Talend Big Data Integration Training Course

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level data scientists and engineers who wish 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.

Informatica with Big Data (BDM) is a program designed to equip data professionals with the skills to develop, manage, and analyze large datasets, utilizing the latest technologies and architectures in the Big Data field. The course covers the entire data lifecycle, from ingestion, integration, cleansing, and curation to analytics and the exposure and consumption of big data services.
In this course, participants will explore solutions that process large datasets using Big Data technologies and architectures such as Apache Hive, Apache Hadoop, and Apache Spark. The course also provides hands-on experience with Informatica tools like Bloombox, Big Data Management, and iData Fabric to understand big data technologies such as MapReduce and Hadoop. By the end of the course, learners will be capable of creating end-to-end data solutions using Informatica and its associated Big Data offerings.

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.

Renowned for its clear syntax and code readability, Python is a high-level programming language. Spark serves as a powerful engine for processing big data, enabling efficient querying, analysis, and transformation. PySpark bridges the two, allowing users to interface Spark with Python.

Target Audience: This course is designed for intermediate-level banking professionals who are already familiar with Python and Spark and wish to enhance their expertise in big data processing and machine learning.

This training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows with PySpark. Participants will learn how Apache Spark functions within contemporary Big Data ecosystems and how to efficiently manage large datasets using distributed computing principles.

This instructor-led, live training in Uzbekistan (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 Uzbekistan (online or onsite) is aimed at beginner-level to intermediate-level 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 initial learning curve for Apache Spark can be steep, requiring significant effort before yielding results. This course is designed to help you navigate that challenging early stage. 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. Learners will also develop a solid understanding of executors, tasks, and other core concepts. Aligned with industry best practices, the course places strong emphasis on cloud deployment strategies, 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 Engineers, DevOps Professionals, Data Scientists

In this instructor-led live training in Uzbekistan, participants will learn how to leverage Python and Spark together to analyze big data while completing hands-on exercises.

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

• Master the use of Spark with Python to analyze Big Data.
• Complete exercises that simulate real-world scenarios.
• Apply various tools and techniques for big data analysis using PySpark.

This instructor-led live training in Uzbekistan (available online or on-site) is designed for developers who want 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 data-centric platform that integrates big data, AI, and governance into a single solution. Its Rocket and Intelligence modules enable rapid data exploration, transformation, and advanced analytics in enterprise environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data professionals who wish to use the Rocket and Intelligence modules in Stratio effectively with PySpark, focusing on looping structures, user-defined functions, and advanced data logic.

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

• Navigate and work within the Stratio platform using Rocket and Intelligence modules.
• Apply PySpark in the context of data ingestion, transformation, and analysis.
• Use loops and conditional logic to control data workflows and feature engineering tasks.
• Create and manage user-defined functions (UDFs) for reusable data operations in PySpark.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

This instructor-led, live training in Uzbekistan (online or on-site) is designed for system administrators, data scientists, and business analysts who aim 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 apply core Talend management principles.
• Build, deploy, and execute business projects or tasks within Talend.
• Monitor dataset security and develop business routines based on the TAC framework.
• Gain a broader understanding of big data applications.

This instructor-led, live training in Uzbekistan (online or on-site) is designed for beginner to intermediate-level data analysts who aim to deepen their knowledge and enhance their 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 high-quality data.
• Leverage Talend Data Stewardship to manage data quality tasks effectively.
• Create, assign, and manage tasks within Talend Data Stewardship, including customising workflows.
• Utilise the tool's reporting and monitoring features to track data quality progress and stewardship initiatives.

In this instructor-led, live training in Uzbekistan, 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 deploy ESB technologies as unified packages across diverse deployment environments.
• Understand and leverage the most commonly used components within Talend Open Studio.
• Integrate any application, database, API, or web service seamlessly.
• Effortlessly connect heterogeneous systems and applications.
• Embed existing Java code libraries to extend project capabilities.
• Utilize community-driven components and code to enhance and expand projects.
• Quickly integrate systems, applications, and data sources using the intuitive drag-and-drop interface of the Eclipse environment.
• Reduce development time and maintenance costs by generating optimized, reusable code.