Data Warehousing: Concepts, Design, and Implementation Training Course

This instructor-led, live training in Australia (online or onsite) is tailored for advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions capable of handling the complexities of modern big data applications.

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

• Gain an in-depth understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance in various environments and integrate it with multiple data processing engines.
• Manage large-scale Iceberg tables, perform complex schema changes, and handle partition evolution.
• Master techniques to optimize query performance and data scan efficiency for large datasets.
• Implement mechanisms to ensure data consistency, manage transactional guarantees, and handle failures in distributed environments.

This instructor-led live training in Australia (online or onsite) is tailored for beginner-level data professionals who want to acquire the knowledge and skills needed to effectively utilise Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimising data processing workflows.

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

• Gain a thorough understanding of Apache Iceberg's architecture, features, and benefits.
• Learn about table formats, partitioning, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg in different environments.
• Create, manage, and manipulate Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

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.

Advancements in technology and the exponential growth of data are reshaping business operations across various sectors, including the government sector. Government agencies are experiencing a surge in data generation and digital archiving, driven by the rapid proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information becomes more expansive and complex, the management, processing, storage, security, and disposition of this data become increasingly intricate. New tools for capture, search, discovery, and analysis are enabling organisations to extract valuable insights from unstructured data. The government sector is at a critical juncture, recognising that information is a strategic asset. Governments must protect, leverage, and analyse both structured and unstructured information to better serve citizens and meet mission objectives. As government leaders strive to evolve into data-driven organisations to successfully achieve their missions, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions are being developed through the integration of the most disruptive technologies:

• Mobile devices and applications
• Cloud services
• Social business technologies and networking
• Big Data and analytics

Big Data represents an intelligent industry solution that enables government entities to make better decisions by taking action based on patterns revealed through the analysis of large volumes of data—both related and unrelated, structured and unstructured.

However, achieving these outcomes requires far more than simply accumulating massive quantities of data. "Making sense of these volumes of Big Data requires cutting-edge tools and technologies that can analyse and extract useful knowledge from vast and diverse streams of information," wrote Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy in a post on the OSTP Blog.

The White House took a significant step toward helping agencies identify these technologies when it established the National Big Data Research and Development Initiative in 2012. This initiative included more than $200 million to maximise the potential of the Big Data explosion and the tools needed to analyse it.

The challenges posed by Big Data are nearly as daunting as its promise is encouraging. Efficiently storing data is one such challenge. With budgets always tight, agencies must minimise the per-megabyte cost of storage and keep data readily accessible so users can retrieve it when and how they need it. Backing up massive quantities of data further heightens this challenge.

Effectively analysing data is another major challenge. Many agencies employ commercial tools that enable them to sift through mountains of data, spotting trends that help them operate more efficiently. A recent study by MeriTalk found that federal IT executives believe Big Data could help agencies save more than $500 billion while also fulfilling mission objectives.

Custom-developed Big Data tools are also allowing agencies to address their need for data analysis. For example, Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers identify a link that can alert doctors to aortic aneurysms before they occur. It is also used for more routine tasks, such as sifting through resumes to connect job candidates with hiring managers.

Upon completing this instructor-led, live training in Australia, participants will acquire a practical, real-world understanding of Big Data, alongside its associated technologies, methodologies, and tools.

Attendees will have the opportunity to apply this knowledge through hands-on exercises. Group interaction and feedback from the instructor form a crucial part of the learning experience.

The course begins with an overview of fundamental Big Data concepts, then moves on to the programming languages and methodologies employed for Data Analysis. Finally, we explore the tools and infrastructure that support Big Data storage, Distributed Processing, and Scalability.

Big Data and Advanced Analytics involves applying sophisticated techniques and tools to analyse large, complex datasets for actionable insights and strategic decision-making.

This instructor-led, live training (online or onsite) is aimed at advanced-level data professionals who wish to leverage cutting-edge analytical methods and big data technologies for predictive, prescriptive, and real-time analytics.

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

• Design and implement large-scale data processing pipelines for structured and unstructured data.
• Apply advanced machine learning and deep learning techniques to massive datasets.
• Leverage distributed computing frameworks for real-time analytics and data streaming.
• Integrate big data analytics into business intelligence and decision-making systems.

Format of the Course

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

Course Customization Options

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

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.

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.

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.