Apache Spark Training in Utrecht

This guided, live training in Utrecht (online or on-site) is tailored for intermediate-level data scientists and engineers who intend to use Google Colab and Apache Spark for big data processing and analytics.

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

• Establish a big data environment using Google Colab and Spark.
• Efficiently process and analyze large datasets 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 Utrecht (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, 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 Utrecht (online or onsite) is designed for beginner to intermediate system administrators who wish to deploy, maintain, and optimize Spark clusters.

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

• Install and configure Apache Spark across 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 Utrecht, participants will learn to combine Python and Spark for big data analysis while engaging in practical exercises.

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

• Utilize Spark alongside Python to analyze big data effectively.
• Complete exercises that simulate real-world scenarios.
• Apply various tools and techniques for big data analysis using PySpark.

This course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Participants will gain insights 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 (online or onsite) targets engineers who wish to set up and deploy Apache Spark systems for processing very large datasets.

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.

Although Apache Spark presents a steep learning curve at the outset, requiring significant effort to see initial results, this course is designed to help you navigate that challenging phase more efficiently. By the end of the training, participants will gain a solid understanding of Apache Spark fundamentals, clearly distinguish between RDDs and DataFrames, master both the Python and Scala APIs, and comprehend key concepts such as executors and tasks. Aligned with industry best practices, the curriculum places strong emphasis on cloud deployment strategies, specifically focusing on Databricks and AWS. Additionally, students will explore the distinctions between AWS EMR and AWS Glue, which is one of AWS's most recent Spark-based services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists