Big Data Training in Durban

This instructor-led live training in Durban (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use 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 analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

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

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

By the conclusion 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.
• Use loops and conditional logic to manage data workflows and feature engineering tasks.
• Create and manage user-defined functions (UDFs) for reusable data operations in PySpark.

Format of the Course

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

Course Customization Options

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

Data Warehousing involves the design, construction, and operation of centralized data repositories to facilitate analytics, reporting, and informed decision-making.

This instructor-led live training, available either online or on-site, is tailored for intermediate-level data professionals seeking to master dimensional data modelling, construct resilient ETL pipelines, and optimise analytical workloads.

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

• Articulate core data warehousing concepts and architectural principles.
• Design dimensional models and select appropriate star or snowflake schemas.
• Develop and orchestrate reliable ETL and ELT pipelines.
• Distinguish between OLTP and OLAP workloads and optimise for analytical performance.

Course Format

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

Course Customisation Options

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

Participants who complete this instructor-led, live training in Durban 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 starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.

Greenplum is an open-source massively parallel processing (MPP) data platform specifically designed for analytics and large-scale data environments.

This instructor-led, live training (available online or onsite) is targeted at intermediate to advanced technical users who wish to gain a deep understanding of Greenplum's internal architecture, build optimised data models, and apply high-performance SQL within distributed environments.

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

• Grasp Greenplum’s shared-nothing architecture and parallelism model.
• Design distributed table structures and partitions optimised for query performance.
• Apply indexing strategies, storage formats, and physical design techniques.
• Interpret EXPLAIN plans and optimise queries using established best practices.

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical sessions.
• Hands-on implementation in a live-lab environment.

Course Customisation Options

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

Greenplum is an open-source massively parallel data platform based on PostgreSQL, designed for advanced analytics and large-scale data warehousing.

This instructor-led, live training (online or onsite) is aimed at intermediate-level administrators who wish to install, configure, and manage Greenplum environments effectively, including managing system updates and libraries.

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

• Install and configure a production-ready Greenplum cluster.
• Manage system updates and apply patches safely.
• Handle Greenplum library management for analytics and extensions.
• Troubleshoot and monitor Greenplum systems.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Greenplum in a controlled lab environment.
• Guided exercises focused on cluster setup, upgrade workflows, and system extensions.

Course Customization Options

• To request a customized training for this course based on your environment or data platform, please contact us to arrange.

This instructor-led, live training in Durban (online or onsite) is aimed at advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can handle 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 Durban (online or onsite) is tailored for beginner-level data professionals who wish to acquire the knowledge and skills necessary 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 Durban (online or onsite) is designed for IT professionals with intermediate-level skills who seek to refine their capabilities in data architecture, governance, cloud computing, and big data technologies. The goal is to equip them to effectively manage and analyse large datasets for data migration within their respective organisations.

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

• Grasp the foundational concepts and components of diverse data architectures.
• Acquire a thorough understanding of data governance principles and their significance within regulatory environments.
• Deploy and oversee data governance frameworks, including DAMA and TOGAF.
• Utilise cloud platforms to facilitate efficient data storage, processing, and management.

This instructor-led, live training in Durban (online or onsite) is designed for intermediate-level data engineers who wish to learn how to use Azure Data Lake Storage Gen2 for effective data analytics solutions.

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

• Understand the architecture and key features of Azure Data Lake Storage Gen2.
• Optimise data storage and access for cost and performance.
• Integrate Azure Data Lake Storage Gen2 with other Azure services for analytics and data processing.
• Develop solutions using the Azure Data Lake Storage Gen2 API.
• Troubleshoot common issues and optimise storage strategies.

This instructor-led, live training in Durban (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.

Big Data and Advanced Analytics involves the application of 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.

This instructor-led, live training in Durban (online or on-site) is designed for intermediate-level IT professionals who aim to gain a comprehensive understanding of IBM DataStage from both administrative and developmental perspectives. This equips them to manage and utilise the tool effectively in their respective workplaces.

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

• Understand the core concepts of DataStage.
• Learn how to effectively install, configure, and manage DataStage environments.
• Connect to various data sources and extract data efficiently from databases, flat files, and external sources.
• Implement effective data loading techniques.

This live, instructor-led training in Durban (online or at your premises) is designed for system administrators with beginner to intermediate skills who want to deploy, maintain, and optimise Spark clusters.

At 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 Spark cluster performance.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

In this instructor-led, live training in Durban, 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 (available online or on-site) is designed for software developers, managers, and business analysts who wish to use big data systems to store and retrieve large amounts of data.

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

• Query large amounts of data efficiently.
• Understand how Big Data systems store and retrieve data.
• Use the latest big data systems available.
• Wrangle data from data systems into reporting systems.
• Learn to write SQL queries in:
  • MySQL.
  • Postgres.
  • Hive Query Language (HiveQL/HQL).
  • Redshift.

Format of the Course

• Interactive lecture and discussion.
• Extensive 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.

Technological advancements and the exponential growth of information are reshaping business operations across numerous sectors, including government. The volume of government data generation and digital archiving is surging, driven by the proliferation of mobile devices and applications, smart sensors and IoT devices, 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 grow increasingly challenging. 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 information as a strategic asset. Governments must protect, leverage, and analyse both structured and unstructured data to enhance service delivery and meet mission requirements. As government leaders strive to evolve into data-driven organisations, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions will emerge from 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 bodies to make better decisions by acting on patterns revealed through the analysis of large volumes of data—both related and unrelated, structured and unstructured.

However, achieving these feats 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 by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to maximise the potential of the Big Data explosion and the tools required to analyse it.

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

Effective data analysis presents another major hurdle. Many agencies utilise commercial tools that allow them to sift through mountains of data, identifying trends that enhance operational efficiency. (A recent MeriTalk study found that federal IT executives believe Big Data could help agencies save over $500 billion while also fulfilling mission objectives.)

Custom-developed Big Data tools are also enabling agencies to address 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. This system has helped medical researchers identify links that can alert doctors to aortic aneurysms before they occur. It is also employed for more routine tasks, such as sifting through resumes to connect job candidates with hiring managers.

In this instructor-led, live training in Durban, participants will develop the mindset required to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies to identify criminal activity and prevent crime. We will examine case studies from law enforcement organisations worldwide to gain insights into their adoption strategies, challenges, and results.

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

• Integrate Big Data technology with traditional data gathering processes to construct a narrative during investigations.
• Implement industrial-grade Big Data storage and processing solutions for data analysis.
• Prepare a proposal for the adoption of the most appropriate tools and processes to enable a data-driven approach to criminal investigation.

Big Data encompasses solutions designed for the storage and processing of extensive data sets. Originally developed by Google, these Big Data frameworks have evolved and spurred the creation of numerous similar open-source projects. R has established itself as a widely adopted programming language within the financial sector.

Course Overview

This programme offers a hands-on and structured introduction to constructing real-time data streaming systems. It explores essential concepts, architectural patterns, and industry-standard tools required to process continuous data at scale. Participants will gain the skills to design, implement, and optimise streaming pipelines using contemporary frameworks. The curriculum advances from foundational principles to practical applications, empowering learners to confidently develop production-ready real-time solutions.

Format of Training

• Instructor-led sessions with guided explanations

• Concept walkthroughs with real-world examples

• Hands-on demonstrations and coding exercises •

• Progressive labs aligned with daily topics

• Interactive discussions and Q&A • •

Course Objectives

• Understand real-time data streaming concepts and system architecture

• Differentiate between batch and streaming data processing models •

• Design scalable and fault-tolerant streaming pipelines •

• Work with distributed streaming tools and frameworks •

• Apply event time processing, windowing, and stateful operations •

• Build and optimise real-time data solutions for business use cases •

Apache Hadoop stands as the premier framework for processing Big Data across server clusters. Over the course of this three-day program—with an optional fourth day—participants will explore the business advantages and practical applications of Hadoop and its surrounding ecosystem. The curriculum covers cluster deployment planning and scalability, alongside the installation, maintenance, monitoring, troubleshooting, and optimization of Hadoop environments. Attendees will also engage in bulk data loading exercises, become acquainted with various Hadoop distributions, and gain hands-on experience in installing and managing ecosystem tools. The course concludes with an in-depth discussion on securing clusters using Kerberos.

“The materials were meticulously prepared and covered comprehensively. The lab sessions were highly beneficial and well-structured.”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Target Audience

Hadoop systems administrators

Delivery Format

A blend of theoretical lectures and practical hands-on labs, with an approximate distribution of 60% lectures and 40% lab work.

Informatica with Big Data (BDM) is a programme designed to equip data professionals with the skills to develop, manage, and analyse large datasets by leveraging the most recent technologies and architectures in the Big Data sector. The curriculum covers the entire lifecycle, from data ingestion, integration, cleansing, and curation to data analytics, as well as the exposure and consumption of big data services.
Participants will explore solutions that process extensive datasets using Big Data technologies and architectures such as Apache Hive, Apache Hadoop, and Apache Spark. The course also offers hands-on experience with Informatica tools like Bloombox, Big Data Management, and iData Fabric, providing a practical understanding of big data concepts like Map Reduce and Hadoop. Upon completion, learners will be capable of creating end-to-end data solutions using Informatica and its associated Big Data offerings.

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 optimize 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.
• Optimize 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 in Durban, participants will learn the fundamentals of flow-based programming as they develop a number 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 course offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and how to efficiently manage large datasets by applying distributed computing principles.

This instructor-led, live training in Durban (online or onsite) is aimed at engineers who wish to set up and deploy an 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.

The initial learning curve for Apache Spark is steep, requiring significant effort before yielding results. 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, and become proficient with both Python and Scala APIs. They will also gain a solid understanding of executors, tasks, and other core concepts. Aligned with industry best practices, the course places a strong emphasis on cloud deployment, specifically within Databricks and AWS environments. Students will also learn to differentiate between AWS EMR and AWS Glue, one of AWS’s latest Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

Spark SQL serves as Apache Spark's dedicated module for processing both structured and unstructured data. It offers insights into data structure alongside the computations being executed, information that can be leveraged to optimise performance. Spark SQL is commonly utilised for two primary purposes:
- executing SQL queries;
- reading data from an existing Hive installation.

During this instructor-led live training (available onsite or remotely), participants will gain the skills necessary to analyse various datasets using Spark SQL.

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

• Install and configure Spark SQL.
• Conduct data analysis using Spark SQL.
• Query datasets in diverse formats.
• Visualise data and query outcomes.

Format of the Course

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

Course Customization Options

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

This instructor-led, live training in Durban (online or onsite) is designed for technical professionals who want to deploy Talend Open Studio for Big Data to simplify the process of reading and processing big data.

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

• Install and configure Talend Open Studio for Big Data.
• Connect with big data systems such as Cloudera, HortonWorks, MapR, Amazon EMR, and Apache.
• Understand and set up the big data components and connectors within Open Studio.
• Configure parameters to automatically generate MapReduce code.
• Use Open Studio's drag-and-drop interface to execute Hadoop jobs.
• Prototype big data pipelines.
• Automate big data integration projects.