Programming with Big Data in R Training Course

This instructor-led, live training in Argentina (online or onsite) is designed for intermediate-level data scientists and engineers who intend 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. This comprehensive three-day course (with an optional fourth day) enables participants to understand the business advantages and practical applications of Hadoop and its ecosystem. Attendees will learn to plan for cluster deployment and scalability, and gain hands-on experience in installing, maintaining, monitoring, troubleshooting, and optimizing Hadoop environments. The curriculum includes practicing bulk data loading within clusters, exploring various Hadoop distributions, and managing ecosystem tools. The course concludes with a focus 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

Audience

Hadoop administrators

Format

A blend of lectures and hands-on labs, with an approximate distribution of 60% instructional time and 40% laboratory practice.

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 Argentina, 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 training offers a hands-on introduction to creating scalable data processing and Machine Learning workflows using PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently process large datasets by applying distributed computing principles.

This instructor-led, live training in Argentina (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 Argentina (online or onsite) is designed for 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 learning curve for Apache Spark can be steep at the outset, requiring significant effort before seeing initial results. This course is designed to help you overcome that initial hurdle. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and gain proficiency in both Python and Scala APIs. You will also learn about executors, tasks, and other core concepts. Adhering to best practices, the course places a strong emphasis on cloud deployment, specifically focusing on Databricks and AWS. Students will explore the differences between AWS EMR and AWS Glue, one of AWS's latest Spark-related services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

OBJECTIVE:

This course provides an introduction to Apache Spark. Participants will explore how Spark integrates into the Big Data ecosystem and gain skills in utilizing Spark for data analysis. The curriculum spans the Spark shell for interactive analysis, internal mechanisms, APIs, Spark SQL, streaming capabilities, as well as machine learning and GraphX functionalities.

AUDIENCE :

Developers and Data Analysts

This instructor-led, live training in Argentina (online or onsite) is designed for data scientists and developers who aim to leverage Spark NLP, built upon Apache Spark, to create, implement, and scale natural language text processing models and pipelines.

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

• Configure the necessary development environment to begin constructing NLP pipelines with Spark NLP.
• Gain a comprehensive understanding of the features, architecture, and advantages of using Spark NLP.
• Utilize the pre-trained models available in Spark NLP to execute text processing tasks.
• Acquire the skills to build, train, and scale Spark NLP models for production-ready projects.
• Apply classification, inference, and sentiment analysis techniques to real-world scenarios (such as clinical data and customer behavior insights).

In this instructor-led, live training in Argentina, participants will learn how to combine Python and Spark to analyze big data while working through practical exercises.

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

• Utilize Spark with Python for big data analysis.
• Complete exercises that simulate real-world scenarios.
• Apply various tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Argentina (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 serves as Apache Spark's specialized component for managing both structured and unstructured data. It exposes details regarding the data's structure and the computations involved, enabling the system to execute optimizations. The primary applications of Spark SQL include:
- Executing SQL queries.
- Accessing data from an existing Hive installation.

Through this instructor-led live training (available on-site or remotely), attendees will acquire the skills necessary to analyze diverse data sets using Spark SQL.

Upon completion of this training, participants will be capable of:

• Installing and setting up Spark SQL.
• Conducting data analysis via Spark SQL.
• Querying data sets in various formats.
• Visualizing data and query outcomes.

Course Format

• Engaging lectures and discussions.
• Numerous exercises and practical sessions.
• Practical implementation within a live-lab environment.

Customization Options

• To arrange a tailored training session for this course, please get in touch with us.

Stratio stands as a data-centric platform that seamlessly integrates big data, artificial intelligence, and governance into a unified solution. Its Rocket and Intelligence modules empower organizations to perform rapid data exploration, transformation, and advanced analytics within enterprise environments.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals aiming to effectively utilize Stratio's Rocket and Intelligence modules with PySpark. The curriculum focuses on mastering looping structures, user-defined functions, and implementing sophisticated data logic.

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

• Navigate and operate within the Stratio platform, proficiently using the Rocket and Intelligence modules.
• Apply PySpark effectively across data ingestion, transformation, and analysis workflows.
• Utilize loops and conditional logic to manage data workflows and execute feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to facilitate reusable data operations within PySpark.

Course Format

• Interactive lectures accompanied by guided discussions.
• Extensive exercises and practical practice sessions.
• Hands-on implementation within a live-lab environment.

Course Customization Options

• To request a customized training session for this course, please contact us to make arrangements.