Apache Spark Training in Patras

This instructor-led, live training in Patras (online or onsite) is designed for intermediate-level data scientists and engineers seeking to apply Google Colab and Apache Spark to 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.
• Process and analyse large datasets efficiently with Apache Spark.
• Visualise big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Stratio offers a comprehensive, data-centric platform that unifies big data capabilities, artificial intelligence, and governance into a single, cohesive solution. Its Rocket and Intelligence modules empower organizations to perform rapid data exploration, transformation, and advanced analytics within enterprise-grade environments.

This instructor-led live training, available online or on-site, is designed for intermediate-level data professionals seeking to harness the full potential of Stratio’s Rocket and Intelligence modules using PySpark. The curriculum emphasizes looping structures, user-defined functions, and sophisticated data logic to enhance workflow efficiency.

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

• Navigate and effectively utilize the Stratio platform, specifically the Rocket and Intelligence modules.
• Apply PySpark techniques for data ingestion, transformation, and analysis within the Stratio ecosystem.
• Implement 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 paired with group discussions.
• Extensive hands-on exercises and practical practice sessions.
• Real-world implementation through a live-lab environment.

Customization Options

• For tailored training solutions, please reach out to us to discuss your specific requirements.

This instructor-led live training in Patras (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 Patras (online or onsite) targets beginner to intermediate system administrators who wish to deploy, maintain, and optimize Spark clusters.

Upon completing this training, participants will be able to:

• Install and configure Apache Spark across diverse environments.
• Manage cluster resources and monitor Spark applications.
• Enhance the performance of Spark clusters.
• Implement security protocols and ensure high availability.
• Debug and resolve common Spark issues.

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

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

• Utilize Spark with Python to analyze Big Data.
• Complete 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 developing scalable data processing and Machine Learning workflows utilizing PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by applying distributed computing principles.

This instructor-led live training in Patras (online or on-site) is aimed at engineers who wish to set up and deploy an 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.

While Apache Spark initially presents a steep learning curve requiring considerable effort to yield early results, this course is designed to help learners navigate and overcome that initial hurdle. Upon completion, participants will gain a solid grasp of Apache Spark fundamentals, clearly distinguish between RDDs and DataFrames, become proficient with the Python and Scala APIs, and understand core concepts such as executors and tasks. Aligned with industry best practices, the curriculum places a strong emphasis on cloud deployment, specifically targeting Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, which represents one of AWS's newer Spark-based services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists