Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in France (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 France (online or onsite) is tailored for beginner-level data professionals who want to acquire the knowledge and skills needed to effectively use 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 France (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.

Advances in technology and the exponential growth of information are reshaping business operations across various sectors, including the government. The generation of government data and the pace of 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 expands and grows more complex, the management, processing, storage, security, and disposition of this data become increasingly challenging. New 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 that information is a strategic asset. Governments must now protect, leverage, and analyze both structured and unstructured information to better serve the public and fulfill mission requirements. As government leaders work to evolve their organizations into data-driven entities to successfully achieve their missions, they are establishing the foundation to correlate dependencies across events, personnel, processes, and information.

High-value government solutions will emerge from a convergence of the most disruptive technologies:

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

Big Data represents one of the intelligent industry solutions, enabling government entities to make better decisions by taking action based on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these objectives requires far more than simply accumulating massive 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 wrote 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 required to analyze it.

The challenges posed by Big Data are nearly as daunting as the promise it offers is encouraging. Efficient data storage is one such challenge. As budgets remain tight, agencies must minimize the per-megabyte cost of storage while keeping data easily accessible so users can retrieve it whenever and however they need it. Backing up massive amounts of data further complicates this challenge.

Effective data analysis is another major hurdle. Many agencies utilize commercial tools that allow them to sift through vast amounts of data, identifying trends that enhance operational efficiency. (A recent study by MeriTalk 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 meet the need for data analysis. For example, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. The system has helped medical researchers identify links that can alert doctors to aortic aneurysms before they occur. It is also used for more routine tasks, such as sifting through resumes to connect job candidates with hiring managers.

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

Participants will have the chance 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 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 the use 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 also allows for the evaluation of participants.

• 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 platform designed for flow-based data integration and event processing. It facilitates automated, real-time data routing, transformation, and system mediation between disparate systems, featuring a web-based user interface and fine-grained control capabilities.

This instructor-led live training, available either onsite or remotely, is designed for intermediate-level administrators and engineers who aim to deploy, manage, secure, and optimize NiFi dataflows within production environments.

Upon completing this training, participants will be equipped to:

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows originating from diverse sources and targets.
• Implement automation, routing, and transformation logic for data flows.
• Optimize performance, monitor operational health, and resolve issues.

Format of the Course also allows for the evaluation of participants.

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

Course Customization Options

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

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 how to effectively manage large datasets by applying distributed computing principles.

This instructor-led, live training in France (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 France (online or onsite) is aimed at beginner to intermediate-level system administrators seeking to deploy, maintain, and optimize Spark clusters.

Upon completion of this training, participants will be capable of:

• Installing and configuring Apache Spark across various environments.
• Managing cluster resources and monitoring Spark applications.
• Optimizing the performance of Spark clusters.
• Implementing security measures and ensuring high availability.
• Debugging and troubleshooting common Spark issues.

Although Apache Spark has a challenging initial learning curve that requires significant effort to yield early results, this course is designed to help you navigate that difficult beginning. Upon completion, participants will gain a solid understanding of Apache Spark fundamentals, including the ability to clearly distinguish between RDDs and DataFrames. The curriculum covers the Python and Scala APIs, as well as key concepts such as executors and tasks. Aligned with best practices, the course emphasizes cloud deployment, with a strong focus on Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, one of AWS's latest Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in France, participants will discover how to leverage Python and Spark in unison to analyze big data through hands-on exercises.

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

• Understand how to use Spark with Python to analyze Big Data.
• Complete exercises that simulate real-world scenarios.
• Utilize various tools and techniques for big data analysis using PySpark.

This instructor-led, live training in France (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 seamlessly integrates big data, AI, and governance into a single, unified solution. Its Rocket and Intelligence modules empower organizations with rapid data exploration, transformation, and advanced analytics capabilities tailored for enterprise environments.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals looking to master the Rocket and Intelligence modules within Stratio using PySpark. The curriculum focuses on leveraging looping structures, user-defined functions, and complex data logic to enhance workflow efficiency.

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

• Navigate and effectively utilize the Stratio platform through its Rocket and Intelligence modules.
• Apply PySpark techniques for data ingestion, transformation, and analysis within the Stratio ecosystem.
• Implement loops and conditional logic to manage data workflows and streamline feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to create reusable data operations in PySpark.

Course Format

• Engaging interactive lectures and discussions.
• Extensive exercises and practical 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.