Talend Big Data Integration Training Course

This instructor-led, live training in Serbia (online or onsite) targets advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can handle the complexities of modern big data applications.

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

• Gain an in-depth understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance in various environments and integrate it with multiple data processing engines.
• Manage large-scale Iceberg tables, perform complex schema changes, and handle partition evolution.
• Master techniques to optimize query performance and data scan efficiency for large datasets.
• Implement mechanisms to ensure data consistency, manage transactional guarantees, and handle failures in distributed environments.

This instructor-led live training in Serbia (online or onsite) is intended for beginner-level data professionals who want to acquire the knowledge and skills needed to effectively use Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing data processing workflows.

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

• Gain a thorough understanding of Apache Iceberg's architecture, features, and benefits.
• Learn about table formats, partitioning, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg in various environments.
• Create, manage, and manipulate Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

This instructor-led live training in Serbia (available online or onsite) is targeted at intermediate-level data scientists and engineers interested in employing Google Colab and Apache Spark for big data processing and analytics.

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

• Configure a big data environment using Google Colab and Spark.
• Efficiently process and analyze large datasets via Apache Spark.
• Integrate Apache Spark with cloud-based tools.

Apache NiFi is an open-source platform designed for flow-based data integration and event processing. It facilitates automated, real-time data routing, transformation, and system mediation between diverse systems, supported by a web-based UI and granular control mechanisms.

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

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

• Installing, configuring, and maintaining Apache NiFi clusters.
• Designing and managing dataflows across various sources and sinks.
• Implementing logic for flow automation, routing, and data transformation.
• Optimizing performance, monitoring operations, and troubleshooting issues.

Course Format

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

Customization Options

• For customized training tailored to your needs, please contact us to arrange.

This course offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insights into 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 Serbia (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 Serbia (online or onsite) is designed for beginner to intermediate-level system administrators who want to deploy, maintain, and optimize Spark clusters.

Upon completing this training, participants will be able to:

• Install and configure Apache Spark across different environments.
• Manage cluster resources and monitor Spark applications.
• Enhance the performance of Spark clusters.
• Implement security protocols and ensure high availability.
• Debug and resolve common Spark issues.

The initial learning curve for Apache Spark is steep, requiring significant effort before yielding tangible results. This course is designed to help you navigate that challenging first phase. Upon completion, participants will grasp the fundamental concepts of Apache Spark, clearly distinguish between RDDs and DataFrames, master the Python and Scala APIs, and comprehend the roles of executors and tasks. Adhering to industry best practices, the curriculum places a strong emphasis on cloud deployment, 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 services.

AUDIENCE:

Data Engineer, DevOps, Data Scientist

In this instructor-led, live training in Serbia, participants will learn how to combine Python and Spark to analyze big data while engaging in 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 Serbia (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.

Stratio is a comprehensive, data-centric platform that unifies big data capabilities, artificial intelligence, and governance into a single, cohesive solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics, making it ideal for enterprise environments.

This instructor-led live training, available both online and onsite, is designed for intermediate-level data professionals eager to master the Rocket and Intelligence modules within the Stratio ecosystem using PySpark. The curriculum emphasizes looping structures, user-defined functions (UDFs), and complex data logic.

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

• Efficiently navigate and operate within the Stratio platform, utilizing both Rocket and Intelligence modules.
• Apply PySpark effectively for data ingestion, transformation, and analytical tasks.
• Utilize loops and conditional logic to orchestrate data workflows and execute feature engineering.
• Develop and manage user-defined functions (UDFs) to create reusable data operations in PySpark.

Course Format

• Interactive lectures and group discussions.
• Extensive exercises and practical practice sessions.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

• For customized training tailored to your specific needs, please reach out to us to make arrangements.

This instructor-led live training in Serbia (online or onsite) is aimed at system administrators, data scientists, and business analysts who wish to set up Talend Administration Center to deploy and manage the organization's roles and tasks.

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

• Install and configure Talend Administration Center.
• Understand and implement Talend management fundamentals.
• Build, deploy, and run business projects or tasks in Talend.
• Monitor the security of datasets and develop business routines based on the TAC framework.
• Obtain a broader comprehension of big data applications.

This instructor-led, live training in Serbia (online or onsite) is designed for data analysts at beginner to intermediate levels who wish to deepen their understanding and skills in managing and improving data quality using Talend Data Stewardship.

By the end of this training, 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.

In this instructor-led, live training conducted in Serbia, participants will learn how to utilize Talend Open Studio for ESB to create, connect, mediate, and manage services and their interactions.

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

• Integrate, enhance, and deliver ESB technologies as single packages across various deployment environments.
• Understand and utilize the most commonly used components of Talend Open Studio.
• Integrate any application, database, API, or web service.
• Seamlessly integrate heterogeneous systems and applications.
• Embed existing Java code libraries to extend project functionality.
• Leverage community components and code to extend projects.
• Rapidly integrate systems, applications, and data sources within a drag-and-drop Eclipse environment.
• Reduce development time and maintenance costs by generating optimized, reusable code.