Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in Italy (online or onsite) is designed for advanced-level data professionals who want 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 completion of 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, 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 Italy (online or onsite) is tailored 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 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 Italy (online or onsite) is intended for intermediate-level data scientists and engineers seeking to utilize Google Colab and Apache Spark for big data processing and analytics.

Upon completing this training, participants will be capable of:

• Establishing a big data environment using Google Colab and Spark.
• Efficiently processing and analyzing large-scale datasets with Apache Spark.
• Visualizing big data within a collaborative framework.
• Integrating Apache Spark with cloud-based tools.

Technological advancements and the exponential growth of data are reshaping operational practices across numerous sectors, including the public sector. Government data generation and digital archiving are accelerating, 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 management, processing, storage, security, and retention of this data grow increasingly intricate. New tools for capture, search, discovery, and analysis are enabling organizations to derive valuable insights from unstructured data. The government sector is reaching a critical juncture, recognizing information as a strategic asset. Governments must now protect, leverage, and analyze both structured and unstructured data to fulfill mission requirements and serve the public more effectively. As government leaders work to evolve into data-driven organizations, they are establishing the foundation to correlate dependencies among events, personnel, processes, and information.

High-value government solutions will emerge from the integration of several 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 empowers government entities to make better decisions by acting on patterns identified through the analysis of large volumes of data—both related and unrelated, structured and unstructured.

However, achieving these outcomes requires more than simply accumulating massive amounts of data. As Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy noted in a post on the OSTP Blog, "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."

The White House facilitated agency access to 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 formidable as its potential is promising. Efficient data storage remains a primary challenge. With budgets often constrained, agencies must minimize storage costs per megabyte while ensuring data remains easily accessible for users to retrieve as needed. Backing up massive data volumes further complicates this task.

Effective data analysis presents another significant challenge. Many agencies utilize commercial tools to sift through vast data sets, identifying trends that enhance operational efficiency. (A recent MeriTalk study indicated that federal IT executives believe Big Data could help agencies save over $500 billion while 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 helped medical researchers identify links that can alert doctors to aortic aneurysms before they occur. It is also used for routine tasks, such as matching job candidates with hiring managers by reviewing resumes.

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

Attendees will have the chance to apply this knowledge through hands-on exercises. The class places significant emphasis on group interaction and direct feedback from the instructor.

The course begins with an overview of fundamental Big Data concepts, then advances to the programming languages and methodologies 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 the use of sophisticated techniques and tools to examine large, complex datasets, yielding actionable insights and supporting strategic decision-making.

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

Upon completing 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.
• Utilize 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 lectures and discussions.
• Extensive exercises and practice sessions.
• 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 platform designed for flow-based data integration and event processing. It facilitates the automated, real-time routing, transformation, and mediation of data between diverse systems, supported by a web-based interface that offers granular control.

This instructor-led live training, available both onsite and remotely, is tailored for intermediate-level administrators and engineers aiming to deploy, manage, secure, and optimize NiFi dataflows within production environments.

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

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows connecting various sources and sinks.
• Implement logic for flow automation, routing, and transformation.
• Optimize performance, monitor operations, and troubleshoot issues.

Format of the Course

• Interactive lectures featuring discussions on real-world architectures.
• Hands-on labs focused on building, deploying, and managing flows.
• Scenario-based exercises conducted in a live-lab environment.

Course Customization Options

• For information on arranging customized training for this course, please contact us.

This course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Attendees will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process extensive datasets efficiently by leveraging distributed computing principles.

This instructor-led, live training in Italy (online or onsite) is designed for engineers who want to set up and deploy an Apache Spark system for processing vast 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 distinctions between Apache Spark and Hadoop MapReduce, and identify when to use each.
• Integrate Apache Spark with other machine learning tools.

This instructor-led, live training in Italy (online or onsite) targets 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 considerable effort before yielding tangible results. This course is designed to help learners navigate this challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and become proficient with the Python and Scala APIs. They will also gain insight into executors, tasks, and other core concepts. Aligned with best practices, the course places a strong emphasis on cloud deployment, with specific attention to Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, one of AWS's more recent Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Italy, participants will learn how to combine Python and Spark 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 Italy (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 serves as a unified, data-centric platform that seamlessly combines big data capabilities, artificial intelligence, and governance. Its Rocket and Intelligence modules empower organizations to rapidly explore, transform, and analyze data with advanced analytics tailored for enterprise needs.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals aiming to master the Rocket and Intelligence modules within Stratio using PySpark. The curriculum emphasizes looping structures, user-defined functions (UDFs), and sophisticated data logic.

Upon completion, participants will gain the ability to:

• Effectively navigate and utilize the Rocket and Intelligence modules within the Stratio platform.
• Implement PySpark for efficient data ingestion, transformation, and analytical processes.
• Control data workflows and execute feature engineering tasks using loops and conditional logic.
• Develop and manage user-defined functions (UDFs) to facilitate reusable data operations in PySpark.

Training Format

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

Customization Options

• For tailored training requests, please contact us to make arrangements.