Programming with Big Data in R Training Course

This instructor-led, live training in Bulgaria (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 Bulgaria (online or onsite) is aimed at business analysts who wish to automate trade with algorithmic trading, Python, and R.

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

• Employ algorithms to buy and sell securities at specialized increments rapidly.
• Reduce costs associated with trade using algorithmic trading.
• Automatically monitor stock prices and place trades.

This instructor-led, live training in Bulgaria (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.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. During this three-day course (with a four-day option available), participants will explore the business advantages and practical applications of Hadoop and its broader ecosystem. The curriculum covers cluster deployment planning and scalability strategies, as well as the installation, maintenance, monitoring, troubleshooting, and optimization of Hadoop environments. Attendees will gain hands-on experience with bulk data loading, familiarize themselves with various Hadoop distributions, and practice installing and managing ecosystem tools. The course concludes with a session on securing clusters using Kerberos.

“…The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Target Audience

Hadoop system administrators

Course Format

A blend of lectures and hands-on labs, with an approximate split of 60% lecture content and 40% practical lab work.

Apache NiFi is an open-source, flow-based data integration and event-processing platform. It enables automated, real-time data routing, transformation, and system mediation between disparate systems, with a web-based UI and fine-grained control.

This instructor-led, live training (onsite or remote) is aimed at intermediate-level administrators and engineers who wish to deploy, manage, secure, and optimize NiFi dataflows in production environments.

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

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows from varied sources and sinks.
• Implement flow automation, routing, and transformation logic.
• Optimize performance, monitor operations, and troubleshoot issues.

Format of the Course

• Interactive lecture with real-world architecture discussion.
• Hands-on labs: building, deploying, and managing flows.
• Scenario-based exercises in a live-lab environment.

Course Customization Options

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

In this instructor-led, live training in Bulgaria, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.

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

• Understand NiFi's architecture and dataflow concepts.
• Develop extensions using NiFi and third-party APIs.
• Custom develop their own Apache Nifi processor.
• Ingest and process real-time data from disparate and uncommon file formats and data sources.

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 Bulgaria (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.

This instructor-led live training in Bulgaria (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.

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

In this instructor-led, live training in Bulgaria, 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 instructor-led, live training in Bulgaria (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.

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.

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.