Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in New Zealand (available online or on-site) is tailored for advanced-level data professionals who aim to optimise data processing workflows, ensure data integrity, and implement robust data lakehouse solutions capable of managing the complexities of modern big data applications.

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

• Gain a comprehensive understanding of Iceberg's architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance across various environments and integrate it with multiple data processing engines.
• Manage large-scale Iceberg tables, execute complex schema changes, and handle partition evolution.
• Master techniques to optimise 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 New Zealand (online or on-site) is tailored for beginner-level data professionals seeking 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 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.

Technological advances and the exponential growth of information are reshaping how business is conducted across many sectors, including the public sector. Driven by the rapid proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals, government data generation and digital archiving rates are climbing. As digital information expands and grows more complex, so too do the challenges of managing, processing, storing, securing, and disposing of it. Emerging tools for capture, search, discovery, and analysis are enabling organisations to extract valuable insights from unstructured data. The government sector stands at a critical juncture, recognising that information is a strategic asset. To better serve the public and meet mission requirements, government bodies must protect, leverage, and analyse both structured and unstructured information. As leaders strive to evolve into data-driven organisations capable of successfully fulfilling their missions, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions will emerge from a convergence of the most disruptive technologies:

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

Big Data stands as one of the key intelligent industry solutions, empowering government to make better decisions by acting on patterns revealed through the analysis of vast data volumes—both related and unrelated, structured and unstructured.

However, achieving these outcomes requires far more than merely 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.

Taking a step towards helping agencies identify these technologies, the White House established the National Big Data Research and Development Initiative in 2012. This initiative included over $200 million in funding to maximise the benefits of the Big Data explosion and the tools required to analyse it.

The challenges posed by Big Data are nearly as daunting as the promise it offers is encouraging. Efficient data storage is one such challenge. As always, budgets remain tight, meaning agencies must minimise the per-megabyte cost of storage while ensuring data remains easily accessible so users can retrieve it when needed and in the format they require. Backing up massive quantities of data further heightens this challenge.

Effectively analysing the data presents another major hurdle. Many agencies employ commercial tools that enable them to sift through mountains of data, identifying trends that can improve operational efficiency. (A recent study by MeriTalk found that federal IT executives believe Big Data could help agencies save more than $500 billion while simultaneously fulfilling mission objectives.)

Custom-developed Big Data tools are also enabling agencies to meet their data analysis needs. For instance, the Oak Ridge National Laboratory's Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has assisted medical researchers in identifying links that can alert doctors to aortic aneurysms before they strike. It is also employed for more routine tasks, such as screening resumes to connect job candidates with hiring managers.

Participants who complete this instructor-led, live training in New Zealand will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.

Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.

The course begins with an introduction to the fundamental concepts of Big Data, then progresses to the programming languages and methodologies used to perform Data Analysis. Finally, we explore the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.

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

This instructor-led, live training (available online or on-site) is designed for 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 both 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.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice opportunities.
• Hands-on implementation within a live-lab environment.

Course Customisation Options

• To request a customised training session 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 Customisation Options

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

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.

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.

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

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 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.

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.