Talend Big Data Integration Training Course

This instructor-led live training in Slovakia (online or onsite) is intended for intermediate-level data scientists and engineers who aim to utilize Google Colab and Apache Spark for big data processing and analytics.

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

• Configure 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 an optional fourth day), participants will gain insight into the business advantages and practical use cases of Hadoop and its ecosystem. They will learn how to plan for cluster deployment and future expansion, as well as how to install, maintain, monitor, troubleshoot, and optimize Hadoop environments. Additionally, attendees will practice bulk data loading, explore various Hadoop distributions, and hands-on manage Hadoop ecosystem tools. The course concludes with a discussion on securing clusters using Kerberos.

“The materials were exceptionally well-prepared and thoroughly covered. The laboratory sessions were extremely helpful and clearly 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 balance of 60% lectures and 40% labs.

Apache NiFi is an open-source, flow-based platform for data integration and event processing. It facilitates automated, real-time routing, transformation, and mediation between different systems, featuring a web-based interface and granular control capabilities.

This instructor-led live training (available onsite or remotely) is designed for intermediate-level administrators and engineers who want to deploy, manage, secure, and optimize NiFi dataflows in production environments.

Upon completing this training, participants will be able to:

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

Course Format

• Interactive lectures featuring real-world architecture discussions.
• Hands-on labs focused on building, deploying, and managing flows.
• Scenario-based exercises conducted in a live-lab environment.

Course Customization Options

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

In this instructor-led, live training in Slovakia, 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 developing scalable data processing and Machine Learning workflows with PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by applying distributed computing principles.

This instructor-led, live training in Slovakia (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 Slovakia (online or onsite) targets beginner to intermediate system administrators who wish to deploy, maintain, and optimize Spark clusters.

Upon completion, participants will be able to:

• Install and configure Apache Spark across diverse environments.
• Manage cluster resources and monitor Spark applications.
• Optimize the performance of Spark clusters.
• Implement security measures to ensure high availability.
• Debug and troubleshoot common Spark issues.

While Apache Spark has a steep initial learning curve that demands significant effort before yielding results, this course is designed to help learners quickly overcome that initial hurdle. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and become proficient with both Python and Scala APIs. They will also gain a solid understanding of executors, tasks, and other core concepts. Aligned with industry best practices, the course places a strong emphasis on cloud deployment, specifically focusing on Databricks and AWS. Additionally, students will learn to differentiate between AWS EMR and AWS Glue, with special attention to AWS Glue as one of the latest Spark-based services offered by AWS.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Slovakia, 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 Slovakia (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 the component within Apache Spark designed for handling both structured and unstructured data. It exposes details regarding the data's structure alongside the computations being executed, enabling various performance optimizations. Spark SQL is commonly utilized for:
- running SQL queries.
- accessing data from an existing Hive deployment.

In this instructor-led live training (available onsite or remotely), attendees will gain the skills to analyze diverse datasets using Spark SQL.

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

• Installing and configuring Spark SQL.
• Conducting data analysis with Spark SQL.
• Executing queries on datasets in various formats.
• Visualizing data and the outcomes of queries.

Course Format

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

Customization Options

• For customized training requests, please reach out to us to make arrangements.

Stratio is 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 conduct rapid data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led live training (available online or on-site) targets intermediate-level data professionals who want to leverage the Rocket and Intelligence modules in Stratio effectively using PySpark. The focus is on mastering looping structures, user-defined functions, and implementing advanced data logic.

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

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

Course Format

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

Course Customization Options

• For customized training requirements for this course, please contact us to arrange your schedule.

This instructor-led, live training in Slovakia (online or onsite) is designed for system administrators, data scientists, and business analysts who wish to set up Talend Administration Center to deploy and manage organizational roles and tasks.

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

• Install and configure Talend Administration Center.
• Grasp and implement core Talend management principles.
• Create, deploy, and execute business projects or tasks within Talend.
• Monitor dataset security and develop business routines aligned with the TAC framework.
• Gain a deeper understanding of big data applications.

This instructor-led live training in Slovakia (online or onsite) is designed for beginner to intermediate-level data analysts who want to deepen their expertise in managing and enhancing data quality using Talend Data Stewardship.

By the end of the program, participants will be able to:

• Gain a comprehensive understanding of the role of data stewardship in maintaining data quality.
• Use Talend Data Stewardship for managing data quality tasks.
• Create, assign, and manage tasks within Talend Data Stewardship, including workflow customization.
• Use the tool's reporting and monitoring capabilities to track data quality and stewardship efforts.