Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in the Netherlands (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 the Netherlands (online or onsite) is designed for beginner-level data professionals seeking to acquire the knowledge and skills needed 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 guided, live training in the Netherlands (online or on-site) is tailored for intermediate-level data scientists and engineers who intend to use Google Colab and Apache Spark for big data processing and analytics.

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

• Establish a big data environment using Google Colab and Spark.
• Efficiently process and analyze large datasets 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 fundamentally reshaping business operations across numerous sectors, including the public sector. The volume of government data generation and digital archiving is accelerating, driven by the proliferation of mobile devices and applications, smart sensors and computing infrastructure, cloud-based solutions, and citizen-engagement portals. As digital information becomes increasingly vast and complex, the tasks of managing, processing, storing, securing, and disposing of this data become more intricate. New tools for capturing, searching, discovering, and analyzing data are empowering organizations to derive valuable insights from unstructured sources. The government sector is reaching a critical juncture, recognizing information as a strategic asset that must be protected, leveraged, and analyzed in both structured and unstructured forms to fulfill mission requirements effectively. Government leaders are working to evolve into data-driven organizations capable of successfully executing their missions, laying the groundwork to correlate dependencies among events, personnel, 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 agencies to make superior decisions by acting on patterns revealed through the analysis of large volumes of data—encompassing related and unrelated, as well as structured and unstructured, information.

However, achieving these capabilities requires more than simply amassing vast 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 supported agencies in identifying 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 develop the necessary tools for its analysis.

The challenges posed by Big Data are as significant as the opportunities it presents. Efficiently storing data is one such challenge. With budgets often constrained, agencies must minimize storage costs per megabyte while ensuring data remains readily accessible to users when and how they need it. Backing up massive data volumes further compounds this difficulty.

Effective data analysis is another major hurdle. Many agencies utilize commercial tools that allow them to navigate through large datasets, identifying trends that enhance operational efficiency. (A recent study by MeriTalk indicated that federal IT executives believe Big Data could help agencies save over $500 billion while also meeting mission objectives.)

Custom-developed Big Data tools are also enabling agencies to meet 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 assisted medical researchers in identifying links that can alert doctors to aortic aneurysms before they occur. It is also utilized for routine tasks, such as reviewing resumes to match job candidates with hiring managers.

Upon completing this instructor-led, live training in the Netherlands, 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 are integral components of the class.

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

Big Data and Advanced Analytics involves applying 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 facilitates automated, real-time data routing, transformation, and system mediation between disparate systems, featuring a web-based UI and fine-grained control.

This instructor-led, live training (onsite or remote) is designed for intermediate-level administrators and engineers who want 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.

Course Format

• 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 creating scalable data processing and Machine Learning workflows with PySpark. Participants will gain insights into how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by leveraging distributed computing principles.

This instructor-led live training (online or onsite) targets engineers who wish to set up and deploy Apache Spark systems for processing very large datasets.

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 the Netherlands (online or onsite) is designed for beginner to intermediate system administrators who wish to deploy, maintain, and optimize Spark clusters.

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

• Install and configure Apache Spark across 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.

Although Apache Spark presents a steep learning curve at the outset, requiring significant effort to see initial results, this course is designed to help you navigate that challenging phase more efficiently. By the end of the training, participants will gain a solid understanding of Apache Spark fundamentals, clearly distinguish between RDDs and DataFrames, master both the Python and Scala APIs, and comprehend key concepts such as executors and tasks. Aligned with industry best practices, the curriculum places strong emphasis on cloud deployment strategies, specifically focusing on Databricks and AWS. Additionally, students will explore the distinctions between AWS EMR and AWS Glue, which is one of AWS's most recent Spark-based services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led live training in the Netherlands, participants will learn to combine Python and Spark for big data analysis while engaging in practical exercises.

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

• Utilize Spark alongside Python to analyze big data effectively.
• Complete exercises that simulate real-world scenarios.
• Apply various tools and techniques for big data analysis using PySpark.

This instructor-led, live training in the Netherlands (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, 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 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.