Apache Iceberg Fundamentals Training Course

This instructor-led, live training in Taiwan (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 Taiwan (online or onsite) is tailored for intermediate-level data scientists and engineers who wish to utilize Google Colab and Apache Spark for big data processing and analytics.

By the conclusion 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 numerous sectors, including the public sector. Government agencies are generating and archiving digital records at an accelerating pace, driven by the proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information becomes more voluminous and complex, the challenges associated with information management, processing, storage, security, and retention intensify. Emerging tools for capture, search, discovery, and analysis are enabling organizations to extract valuable insights from unstructured data. The government sector has reached a critical juncture where leaders recognize information as a strategic asset. To better serve citizens and meet mission requirements, governments must protect, leverage, and analyze both structured and unstructured information. As government leaders work 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 being created through the integration of disruptive technologies:

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

Big Data serves as an 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 of the White House Office of Science and Technology Policy in a post on the OSTP Blog.

The White House advanced this effort by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to maximize the potential of Big Data and the tools required to analyze it.

The challenges posed by Big Data are nearly as formidable as its promise is encouraging. Efficiently storing data is one significant challenge. With budgets remaining tight, agencies must minimize the cost per megabyte of storage while ensuring data remains easily accessible so users can retrieve it efficiently. The need to back up massive amounts of data further exacerbates this challenge.

Effectively analyzing data presents another major hurdle. Many agencies utilize commercial tools to sift through large datasets, identifying trends that improve 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 helping agencies 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. 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 sifting through resumes to match job candidates with hiring managers.

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

Participants will have the opportunity to apply this knowledge through hands-on exercises. Group interaction and instructor feedback are key components of the class.

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

Big Data and Advanced Analytics involves the application of sophisticated techniques and tools to analyze 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 gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process extensive datasets efficiently by applying distributed computing principles.

This instructor-led, live training in Taiwan (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 Taiwan (online or onsite) targets beginner to intermediate-level 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 achieving tangible results. This course is designed to help you navigate that challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDD and DataFrame, and gain proficiency in both Python and Scala APIs. You will also gain a solid understanding of executors, tasks, and other core concepts. Adhering to 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 the latest Spark services offered by AWS.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Taiwan, participants will learn how to combine Python and Spark to analyze 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 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 Taiwan (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.

Spark SQL serves as Apache Spark's module designed for handling both structured and unstructured data. It offers insights into data structure and the computations involved, enabling performance optimizations. Spark SQL is commonly utilized for:
- Executing SQL queries.
- Accessing data from existing Hive installations.

In this instructor-led live training, available either onsite or remotely, participants will learn to analyze diverse 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 various formats.
• Visualize data and query outcomes.

Course Format

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

Course Customization Options

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

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

This instructor-led, live training (online or onsite) is aimed at intermediate-level data professionals who wish to use the Rocket and Intelligence modules in Stratio effectively with PySpark, focusing 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 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.

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.