Big Data Training in Oslo

This instructor-led live training in Oslo (online or onsite) is designed for intermediate-level data scientists and engineers who wish 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.

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.

Data Warehousing involves the design, construction, and management of centralized data repositories that enable analytics, reporting, and informed decision-making.

This instructor-led live training, available online or onsite, targets intermediate-level data professionals seeking to master dimensional data modeling, construct robust ETL pipelines, and enhance analytical workloads.

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

• Articulating fundamental data warehousing concepts and architectural patterns.
• Designing dimensional models and selecting appropriate star or snowflake schemas.
• Developing and orchestrating reliable ETL and ELT pipelines.
• Distinguishing between OLTP and OLAP workloads and optimizing for analytical performance.

Course Format

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

Customization Options

• For customized training arrangements, please contact us to discuss your specific needs.

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

Attendees will have the chance to apply what they learn through hands-on exercises. The course emphasizes group interaction and provides valuable feedback from the instructor.

The curriculum begins by introducing the fundamental concepts of Big Data, then moves on to the programming languages and methods used for Data Analysis. Finally, it covers the tools and infrastructure necessary for Big Data storage, Distributed Processing, and Scalability.

Greenplum is an open-source massively parallel processing (MPP) data platform designed for analytics and large-scale data environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level technical users who wish to understand the internal architecture of Greenplum, build optimized data models, and apply high-performance SQL in distributed environments.

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

• Understand Greenplum’s shared-nothing architecture and parallelism model.
• Design distributed table structures and partitions optimized for query performance.
• Use indexing strategies, storage formats, and physical design techniques.
• Interpret EXPLAIN plans and optimize queries using best practices.

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.

Greenplum is an open-source massively parallel data platform built on PostgreSQL, designed for advanced analytics and large-scale data warehousing.

This instructor-led training (available online or onsite) targets intermediate-level administrators seeking to install, configure, and manage Greenplum environments efficiently, including the handling of system updates and libraries.

Upon completing this training, participants will be able to:

• Install and configure a production-ready Greenplum cluster.
• Manage system updates and apply patches safely.
• Handle Greenplum library management for analytics and extensions.
• Troubleshoot and monitor Greenplum systems.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Greenplum in a controlled lab environment.
• Guided exercises focused on cluster setup, upgrade workflows, and system extensions.

Course Customization Options

• To request a customized training for this course based on your environment or data platform, please contact us to arrange.

This instructor-led, live training in Oslo (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 Oslo (online or onsite) targets 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, available in Oslo (online or onsite), is designed for intermediate-level IT professionals seeking to improve their skills in data architecture, governance, cloud computing, and big data technologies. The goal is to enable participants to effectively manage and analyze large datasets for data migration purposes within their organizations.

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

• Understand the foundational concepts and components of various data architectures.
• Gain a comprehensive understanding of data governance principles and their importance in regulatory environments.
• Implement and manage data governance frameworks such as Dama and Togaf.
• Leverage cloud platforms for efficient data storage, processing, and management.

This instructor-led live training in Oslo (available online or onsite) is designed for intermediate-level data engineers who wish to learn how to utilize Azure Data Lake Storage Gen2 for effective data analytics solutions.

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

• Understand the architecture and key features of Azure Data Lake Storage Gen2.
• Optimize data storage and access for cost and performance.
• Integrate Azure Data Lake Storage Gen2 with other Azure services for analytics and data processing.
• Develop solutions using the Azure Data Lake Storage Gen2 API.
• Troubleshoot common issues and optimize storage strategies.

This instructor-led, live training in Oslo (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.

Big Data and Advanced Analytics involves the use of sophisticated techniques and tools to analyze large, complex datasets, generating actionable insights and supporting 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.

Upon completion 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.
• 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 practice.
• Hands-on implementation in a live-lab environment.

Customization Options

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

This instructor-led live training in Oslo (online or onsite) is aimed at intermediate-level IT professionals who wish to have a comprehensive understanding of IBM DataStage from both an administrative and a development perspective, allowing them to manage and utilize this tool effectively in their respective workplaces.

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

• Understand the core concepts of DataStage.
• Learn how to effectively install, configure, and manage DataStage environments.
• Connect to various data sources and extract data efficiently from databases, flat files, and external sources.
• Implement effective data loading techniques.

This instructor-led live training in Oslo (online or onsite) is aimed at 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.

In this instructor-led live training in Oslo, participants will learn how to utilize Python and Spark together to analyze big data while working on hands-on exercises.

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

• Master the use of Spark with Python to analyze Big Data.
• Tackle exercises that simulate real-world scenarios.
• Apply various tools and techniques for big data analysis using PySpark.

Technological advancements and the exponential growth of information are reshaping business operations across numerous sectors, including government. The generation of government data and the pace of digital archiving are accelerating, driven by the surge in mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information becomes more expansive and complex, managing, processing, storing, securing, and disposing of this data grows increasingly difficult. New tools for capturing, searching, discovering, and analyzing data are enabling organizations to extract valuable insights from their unstructured data. The government sector is reaching a critical juncture, recognizing information as a strategic asset. Government bodies must protect, leverage, and analyze both structured and unstructured information to better serve the public and fulfill mission requirements. As government leaders work to transform into data-driven organizations to successfully achieve their missions, they are establishing the framework to correlate dependencies across events, people, processes, and information.

