Data Streaming and Real Time Data Processing Training Course

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

Upon completion 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 across 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 Botswana (online or onsite) is aimed at beginner-level data professionals who wish to acquire the knowledge and skills necessary to effectively utilize Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing 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 Botswana (online or onsite) is designed for data scientists and engineers at an intermediate level who wish to utilise Google Colab and Apache Spark for big data processing and analytics.

Upon completing this training, participants will be able to:

• Establish 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 advancements and the exponential growth of information are fundamentally transforming business operations across numerous industries, including the public sector. The rate of government data generation and digital archiving is accelerating, driven by the rapid proliferation of mobile devices and applications, smart sensors and IoT devices, cloud computing solutions, and citizen-facing digital portals. As digital information expands in volume and complexity, the challenges associated with information management, processing, storage, security, and disposition become increasingly sophisticated. New tools for capture, search, discovery, and analysis are enabling organizations to derive valuable insights from unstructured data. The government sector is reaching a tipping point, recognizing information as a strategic asset. Governments must now protect, leverage, and analyze both structured and unstructured data to better serve citizens and meet mission requirements. As government leaders strive to evolve into data-driven organizations, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions are emerging from a combination of the most disruptive technologies:

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

Big Data is one such intelligent industry solution that empowers government entities to make better decisions by taking action based on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these capabilities requires more than simply accumulating massive quantities of data. "Making sense of these volumes of Big Data requires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information," Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy noted 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 maximize the potential of the Big Data explosion and the tools necessary to analyze it.

The challenges posed by Big Data are nearly as daunting as its promise is encouraging. One significant challenge is storing data efficiently. With budgets often tight, agencies must minimize the per-megabyte cost of storage while ensuring data remains easily accessible so users can retrieve it when and how they need it. Additionally, backing up massive amounts of data compounds this challenge.

Effectively analyzing data is another major hurdle. Many agencies utilize commercial tools to sift through vast amounts 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 allowing 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 used for more routine tasks, such as sifting through resumes to connect job candidates with hiring managers.

Participants who complete this instructor-led, live training in Botswana 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.

Informatica with Big Data (BDM) is a specialized course aimed at empowering data professionals to develop, manage, and analyze large datasets by leveraging the most current technologies and architectures in the Big Data domain. The course covers the entire lifecycle, from data ingestion, integration, cleansing, and curation, through to data analytics and the exposure and consumption of big data services.
In this course, participants will explore solutions that process large datasets using Big Data technologies and architectures such as Apache Hive, Apache Hadoop, and Apache Spark. The course also provides hands-on experience with Informatica tools like Bloombox, Big Data Management, and iData Fabric, facilitating a deeper understanding of big data technologies like MapReduce and Hadoop. By the conclusion of this course, learners will be equipped to create 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.

This course offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process extensive datasets effectively by applying distributed computing principles.

This instructor-led, live training in Botswana (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 analyze 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 Botswana (online or onsite) is aimed at beginner to intermediate system administrators 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 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 considerable effort before seeing tangible results. This course is designed to help you surmount that early hurdle. 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 comprehend the roles of executors and tasks. Furthermore, adhering to industry best practices, the course places strong emphasis on cloud deployment, with specific focus on Databricks and AWS. Students will also learn to differentiate between AWS EMR and AWS Glue, one of AWS's most recent Spark-related services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Botswana, participants will learn how to use Python and Spark together to analyze big data while working on hands-on exercises.

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

• Learn how to use Spark with Python to analyze 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 Botswana (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 is a unified platform that brings together big data, artificial intelligence, and governance. Through its Rocket and Intelligence modules, it facilitates swift data exploration, transformation, and advanced analytics for enterprise settings.

This instructor-led live training, available online or onsite, is designed for intermediate data professionals keen on leveraging the Rocket and Intelligence modules within Stratio using PySpark. The focus is on mastering looping structures, user-defined functions, and sophisticated data logic.

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

• Navigate and operate within the Stratio platform, utilising its Rocket and Intelligence modules.
• Apply PySpark effectively 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 discussions.
• Numerous exercises and practical sessions.
• Hands-on implementation in a live laboratory environment.

Customisation Options

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