Apache Spark Training in Plovdiv

This instructor-led, live training in Plovdiv (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use 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 comprehensive, data-driven 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 sophisticated analytics within enterprise settings.

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

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

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

Training Format

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation in a live lab environment.

Customization Options

• For customized training arrangements for this course, please contact us directly.

This instructor-led, live training in Plovdiv (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 Plovdiv (online or onsite) is designed for beginner to intermediate-level system administrators who want to deploy, maintain, and optimize Spark clusters.

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

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

In this instructor-led, live training in Plovdiv, participants will learn how to use Python and Spark together to analyze big data as they work on 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 course offers a hands-on introduction to developing scalable data processing and Machine Learning workflows with PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and how to effectively manage large datasets by leveraging distributed computing principles.

This instructor-led live training in Plovdiv (online or onsite) is designed for engineers aiming to set up and deploy an Apache Spark system for processing massive data volumes.

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

• Install and configure Apache Spark.
• Efficiently process and analyze extensive data sets.
• Distinguish between Apache Spark and Hadoop MapReduce and understand their respective use cases.
• Integrate Apache Spark with external machine learning tools.

The initial learning curve for Apache Spark can be steep, requiring significant effort to achieve early results. This course is designed to help learners overcome that challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, become proficient in both Python and Scala APIs, and gain a solid understanding of executors, tasks, and related concepts. Aligning with industry best practices, the curriculum places a strong emphasis on cloud deployment strategies, specifically within Databricks and AWS environments. Students will also explore the distinctions between AWS EMR and AWS Glue, one of AWS's most recent Spark-based services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

Spark SQL is the Apache Spark module designed for handling both structured and unstructured data. It provides insights into data structure and the computations being executed, which can be leveraged for performance optimizations. Spark SQL is commonly used to:
- Run SQL queries.
- Access data from an existing Hive deployment.

During this instructor-led live training (available onsite or remotely), participants will learn to analyze diverse datasets using Spark SQL.

Upon completion, participants will be able to:

• Install and set up Spark SQL.
• Conduct data analysis with Spark SQL.
• Query datasets in various formats.
• Visualize data and query outcomes.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical work.
• Hands-on implementation in a live lab environment.

Customization Options

• To request a customized training for this course, please contact us to arrange.