Online Apache Spark Training in Norway

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.

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.

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.

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.

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 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 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.