Apache Spark Fundamentals Training Course

This instructor-led live training in Italy (online or onsite) is intended for intermediate-level data scientists and engineers seeking to utilize Google Colab and Apache Spark for big data processing and analytics.

Upon completing this training, participants will be capable of:

• Establishing a big data environment using Google Colab and Spark.
• Efficiently processing and analyzing large-scale datasets with Apache Spark.
• Visualizing big data within a collaborative framework.
• Integrating Apache Spark with cloud-based tools.

This course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Attendees will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process extensive datasets efficiently by leveraging distributed computing principles.

This instructor-led, live training in Italy (online or onsite) targets 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 before yielding tangible results. This course is designed to help learners navigate this challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and become proficient with the Python and Scala APIs. They will also gain insight into executors, tasks, and other core concepts. Aligned with best practices, the course places a strong emphasis on cloud deployment, with specific attention to Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, one of AWS's more recent Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Italy, participants will learn how to combine Python and Spark to analyze big data while engaging in hands-on exercises.

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

• Learn how to use Spark with Python to analyze Big Data.
• Work on exercises that mimic real world cases.
• Use different tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Italy (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 serves as a unified, data-centric platform that seamlessly combines big data capabilities, artificial intelligence, and governance. Its Rocket and Intelligence modules empower organizations to rapidly explore, transform, and analyze data with advanced analytics tailored for enterprise needs.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals aiming to master the Rocket and Intelligence modules within Stratio using PySpark. The curriculum emphasizes looping structures, user-defined functions (UDFs), and sophisticated data logic.

Upon completion, participants will gain the ability to:

• Effectively navigate and utilize the Rocket and Intelligence modules within the Stratio platform.
• Implement PySpark for efficient data ingestion, transformation, and analytical processes.
• Control data workflows and execute feature engineering tasks using loops and conditional logic.
• Develop and manage user-defined functions (UDFs) to facilitate reusable data operations in PySpark.

Training Format

• Engaging interactive lectures and discussions.
• Extensive exercises and practical practice sessions.
• Hands-on implementation within a live laboratory environment.

Customization Options

• For tailored training requests, please contact us to make arrangements.