Programming with Big Data in R Training Course

This instructor-led, live training in Australia (online or onsite) is aimed at intermediate-level advanced R users who wish to use R to build faster workflows, improve code quality, and handle more complex analysis tasks.

By the end of this training, participants will be able to: create reusable functions, improve data workflows, debug and optimize code, and produce reproducible reports.

This instructor-led live training in Australia (online or onsite) is designed for business analysts wishing to automate trading using algorithmic strategies, Python, and R.

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

• Use algorithms to rapidly buy and sell securities at specific increments.
• Reduce trading costs by employing algorithmic trading techniques.
• Automatically monitor stock prices and execute trades.

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

Upon completing 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 in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. This course, spanning three days (with an optional fourth day), enables attendees to understand the business advantages and practical applications of Hadoop and its ecosystem. Participants will learn to plan for cluster deployment and expansion, as well as to install, maintain, monitor, troubleshoot, and optimise Hadoop. Hands-on practice includes bulk data loading, exploring various Hadoop distributions, and installing and managing Hadoop ecosystem tools. The course concludes with a discussion on securing clusters using Kerberos.

“...The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organised.”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop administrators

Format

A mix of lectures and hands-on labs, with an approximate balance of 60% lectures and 40% labs.

Apache NiFi is an open-source, flow-based data integration and event-processing platform. It enables automated, real-time data routing, transformation, and system mediation between disparate systems, with a web-based UI and fine-grained control.

This instructor-led, live training (onsite or remote) is aimed at intermediate-level administrators and engineers who wish to deploy, manage, secure, and optimise NiFi dataflows in production environments.

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

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows from varied sources and sinks.
• Implement flow automation, routing, and transformation logic.
• Optimise performance, monitor operations, and troubleshoot issues.

Format of the Course

• Interactive lecture with real-world architecture discussion.
• Hands-on labs: building, deploying, and managing flows.
• Scenario-based exercises in a live-lab environment.

Course Customization Options

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

In this instructor-led live training held in Australia, participants will learn the fundamentals of flow-based programming while developing a series of demo extensions, components, and processors using Apache NiFi.

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

• Understand NiFi's architecture and dataflow concepts.
• Develop extensions using NiFi and third-party APIs.
• Custom develop their own Apache NiFi processor.
• Ingest and process real-time data from disparate and uncommon file formats and data sources.

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 instructor-led, live training in Australia (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark system 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 analyse very large data sets.
• Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
• Integrate Apache Spark with other machine learning tools.

This instructor-led, live training in Australia (online or onsite) is aimed at beginner-level to intermediate-level system administrators who wish to deploy, maintain, and optimize Spark clusters.

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

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

The initial learning curve for Apache Spark can be steep, requiring significant effort before seeing tangible results. This course is designed to help you navigate that challenging first phase effectively. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and become proficient with both the Python and Scala APIs. You will also gain a deep understanding of executors, tasks, and other core concepts. Emphasising best practices, the curriculum places strong focus on cloud deployment strategies, specifically within Databricks and AWS environments. Students will learn to differentiate between AWS EMR and AWS Glue, with AWS Glue being highlighted as one of the latest Spark services offered by AWS.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Australia, participants will learn how to use Python and Spark together to analyse 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 analyse 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 in Australia (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.

Spark SQL is the Apache Spark module designed for interacting with structured and unstructured data. It provides metadata regarding data structure and the computations being executed, enabling various performance optimisations. Spark SQL is commonly used to:
- execute SQL queries.
- read data from an existing Hive installation.

During this instructor-led live training (available onsite or remotely), participants will learn how to analyse diverse data sets using Spark SQL.

Upon completing this training, participants will be able to:

• Install and configure Spark SQL.
• Perform data analysis using Spark SQL.
• Query data sets in various formats.
• Visualise data and query results.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

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

Stratio serves as a data-centric platform that unifies big data, artificial intelligence, and governance within a single solution. Its Rocket and Intelligence modules empower organisations to conduct rapid data exploration, transformation, and advanced analytics in enterprise settings.

This instructor-led live training, available online or onsite, is designed for intermediate-level data professionals looking to effectively utilise the Rocket and Intelligence modules in Stratio with PySpark. The course focuses on looping structures, user-defined functions, and advanced data logic.

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

• Navigate and operate within the Stratio platform using the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis.
• Employ loops and conditional logic to manage data workflows and feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to facilitate reusable data operations in PySpark.

Course Format

• Interactive lectures and group discussions.
• Ample exercises and practical practice.
• Hands-on implementation within a live-lab environment.

Course Customisation Options

• To request customised training for this course, please contact us to make arrangements.