Data Streaming and Real Time Data Processing Training Course

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

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

• Configure 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 fundamentally reshaping business operations across numerous sectors, including the public sector. Government data generation and digital archiving volumes are surging, driven by the proliferation of mobile devices and applications, smart sensors and connected devices, cloud computing solutions, and citizen-facing digital portals. As digital information expands and becomes increasingly complex, the management, processing, storage, security, and disposition of that data become more intricate. Emerging tools for capture, search, discovery, and analysis are enabling organizations to extract valuable insights from unstructured data. The government sector is at a critical juncture, recognizing information as a strategic asset. Governments must now protect, leverage, and analyze both structured and unstructured information to better serve citizens and meet mission objectives. As government leaders strive to evolve into data-driven organizations, they are establishing the foundation to correlate dependencies across events, personnel, 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 transformative industry solution, empowering governments to make superior decisions by acting on patterns revealed through the analysis of vast volumes of data—both related and unrelated, structured and unstructured.

However, achieving this requires more than simply accumulating large 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," noted 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 a significant step toward supporting agencies in finding these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to capitalize on the explosion of Big Data and develop the necessary tools to analyze it.

The challenges posed by Big Data are nearly as daunting as the promise it offers. Efficiently storing data is one such challenge. With budgets always under pressure, agencies must minimize the cost per megabyte of storage while ensuring data remains easily accessible so users can retrieve it whenever and however they need it. Backing up these massive data volumes further intensifies the challenge.

Effectively analyzing data is another major hurdle. Many agencies utilize commercial tools that enable them to sift through vast amounts of data, identifying trends that enhance operational efficiency. (A recent MeriTalk study revealed 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 allowing agencies to meet their data analysis needs. For instance, the Computational Data Analytics Group at Oak Ridge National Laboratory 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.

Participants who complete this instructor-led, live training in Romania will acquire a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.

Participants will have the chance to apply this knowledge through practical, hands-on exercises. Collaborative group interaction and direct feedback from the instructor are integral parts of the learning experience.

The course begins by introducing fundamental Big Data concepts, then moves on to the programming languages and methods employed for Data Analysis. Finally, we explore 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, yielding actionable insights for strategic decision-making.

This instructor-led training, available both online and onsite, is designed for advanced-level data professionals looking to leverage cutting-edge analytical methods and big data technologies for predictive, prescriptive, and real-time analytics.

Upon completing this training, participants will be equipped 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.
• Utilize distributed computing frameworks for real-time analytics and data streaming.
• Integrate big data analytics into business intelligence and decision-making systems.

Course Format

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

Customization Options

• To request a customized training for this course, please contact us to arrange it.

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 (available onsite or remote) is designed for 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 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 large datasets efficiently by leveraging distributed computing principles.

This instructor-led, live training in Romania (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 Romania (online or onsite) is designed for beginner to intermediate system administrators who want 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.

Initially, the learning curve for Apache Spark can be steep, requiring significant effort before achieving 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 fundamentals, clearly distinguish between RDDs and DataFrames, and master both the Python and Scala APIs. They will also develop a comprehensive grasp of executors, tasks, and other core concepts. In alignment with industry best practices, the course places a strong emphasis on cloud-based deployment, specifically focusing on Databricks and AWS environments. Students will also learn to differentiate between AWS EMR and AWS Glue, one of AWS's most recent Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Romania, 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 Romania (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 data-centric platform that combines big data, artificial intelligence, and governance into a unified solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led training session, available both online and on-site, is designed for data professionals at an intermediate level who aim to effectively utilize Stratio's Rocket and Intelligence modules with PySpark. The curriculum focuses on looping structures, user-defined functions, and complex data logic.

Upon completing this training, participants will be capable of:

• Navigating and operating within the Stratio platform using the Rocket and Intelligence modules.
• Applying PySpark for data ingestion, transformation, and analysis.
• Utilizing loops and conditional logic to manage data workflows and feature engineering tasks.
• Creating and managing user-defined functions (UDFs) to enable reusable data operations in PySpark.

Format of the Course

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

Course Customization Options

• To request customized training for this course, please contact us to arrange.