Online Apache Spark Training in New Zealand

This instructor-led, live training in New Zealand (delivered either online or on-site) is designed for intermediate-level data scientists and engineers who wish to leverage 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 analyse large datasets efficiently with Apache Spark.
• Visualise big data within a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Stratio is a data-centric platform that brings together big data, AI, and governance into a single solution. Its Rocket and Intelligence modules enable rapid data exploration, transformation, and advanced analytics within enterprise environments.

This instructor-led, live training (available online or on-site) is designed for intermediate-level data professionals who want to effectively use the Rocket and Intelligence modules in Stratio with PySpark, with a focus on looping structures, user-defined functions, and advanced data logic.

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

• Navigate and work within the Stratio platform using the Rocket and Intelligence modules.
• Apply PySpark in the context of data ingestion, transformation, and analysis.
• Use loops and conditional logic to control data workflows and feature engineering tasks.
• Create and manage user-defined functions (UDFs) for reusable data operations in PySpark.

Course Format

• Interactive lectures and discussions.
• Plenty of exercises and hands-on practice.
• Live-lab implementation in a real-world environment.

Course Customisation Options

• To request a customised training session for this course, please contact us to arrange.

This instructor-led, live training in New Zealand (available online or on-site) is tailored for developers seeking to use and integrate Spark, Hadoop, and Python to process, analyse, and transform large and complex data sets.

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

• Set up the necessary environment to begin processing big data using Spark, Hadoop, and Python.
• Understand the key features, core components, and architecture of both Spark and Hadoop.
• Learn how to integrate Spark, Hadoop, and Python for efficient big data processing.
• Explore tools within the Spark ecosystem, including Spark MLlib, Spark Streaming, Kafka, Sqoop, and Flume.
• Build collaborative filtering recommendation systems similar to those used by Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.

This instructor-led, live training in New Zealand (available online or on-site) is designed for system administrators at beginner to intermediate levels who wish to deploy, maintain, and optimise Spark clusters.

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

• Install and configure Apache Spark across various environments.
• Manage cluster resources and monitor Spark applications.
• Optimise the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

In this instructor-led, live training in New Zealand, participants will learn how to combine Python and Spark to analyse big data through hands-on exercises.

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

• Learn how to use Spark with Python to analyse Big Data.
• Work on exercises that mirror real-world scenarios.
• Apply various tools and techniques for big data analysis using PySpark.

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

This instructor-led, live training in New Zealand (online or on-site) is intended for engineers who wish to set up and deploy the Apache Spark system to process extremely large volumes of data.

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

• Install and configure Apache Spark.
• Efficiently process and analyse very large data sets.
• Understand the differences between Apache Spark and Hadoop MapReduce, and when to use each.
• Integrate Apache Spark with other machine learning tools.

The learning curve for Apache Spark can be steep at the outset, requiring significant effort before seeing initial returns. This course is designed to help learners navigate that challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, gain proficiency in both Python and Scala APIs, and develop a solid understanding of executors and tasks. Emphasising best practices, the course places strong focus on cloud deployment, particularly with Databricks and AWS. Students will also explore the key differences between AWS EMR and AWS Glue, one of AWS's latest Spark services.

AUDIENCE:

Data Engineers, DevOps professionals, Data Scientists