Data Streaming and Real Time Data Processing Training Course

This instructor-led live training in Vietnam (online or onsite) targets 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.

Upon completing this training, participants will be able to:

• Gain a deep 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 optimize query performance and improve 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 Vietnam (online or onsite) is designed for 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 comprehensive 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 across various 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 Vietnam (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 practices across various sectors, including government. The volume of government data generated and archived digitally is increasing rapidly, driven by the surge in mobile devices and applications, smart sensors and IoT devices, cloud computing solutions, and citizen-facing portals. As digital information becomes more expansive and complex, so do the challenges of managing, processing, storing, securing, and disposing of it. New tools for capturing, searching, discovering, and analyzing data are enabling organizations to extract valuable insights from unstructured information. The government sector is reaching a critical juncture, recognizing that information is a strategic asset. Governments must now protect, leverage, and analyze both structured and unstructured data to better serve the public and meet mission objectives. As government leaders work to evolve into data-driven organizations, they are building the foundation to correlate dependencies across events, personnel, processes, and information.

High-value government solutions will emerge from integrating the most transformative technologies:

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

Big Data is a key intelligent industry solution that empowers governments to make better decisions by acting on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or 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 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 assisting 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 needed to analyze it.

The challenges posed by Big Data are nearly as formidable as the opportunities it presents. Efficient data storage is a primary concern. With budgets remaining tight, agencies must minimize the cost per megabyte of storage while ensuring data remains easily accessible so users can retrieve it when and how they need it. Backing up vast quantities of data further intensifies this challenge.

Effective data analysis is another major hurdle. Many agencies utilize 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 analytical 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 match job candidates with hiring managers.

By completing this instructor-led, live training in Vietnam, participants will acquire a practical, real-world grasp of Big Data, along with its associated technologies, methodologies, and tools.

The course provides ample opportunity for learners to apply their knowledge through hands-on exercises. Active group interaction and direct feedback from the instructor are key elements of the class experience.

The curriculum begins by introducing fundamental Big Data concepts, then advances into the programming languages and methodologies essential for data analysis. Finally, it explores 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 analyze large, complex datasets in order to generate actionable insights and support strategic decision-making.

This instructor-led live training (available online or onsite) is designed for advanced data professionals who want to utilize cutting-edge analytical methods and big data technologies for predictive, prescriptive, and real-time analytics.

Upon completion 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.

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.

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. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently manage large datasets by applying distributed computing principles.

This instructor-led, live training in Vietnam (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 Vietnam (online or onsite) is designed for beginner to intermediate 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 learners quickly overcome that initial hurdle. Upon completion, participants will gain a solid understanding of Apache Spark's fundamentals, clearly distinguishing between RDDs and DataFrames. They will become proficient in both Python and Scala APIs, comprehend how executors and tasks function, and more. Additionally, aligning with industry best practices, the curriculum places a strong emphasis on cloud deployment strategies, specifically focusing on Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, one of AWS's latest Spark-based services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Vietnam, participants will learn how to use Python and Spark together to analyze 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 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 Vietnam (online or onsite) targets developers who wish to integrate Spark, Hadoop, and Python for processing, analyzing, and transforming 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 offers a unified platform that combines big data capabilities, artificial intelligence, and governance. Its Rocket and Intelligence modules facilitate swift data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led live training (available online or onsite) is designed for intermediate-level data professionals aiming to effectively utilize Stratio's Rocket and Intelligence modules with PySpark. The curriculum emphasizes looping structures, user-defined functions, and complex data logic.

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

• Navigate the Stratio platform proficiently using its Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis tasks.
• Utilize loops and conditional logic to manage data workflows and feature engineering processes.
• Develop and manage user-defined functions (UDFs) to enable reusable data operations in PySpark.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical applications.
• Hands-on implementation in a live laboratory environment.

Customization Options

• For customized training needs, please contact us to make arrangements.