Data Streaming and Real Time Data Processing Training Course

This instructor-led live training in Malaysia (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 Malaysia (online or onsite) is designed for beginner-level data professionals who want to acquire the knowledge and skills needed to effectively use 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 Malaysia (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.

Technological advancements and the exponential growth of information are reshaping business operations across various sectors, including the government sector. Government data generation and digital archiving are accelerating due to the surge in mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information expands and grows in complexity, managing, processing, storing, securing, and disposing of this data becomes increasingly challenging. New tools for capturing, searching, discovering, and analyzing data are enabling organizations to extract valuable insights from unstructured data. The government sector is at a critical juncture, recognizing that information is a strategic asset. Governments must protect, leverage, and analyze both structured and unstructured information to better serve the public and meet mission objectives. As government leaders strive to evolve into data-driven organizations to successfully accomplish their missions, they are establishing the foundation to correlate dependencies across events, people, processes, and information.

High-impact government solutions will emerge from integrating the most disruptive technologies:

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

Big Data represents a key intelligent industry solution, enabling governments 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 goals requires more than just accumulating massive amounts 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," wrote Tom Kalil and Fen Zhao from the White House Office of Science and Technology Policy in a post on the OSTP Blog.

The White House took steps to assist agencies in finding 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 required to analyze it.

The challenges posed by Big Data are nearly as daunting as the promise it holds is encouraging. Efficiently storing data is one such challenge. With budgets always 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. Backing up massive quantities of data further intensifies this challenge.

Effectively analyzing data is another major challenge. Many agencies use commercial tools to sift through vast amounts of data, identifying trends that help them operate more efficiently. 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 used for routine tasks, such as screening resumes to connect job candidates with hiring managers.

By completing this instructor-led, live training in Malaysia, participants will gain a practical, real-world understanding of Big Data and its associated technologies, methodologies, and tools.

Participants will have the opportunity to apply this knowledge through hands-on exercises. Group interaction and instructor feedback form a vital component of the learning experience.

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

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.

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 training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process vast datasets efficiently by applying distributed computing principles.

This instructor-led, live training in Malaysia (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark systems 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 datasets.
• 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 Malaysia (online or onsite) is designed for beginner to intermediate system administrators seeking 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.

While the initial learning curve for Apache Spark can be steep, requiring significant effort to achieve early results, this course is designed to help you navigate those initial challenges. Upon completion, participants will gain a solid understanding of Apache Spark fundamentals, clearly distinguish between RDDs and DataFrames, and become proficient in using both Python and Scala APIs. You will also deepen your knowledge of executors and tasks. In line with industry best practices, the course places a strong emphasis on cloud deployment, with specific focus on Databricks and AWS. Additionally, students will learn to differentiate 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 Malaysia, participants will learn how to use Python and Spark together to analyze big data while engaging in 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 Malaysia (online or on-site) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyse, and transform large and complex datasets.

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, data-centric platform that consolidates big data management, artificial intelligence, and governance. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics tailored for enterprise settings.

This instructor-led live training, available online or on-site, is designed for intermediate data professionals looking to master the Rocket and Intelligence modules within Stratio using PySpark. Key areas of focus include looping structures, user-defined functions (UDFs), and implementing advanced data logic.

Upon completing this training, participants will be capable of:

• Effectively navigating and utilizing the Stratio platform through its Rocket and Intelligence modules.
• Applying PySpark for data ingestion, transformation, and analytical tasks.
• Employing loops and conditional logic to orchestrate data workflows and execute feature engineering.
• Developing and managing user-defined functions (UDFs) to enable reusable data operations within PySpark.

Course Format

• Engaging lectures accompanied by interactive discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live laboratory environment.

Customization Options

• To arrange customized training for this course, please get in touch with us to discuss your specific requirements.