Talend Big Data Integration Training Course

This instructor-led, live training in Turkey (online or onsite) is aimed at 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 Turkey (online or onsite) is targeted at beginner-level data professionals who wish to acquire the knowledge and skills necessary to effectively utilize 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 different 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 Turkey (online or onsite) is tailored for intermediate-level data scientists and engineers who want to utilize 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 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 disparate systems, featuring a web-based user interface and granular control capabilities.

This instructor-led live training, available onsite or remotely, is designed for 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 connecting various sources and sinks.
• Implementing logic for flow automation, routing, and data transformation.
• Optimizing performance, monitoring operations, and resolving technical issues.

Course Format

• Interactive lectures complemented by real-world architecture discussions.
• Practical labs focused on building, deploying, and managing flows.
• Scenario-based exercises conducted in a live laboratory environment.

Customization Options

• To arrange a customized version of this training, please contact us.

This course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Attendees will explore how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently process extensive datasets using distributed computing principles.

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

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

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

Although the initial learning curve for Apache Spark can be steep, requiring significant effort to see results, this course is designed to help you navigate that challenging phase quickly. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and master both the Python and Scala APIs. You will also gain a deep understanding of executors, tasks, and other core concepts. Aligned with industry best practices, the curriculum places strong emphasis on cloud deployment, particularly within Databricks and AWS environments. Students will learn to differentiate between AWS EMR and AWS Glue, one of AWS’s latest Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Turkey, 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 Turkey (online or onsite) is designed for developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex datasets.

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

• Configure the necessary environment to begin processing big data using Spark, Hadoop, and Python.
• Gain a comprehensive understanding of the features, core components, and architecture of Spark and Hadoop.
• Master the integration of Spark, Hadoop, and Python for efficient big data processing.
• Explore key tools within the Spark ecosystem, including Spark MLlib, Spark Streaming, Kafka, Sqoop, and Flume.
• Develop collaborative filtering recommendation systems akin to those used by Netflix, YouTube, Amazon, Spotify, and Google.
• Utilize 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 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 master the Rocket and Intelligence modules in Stratio using PySpark. The curriculum emphasizes looping structures, user-defined functions, and sophisticated data logic.

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

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

Course Format

• Interactive lectures paired with group discussions.
• Extensive exercises and practical application sessions.
• Hands-on implementation within a live-lab environment.

Customization Options

• For organizations seeking customized training for this course, please contact us to arrange a tailored program.

This instructor-led live training in Turkey (online or onsite) targets system administrators, data scientists, and business analysts who intend to set up Talend Administration Center to deploy and manage organizational 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 Turkey (online or onsite) is aimed at beginner to intermediate-level data analysts 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 in Turkey, participants will learn how to use Talend Open Studio for ESB to create, connect, mediate, and manage services and their interactions.

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

• Integrate, enhance, and deliver ESB technologies as unified packages across various deployment environments.
• Understand and leverage the most frequently used components of Talend Open Studio.
• Integrate any application, database, API, or web services.
• Seamlessly integrate heterogeneous systems and applications.
• Embed existing Java code libraries to extend project capabilities.
• 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.