Programming with Big Data in R Training Course

This instructor-led, live training in Serbia (online or onsite) is aimed at intermediate-level advanced R users who wish to use R to build faster workflows, improve code quality, and handle more complex analysis tasks.

By the end of this training, participants will be able to: create reusable functions, improve data workflows, debug and optimize code, and produce reproducible reports.

This instructor-led live training in Serbia (available online or onsite) is targeted at intermediate-level data scientists and engineers interested in employing Google Colab and Apache Spark for big data processing and analytics.

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

• Configure a big data environment using Google Colab and Spark.
• Efficiently process and analyze large datasets via Apache Spark.
• Integrate Apache Spark with cloud-based tools.

This instructor-led, live training in Serbia (online or onsite) is aimed at beginner-level data analysts who wish to use R programming to manipulate data, perform basic data analysis, and create compelling visualizations for insights.

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

• Understand the basics of R Programming.
• Apply fundamental data science processes.
• Create visual representations of data.

This instructor-led live training (online or onsite) is designed for data engineers, analysts, and professionals who wish to use Databricks and PySpark to build scalable data pipelines and migrate existing SQL workflows.

This course offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insights into how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by applying distributed computing principles.

This hands-on, three-day program is dedicated to designing and tuning high-efficiency data processing pipelines that leverage PySpark, Pandas, and Polars within Kubernetes-based ecosystems.

Learners will gain a working knowledge of how Spark applications are orchestrated on Kubernetes, with a specific focus on how configuration choices at the application layer directly impact throughput, scalability, resource utilization, and operational expenditure. Key areas of optimization covered include executor dimensioning, memory distribution, dynamic resource allocation, partitioning methodologies, shuffle mechanics, mitigating small-file issues, and streamlined Parquet data handling.

Additionally, the curriculum tackles frequent bottlenecks associated with Pandas, such as memory constraints and out-of-memory exceptions, while presenting Polars as a high-performance alternative for specific processing tasks. Through interactive lab sessions, participants will learn to pinpoint performance and memory inefficiencies, evaluate various configuration approaches, and implement optimization strategies for realistic ETL and machine learning use cases.

The core objective of this training is practical decision-making: mastering the identification of system bottlenecks, selecting the right tool for the job, configuring Spark for maximum efficiency, and striking a balance between processing speed and infrastructure resource costs.

This instructor-led, live training in Serbia (online or onsite) is aimed at engineers who wish to set up and deploy 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.

This instructor-led, live training in Serbia (online or onsite) is aimed at beginner-level to intermediate-level 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 held in Serbia, participants will discover how to combine Python and Spark for big data analysis while engaging in practical, hands-on activities.

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

• Leverage Spark and Python to conduct Big Data analysis.
• Complete exercises that simulate real-world business challenges.
• Employ various tools and methodologies for big data analysis via PySpark.

This instructor-led, live training in Serbia (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.