Programming with Big Data in R Training Course

This instructor-led live training in Norway (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.

Big data analytics refers to the process of inspecting vast amounts of diverse data sets to identify correlations, uncover hidden patterns, and generate valuable insights.

The healthcare sector generates enormous volumes of complex, heterogeneous medical and clinical data. Applying big data analytics to health data holds significant potential for deriving insights that enhance healthcare delivery. However, the sheer scale of these datasets presents substantial challenges for analysis and practical implementation within clinical environments.

Through this instructor-led, live remote training, participants will learn how to conduct big data analytics in the health sector by completing a series of hands-on live laboratory exercises.

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

• Install and configure big data analytics tools such as Hadoop MapReduce and Spark
• Understand the characteristics of medical data
• Apply big data techniques to manage medical data
• Study big data systems and algorithms in the context of health applications

Audience

• Developers
• Data Scientists

Course Format

• A blend of lectures, discussions, exercises, and intensive hands-on practice.

Note

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

This instructor-led live training in Norway (online or onsite) is designed for system administrators who wish to learn how to set up, deploy, and manage Hadoop clusters within their organizations.

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

• Install and configure Apache Hadoop.
• Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.  
• Set up HDFS to operate as storage engine for on-premise Spark deployments.
• Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
• Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.

In this instructor-led, live training in Norway (onsite or remotely), participants will learn how to set up and integrate various Stream Processing frameworks with existing big data storage systems, along with related software applications and microservices.

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

• Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
• Understand and select the most appropriate framework for specific tasks.
• Process data continuously, concurrently, and on a record-by-record basis.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
• Integrate the most suitable stream processing library with enterprise applications and microservices.

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 Norway (online or on-site) is designed for data scientists who wish to use the SMACK stack to build data processing platforms for big data solutions.

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

• Implement a data pipeline architecture for processing big data.
• Develop a cluster infrastructure with Apache Mesos and Docker.
• Analyze data with Spark and Scala.
• Manage unstructured data with Apache Cassandra.

This instructor-led live training in Norway (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.

This instructor-led live training in Norway (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.

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

OBJECTIVE:

This course introduces Apache Spark. Participants will learn how Spark integrates into the Big Data ecosystem and how to leverage it for data analysis. The curriculum covers the Spark shell for interactive analysis, Spark internals, Spark APIs, Spark SQL, Spark Streaming, as well as Machine Learning and GraphX.

AUDIENCE :

Developers / Data Analysts

This instructor-led, live training in Norway (online or onsite) is designed for data scientists and developers who wish to use Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines.

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

• Set up the necessary development environment to start building NLP pipelines with Spark NLP.
• Understand the features, architecture, and benefits of using Spark NLP.
• Use the pre-trained models available in Spark NLP to implement text processing.
• Learn how to build, train, and scale Spark NLP models for production-grade projects.
• Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).

In this instructor-led live training in Norway, 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.

This instructor-led, live training in Norway (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 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.

Target Audience

Course Format

Upon completing this training, participants will be capable of:

In this instructor-led, live training session, attendees will acquire the skills to manipulate and visualize data using the tools provided within the Tidyverse.

The Tidyverse comprises a suite of flexible R packages designed for data cleaning, processing, modeling, and visualization. Key packages include: ggplot2, dplyr, tidyr, readr, purrr, and tibble.

• Individuals new to the R programming language
• Those new to data analysis and visualization techniques

• A blend of lectures, discussions, exercises, and extensive practical, hands-on practice

• Execute data analysis and produce compelling visualizations
• Derive meaningful insights from various sample datasets
• Filter, sort, and summarize data to address exploratory questions
• Convert processed data into informative line charts, bar charts, histograms, and other plots
• Import and filter data from diverse sources, such as Excel, CSV, and SPSS files