High-value government solutions will emerge from a combination 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 entities to make better decisions by acting on patterns revealed through the analysis of large volumes of data—whether related or unrelated, structured or unstructured.

However, achieving this requires 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," wrote 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 moved to assist 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 formidable as its promise is encouraging. Efficiently storing data is one such challenge. Budgets are always tight, so agencies must minimize the cost per megabyte of storage while keeping data readily accessible so users can retrieve it when and how they need it. Backing up massive amounts of data further intensifies this challenge.

Effectively analyzing the data is another major hurdle. Many agencies utilize commercial tools that allow them to sift through vast amounts of data, identifying trends that improve operational efficiency. (A recent MeriTalk study found that federal IT executives believe Big Data could help agencies save more than $500 billion while also meeting mission objectives.)

Custom-developed Big Data tools are also enabling agencies to address their data analysis 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 more routine tasks, such as sifting through resumes to match job candidates with hiring managers.

In this instructor-led live training Oslo, participants will cultivate the mindset required to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies to identify and prevent criminal activity. By analyzing case studies from law enforcement organizations worldwide, participants will gain insights into adoption strategies, challenges, and outcomes.

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

• Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation.
• Implement industrial big data storage and processing solutions for data analysis.
• Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation.

Big Data encompasses solutions designed for the storage and processing of vast datasets. Originally pioneered by Google, these Big Data frameworks have evolved and inspired numerous similar open-source projects. R has established itself as a preferred programming language within the financial sector.

Course Overview

This course offers a practical and structured entry point into constructing real-time data streaming systems. It explores core concepts, architectural patterns, and the industry-standard tools required to process continuous data at scale. Participants will acquire the skills to design, implement, and optimize streaming pipelines using contemporary frameworks. The curriculum advances from foundational theory to hands-on application, empowering learners to confidently develop production-ready real-time solutions.

Format of Training

• Instructor-led sessions with guided explanations

• Concept walkthroughs supported by real-world examples

• Hands-on demonstrations and coding exercises

• Progressive labs aligned with daily topics

• Interactive discussions and Q&A

Course Objectives

• Understand real-time data streaming concepts and system architecture

• Differentiate between batch and streaming data processing models

• Design scalable and fault-tolerant streaming pipelines

• Work with distributed streaming tools and frameworks

• Apply event time processing, windowing, and stateful operations

• Build and optimize real-time data solutions for business use cases

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. During this three-day course (with an optional fourth day), participants will explore the business advantages and practical applications of Hadoop and its surrounding ecosystem. The curriculum covers cluster deployment planning, expansion strategies, and the installation, maintenance, monitoring, troubleshooting, and optimization of Hadoop. Attendees will gain hands-on experience with bulk data loading on clusters, become familiar with various Hadoop distributions, and practice installing and managing ecosystem tools. The course concludes with a discussion on securing clusters using Kerberos.

“…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop administrators

Format

The course combines lectures with hands-on labs, maintaining an approximate balance of 60% lectures and 40% labs.

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.

In this instructor-led, live training in Oslo, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.

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

• Understand NiFi's architecture and dataflow concepts.
• Develop extensions using NiFi and third-party APIs.
• Custom develop their own Apache Nifi processor.
• Ingest and process real-time data from disparate and uncommon file formats and data sources.

This training offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight 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 in Oslo (online or on-site) is designed for engineers aiming to set up and deploy Apache Spark for processing very large data sets.

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

• Install and configure Apache Spark.
• Process and analyze massive data sets rapidly.
• Differentiate between Apache Spark and Hadoop MapReduce and know when to use each.
• Integrate Apache Spark with external machine learning tools.

The initial learning curve for Apache Spark can be steep, requiring considerable effort to achieve early results. This course is designed to help learners navigate that challenging first phase effectively. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, become proficient with both Python and Scala APIs, and gain a solid understanding of executors and tasks. Furthermore, adhering to industry best practices, the course places a strong emphasis on cloud deployment strategies, specifically focusing on Databricks and AWS. Students will also learn to differentiate between AWS EMR and AWS Glue, one of the more recent Spark services offered by AWS.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

Spark SQL serves as Apache Spark's dedicated module for managing both structured and unstructured data. It offers insight into the data's structure alongside the computations being executed, enabling performance optimizations. The primary applications of Spark SQL include:
- Running SQL queries.
- Accessing data from an existing Hive setup.

Through this instructor-led live training (available onsite or remotely), attendees will acquire the skills to analyze diverse data sets using Spark SQL.

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

• Installing and setting up Spark SQL.
• Conducting data analysis with Spark SQL.
• Querying data sets across various formats.
• Visualizing data and the outcomes of queries.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Practical implementation within a live-lab environment.

Customization Options

• For personalized training requests for this course, please contact us to make arrangements.

This instructor-led live training in Oslo (online or onsite) is aimed at technical persons who wish to deploy Talend Open Studio for Big Data to simplifying the process of reading and crunching through Big Data.

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

• Install and configure Talend Open Studio for Big Data.
• Connect with Big Data systems such as Cloudera, HortonWorks, MapR, Amazon EMR and Apache.
• Understand and set up Open Studio's big data components and connectors.
• Configure parameters to automatically generate MapReduce code.
• Use Open Studio's drag-and-drop interface to run Hadoop jobs.
• Prototype big data pipelines.
• Automate big data integration projects.