Talend Big Data Integration Training Course

This instructor-led, live training in Nigeria (online or onsite) is tailored for advanced-level data professionals who want to optimise data processing workflows, ensure data integrity, and implement robust data lakehouse solutions capable of handling the complexities of modern big data applications.

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

• Gain a thorough understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance across 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 optimise 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 Nigeria (online or onsite) is aimed 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 of Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

This instructor-led, live training in Nigeria (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.

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

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

Upon completing this training, participants will be equipped to:

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

Course Format

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

Customization Options

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

This training offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight 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 Nigeria (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 Nigeria (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 learning curve for Apache Spark can be steep at the beginning, requiring significant effort before seeing initial results. This course is designed to help you navigate those early challenges. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and become proficient with the Python and Scala APIs. You will also gain a solid understanding of executors and tasks. Aligning with best practices, the course places a strong emphasis on cloud deployment, specifically focusing on Databricks and AWS. Students will also learn the key differences 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 Nigeria, participants will learn how to use Python and Spark together to analyse big data while working through hands-on exercises.

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

• Learn how to use Spark with Python to analyse 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 Nigeria (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 serves as a comprehensive, data-driven platform that unifies big data capabilities, artificial intelligence, and governance into a single cohesive solution. Its Rocket and Intelligence modules empower organizations to conduct rapid data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led training session, available both online and onsite, is tailored 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 (UDFs), and sophisticated data logic.

Upon completing this training, participants will be equipped to:

• Effectively navigate and utilize the Stratio platform, specifically its Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analytical tasks.
• Utilize loops and conditional logic to manage data workflows and perform feature engineering.
• Develop and manage user-defined functions (UDFs) to facilitate reusable data operations within PySpark.

Course Delivery Format

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

Options for Course Customization

• For tailored training requirements, please reach out to us to arrange a customized session.

This instructor-led, live training in Nigeria (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 Nigeria (online or onsite) is designed for beginner to intermediate data analysts looking to deepen their skills and understanding in managing and enhancing data quality using Talend Data Stewardship.

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

• Develop a comprehensive understanding of the role data stewardship plays in upholding data quality.
• Use Talend Data Stewardship to manage data quality tasks.
• Create, assign, and manage tasks within Talend Data Stewardship, including customising workflows.
• Utilise the tool's reporting and monitoring capabilities to track data quality and stewardship efforts.

In this instructor-led live training in Nigeria, 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 single packages in various deployment environments.
• Understand and utilize 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 projects.
• 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.