Technologies

Large-scale data analysis in R

Training on large-scale data analysis in R, covering memory optimization, parallel processing and integration with databases and Apache Spark. The program focuses on practical use of data.table, dplyr, future, foreach and sparklyr packages for efficient work with datasets that exceed the capabilities of standard tools. Participants will learn chunked processing techniques, memory profiling and query optimization.

Why choose this training?

Modern data analysis increasingly requires working with datasets that exceed the capabilities of standard R tools. This two-day training prepares analysts and Data Scientists for efficient processing of large data volumes — from optimizing data structures through data.table and dplyr, through parallel processing with future and foreach packages, to database integration (DBI, dbplyr) and distributed processing in Apache Spark via sparklyr. The program emphasizes practical techniques for memory management, chunked processing and choosing the right data formats.

After completing the training, participants will be able to: efficiently process large datasets using data.table and dplyr, implement parallel processing with future and foreach packages, optimize RAM usage and apply chunked processing techniques, integrate R with databases through DBI and dbplyr. These competencies directly translate into higher efficiency in IT project execution.

This training is particularly valuable for: data analysts working with large datasets, Data Scientists using R in their daily work, statisticians and researchers processing large data volumes.

What sets our approach apart?

At EITT, we believe the best learning happens through practice. During 2 days of intensive training, participants work on real-world examples and scenarios, ensuring not only theoretical understanding but above all the ability to apply it in practice.

With over 2,500 trainings in our portfolio and a 4.8/5 rating from participants, EITT is a trusted partner in competency development for organizations of all sizes. Our trainers are practitioners with years of experience who share current knowledge and proven solutions.

Looking for training tailored to your team’s needs? Contact us — we’ll prepare a program customized to your requirements.

Benefits

Participants will be able to efficiently process large datasets in R using data.table and dplyr
They will gain the ability to implement parallel processing with future and foreach packages
They will learn to optimize RAM usage and apply chunked processing techniques
They will learn methods of integrating R with databases through DBI and dbplyr
They will be able to configure and use Apache Spark through sparklyr for distributed processing
They will develop skills in profiling R code performance and identifying bottlenecks
They will master techniques for working with data formats optimized for large datasets (Arrow, fst, Parquet)

Who is this training for?

Data analysts working with large datasets

Data Scientists using R in their daily work

Statisticians and researchers processing large data volumes

R programmers looking to optimize analysis performance

Business Intelligence specialists

Financial analysts working with market data

Data engineers responsible for analytical pipelines

Prerequisites

Intermediate knowledge of R programming language
Basic familiarity with tidyverse (dplyr, tidyr)
Experience in tabular data analysis
Basic knowledge of SQL

Training program

data.table package — syntax, indexing and group operations
Comparing data.table vs dplyr for performance
Advanced dplyr operations on large datasets
Memory profiling and object management in R

future framework — planning and executing parallel tasks
foreach package with doParallel and doFuture backends
Data chunking strategies
Progress monitoring and error handling in parallel processing

Techniques for reducing RAM usage
Processing data in chunks (chunked processing)
Lazy evaluation and deferred computations
Memory-efficient data formats (fst, qs, Arrow)

DBI package — connections to PostgreSQL, MySQL, SQLite
dbplyr — translating dplyr code to SQL
Query optimization and data retrieval
Working with data directly in the database without loading into memory

Configuration and connecting to a Spark cluster
Distributed processing with dplyr interface
Spark SQL and operations on large tables
Integrating sparklyr with analytical pipelines

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Training Topics

Frequently asked questions

What are the prerequisites for this training?

The Large-scale data analysis in R training requires intermediate knowledge of R programming language, basic familiarity with tidyverse (dplyr, tidyr), experience in tabular data analysis and basic knowledge of SQL.

What is the format and duration of this training?

The training lasts 2 days and is available in online and on-site format.

Who is this training designed for?

This training is designed for data analysts working with large datasets, Data Scientists using R in their daily work, and statisticians and researchers processing large data volumes.