Talend Big Data Integration Training Course

This instructor-led, live training in India (available online or onsite) is targeted at advanced-level data professionals seeking 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 conclusion of this training, participants will be able to:

• Acquire a deep 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 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 India (online or onsite) is designed for 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 India (online or onsite) is designed for intermediate-level data scientists and engineers looking to employ Google Colab and Apache Spark for big data processing and analytics.

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

• Establish a big data environment using Google Colab and Spark.
• Process and analyse large datasets efficiently with Apache Spark.
• Visualise big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Apache NiFi is an open-source platform for data integration and event processing that relies on flow-based architecture. It facilitates automated, real-time data routing, transformation, and system mediation between disparate systems, featuring a web-based UI and fine-grained control capabilities.

This instructor-led live training (available onsite or remotely) is designed for intermediate-level administrators and engineers who aim to deploy, manage, secure, and optimize NiFi dataflows in 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 transformation.
• Optimizing performance, monitoring operations, and resolving issues.

Course Format

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

Course Customization Options

• For custom training requests regarding this course, please reach out to us to make arrangements.

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 India (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 India (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.

Initially, the learning curve for Apache Spark can be steep, requiring significant effort before yielding tangible results. This course is designed to help you navigate that initial challenging phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and gain proficiency in the Python and Scala APIs. The curriculum covers essential concepts such as executors and tasks, alongside best practices with a strong emphasis on cloud deployment using Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, one of the latest Spark services offered by AWS.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led live training India, participants will learn how to combine Python and Spark to analyze big data while working through practical, 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 India (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 sophisticated analytics within enterprise settings.

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 advanced data logic.

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

• Navigate and effectively utilize the Stratio platform, specifically 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.
• Develop and manage user-defined functions (UDFs) to enable 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.

Customization Options

• To arrange a customized training session for this course, please reach out to us.

This instructor-led, live training in India (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 organisation's roles and tasks.

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

• Install and configure Talend Administration Center.
• Understand and implement fundamental Talend management principles.
• Create, 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 India (online or onsite) is tailored for beginner to intermediate-level data analysts who wish to deepen their knowledge and skills in managing and enhancing 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.

During this instructor-led live training conducted at India, participants will learn how to utilize 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.