailored for developers seeking to utilize and integrate Spark, Hadoop, and Python to process, analyze, and transform extensive and complex data sets.

Upon completion of this training, participants will be able to:

• Establish the necessary environment for big data processing using Spark, Hadoop, and Python.
• Gain a comprehensive understanding of the key features, core components, and architecture of both Spark and Hadoop.
• Master the integration of Spark, Hadoop, and Python for effective big data workflows.
• Explore essential tools within the Spark ecosystem, including Spark MlLib, Spark Streaming, Kafka, Sqoop, and Flume.
• Develop collaborative filtering recommendation systems comparable to those used by major platforms like Netflix, YouTube, Amazon, Spotify, and Google.
• Utilize Apache Mahout to scale machine learning algorithms.