Talend Big Data Integration Training Course

This instructor-led, live training in Kenya (online or onsite) is targeted at advanced-level data professionals who aim to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions capable of handling the complexities of modern big data applications.

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

• Gain a deep 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 Kenya (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 live, instructor-led training delivered in Kenya (online or on-site) is designed for intermediate-level data scientists and engineers who aim 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 the automated, real-time routing, transformation, and mediation of data between diverse systems, featuring a web-based interface and granular control capabilities.

This instructor-led live training, available either onsite or remotely, is tailored for intermediate-level administrators and engineers looking 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 destined for different sinks.
• Implement logic for flow automation, routing, and data transformation.
• Optimize performance, monitor operational metrics, and resolve technical issues.

Course Format

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

Course Customization Options

• To request tailored training for this course, please get in touch with us to arrange the details.

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

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

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

• Install and configure Apache Spark across various environments.
• Manage cluster resources and monitor Spark applications.
• Enhance 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 first phase. 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, tasks, and other core concepts. Guided by best practices, the course places a strong emphasis on cloud deployment, with dedicated focus on Databricks and AWS. Additionally, students will explore the differences between AWS EMR and AWS Glue, highlighting one of AWS's latest Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Kenya, 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 Kenya (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-centric platform that unifies big data capabilities, artificial intelligence, and governance into one cohesive solution. Through its Rocket and Intelligence modules, organizations can accelerate data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led training, available either online or onsite, is designed for intermediate data professionals eager to leverage Stratio’s Rocket and Intelligence modules effectively with PySpark. The curriculum emphasizes looping structures, user-defined functions, and sophisticated data logic.

Upon completing this training, participants will be equipped to:

• Navigate the Stratio platform and utilize the Rocket and Intelligence modules proficiently.
• Apply PySpark for data ingestion, transformation, and analytical tasks.
• Employ loops and conditional logic to streamline data workflows and manage feature engineering.
• Develop and manage user-defined functions (UDFs) to enable reusable data operations within PySpark.

Course Format

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

Customization Options

• For personalized training arrangements, please contact us directly.

This instructor-led live training, held in Kenya (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 completing this training, participants will be capable of:

• Installing and configuring Talend Administration Center
• Gaining a thorough understanding of and implementing core Talend management principles
• Creating, deploying, and executing business projects or tasks within Talend
• Monitoring dataset security and establishing business routines aligned with the TAC framework
• Developing a comprehensive understanding of big data applications

This instructor-led live training, conducted either online or onsite, is intended for beginner to intermediate data analysts seeking to deepen their skills and understanding in managing and improving data quality via Talend Data Stewardship.

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

• Understand the critical role of data stewardship in preserving data quality.
• Apply Talend Data Stewardship to manage data quality tasks.
• Create, assign, and manage tasks within Talend Data Stewardship, including customizing workflows.
• Use reporting and monitoring tools to assess data quality and stewardship outcomes.

In this instructor-led live training held in Kenya, 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 a variety of deployment environments.
• Understand and utilize Talend Open Studio's most used components.
• 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.