Data Streaming and Real Time Data Processing Training Course

This instructor-led, live training in Sweden (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 Sweden (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 Sweden (online or onsite) targets intermediate-level data scientists and engineers who intend to utilise 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.

Technological advancements and the exponential growth of data are reshaping business operations across numerous sectors, including government. The volume of data generated and archived digitally by government entities is surging, 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 challenges associated with its management, processing, storage, security, and disposition intensify. New tools for capturing, searching, discovering, and analyzing data are enabling organizations to extract valuable insights from unstructured information. 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 data to better fulfill mission requirements. As leaders work to evolve data-driven organizations, they are establishing the framework to correlate dependencies among events, people, processes, and information.

High-impact government solutions are emerging from 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, 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 these outcomes requires more than merely accumulating large datasets. "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 of the White House Office of Science and Technology Policy in a post on the OSTP Blog.

The White House advanced this effort by launching the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to capitalize on the Big Data explosion and the tools necessary to analyze it.

The challenges posed by Big Data are as formidable as its potential is promising. Efficient data storage is a primary concern. With budgets often constrained, agencies must minimize the cost per megabyte of storage while ensuring data remains easily accessible for users when and how they need it. Backing up massive data volumes further complicates this challenge.

Effective data analysis presents another major hurdle. 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, 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 applied to routine tasks, such as screening resumes to match candidates with hiring managers.

Attendees who finish this instructor-led, live training in Sweden will acquire a practical, real-world grasp of Big Data, along with its associated technologies, methodologies, and tools.

Participants will be able to apply this knowledge through practical exercises. Group interaction and feedback from the instructor are vital parts of the learning experience.

The course begins by introducing the fundamental concepts of Big Data, then moves on to the programming languages and methodologies employed for Data Analysis. Lastly, we examine 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 to extract actionable insights and support strategic decision-making.

This instructor-led live training (available online or onsite) is designed for advanced-level data professionals who want 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 both 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.
• Extensive 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 course offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows with PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process substantial datasets efficiently by leveraging distributed computing principles.

This instructor-led, live training in Sweden (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 Sweden (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 diverse 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 seeing tangible results. This course is designed to help learners navigate that challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and become proficient with both Python and Scala APIs. They will also gain a solid understanding of executors, tasks, and other core concepts. Aligned with industry best practices, the curriculum places strong emphasis on cloud deployment strategies, specifically within Databricks and AWS environments. Students will also learn to differentiate between AWS EMR and AWS Glue, one of AWS's most recent Spark-based services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led live training, participants will learn how to utilize Python and Spark together 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 Sweden (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 unified platform centered on data that combines big data capabilities, artificial intelligence, and governance into one cohesive solution. The Rocket and Intelligence modules within Stratio facilitate swift data exploration, transformation, and sophisticated analytics in enterprise settings.

This instructor-led live training (available online or onsite) is designed for intermediate-level data professionals looking to leverage the Rocket and Intelligence modules in Stratio effectively with PySpark. The curriculum emphasizes looping structures, user-defined functions, and complex data logic.

Upon completing this training, participants will be able to:

• Navigate and utilize the Stratio platform through the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis.
• Utilize loops and conditional logic to manage data workflows and execute feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to enable reusable data operations in PySpark.

Course Format

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

Course Customization Options

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