Talend Big Data Integration Training Course

This instructor-led live training in Czech Republic (online or onsite) targets intermediate-level data scientists and engineers who wish to employ 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 is the leading framework for processing Big Data across server clusters. This course, lasting three days (with an optional fourth day), covers the business advantages and practical use cases of Hadoop and its surrounding ecosystem. Attendees will learn how to plan cluster deployment and expansion, as well as how to install, maintain, monitor, troubleshoot, and optimize Hadoop environments. Practical exercises include bulk data loading into clusters, exploring various Hadoop distributions, and installing and managing tools within the Hadoop ecosystem. The curriculum concludes with a discussion 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 balance of 60% lectures and 40% labs.

Apache NiFi is an open-source platform for data integration and event processing that operates on a flow-based model. It facilitates automated, real-time routing, transformation, and mediation of data between disparate systems, supported by a web-based user interface and granular control mechanisms.

This instructor-led live training, available either onsite or remotely, targets intermediate-level administrators and engineers looking to deploy, manage, secure, and optimize NiFi dataflows within production environments.

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

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

Course Format

• Interactive lectures combined with discussions on real-world architectures.
• Practical labs focused on building, deploying, and managing data flows.
• Scenario-based exercises conducted in a live laboratory environment.

Course Customization Options

• For customized training arrangements, please contact us.

In this instructor-led, live training in Czech Republic, 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 gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by leveraging distributed computing principles.

This instructor-led live training in Czech Republic (online or onsite) is aimed at engineers who wish to set up and deploy an 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 Czech Republic (online or onsite) is designed for system administrators with beginner to intermediate skill levels who want to deploy, maintain, and optimize Spark clusters.

Upon completing this training, participants will be able to:

• Install and configure Apache Spark across 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 initial learning curve for Apache Spark can be steep, requiring significant effort before yielding tangible results. This course is designed to help you overcome that initial hurdle. Upon completion, participants will gain a solid understanding of Apache Spark fundamentals, clearly distinguish between RDDs and DataFrames, and become proficient with both the Python and Scala APIs. You will also develop a deep understanding of executors, tasks, and other core concepts. Adhering to industry best practices, the curriculum places strong emphasis on cloud deployment strategies, with a focus on Databricks and AWS environments. Additionally, students will explore the distinctions between AWS EMR and AWS Glue, examining one of the latest Spark-based services offered by AWS.

AUDIENCE:

Data Engineers, DevOps Engineers, Data Scientists

In this instructor-led, live training in Czech Republic, 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 Czech Republic (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 dedicated to processing structured and semi-structured data. It exposes metadata regarding data structure and the computations involved, enabling the execution of performance optimizations. Spark SQL is commonly utilized for: - executing SQL queries. - accessing data from an existing Hive deployment.

In this instructor-led live training (available onsite or remotely), participants will learn how to analyze diverse datasets using Spark SQL.

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

• Install and configure Spark SQL.
• Conduct data analysis with Spark SQL.
• Query datasets in various formats.
• Visualize data and query results.

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 program for this course, please contact us to arrange details.

Stratio is a data-centric platform that unifies big data, AI, and governance into a single solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics within enterprise environments.

This instructor-led, live training (available online or onsite) is designed for intermediate-level data professionals who want to effectively leverage the Rocket and Intelligence modules in Stratio with 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 work efficiently 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 feature engineering processes.
• Create and manage user-defined functions (UDFs) to enable reusable data operations in PySpark.

Course Format

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

Course Customization Options

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

This instructor-led live training in Czech Republic (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.
• Understand and implement the core principles of Talend management.
• Build, deploy, and execute business projects or tasks within Talend.
• Monitor dataset security and develop business routines aligned with the TAC framework.
• Gain a comprehensive understanding of big data applications.

This instructor-led live training, delivered Czech Republic (online or onsite), is designed for beginner to intermediate data analysts eager to deepen their expertise in managing and enhancing data quality using Talend Data Stewardship.

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

• Comprehend the critical role of data stewardship in upholding data quality.
• Utilize Talend Data Stewardship to manage data quality activities.
• Establish, assign, and control tasks within Talend Data Stewardship, including workflow customization.
• Employ the tool's reporting and monitoring functions to oversee data quality and stewardship outcomes.