Talend Big Data Integration Training Course

This instructor-led, live training in Greece (online or onsite) is designed for intermediate-level data scientists and engineers seeking to apply Google Colab and Apache Spark to 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 Hadoop stands as the leading framework for processing Big Data across server clusters. This three-day (or optionally four-day) program enables participants to understand the business advantages and real-world applications of Hadoop and its broader ecosystem. The curriculum covers planning for cluster deployment and scalability, as well as installing, maintaining, monitoring, troubleshooting, and optimizing Hadoop environments. Attendees will gain practical experience with bulk data loading, explore various Hadoop distributions, and manage ecosystem tools. The course concludes with a focus on securing clusters using Kerberos.

“The course materials were exceptionally well-prepared and thorough. The laboratory sessions were highly beneficial and expertly organized.”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop system administrators

Format

A blend of theoretical lectures and practical hands-on labs, maintaining an approximate ratio of 60% lectures to 40% lab work.

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 diverse systems, featuring a web-based UI that offers fine-grained control.

This instructor-led training session, available either on-site or remotely, targets intermediate-level administrators and engineers who aim 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 operational metrics, and troubleshoot issues.

Course Format

• Interactive lectures accompanied by discussions on real-world architectures.
• Hands-on labs focused on building, deploying, and managing data flows.
• Scenario-based exercises conducted in a live laboratory environment.

Course Customization Options

• For organizations seeking a tailored training experience for this course, please reach out to us to make arrangements.

In this instructor-led, live training in Greece, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.

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

• Understand NiFi's architecture and dataflow concepts.
• Develop extensions using NiFi and third-party APIs.
• Custom develop their own Apache Nifi processor.
• Ingest and process real-time data from disparate and uncommon file formats and data sources.

This training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows utilizing PySpark. Participants will discover 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 Greece (online or on-site) 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 Greece (online or onsite) targets beginner to intermediate system administrators who wish to deploy, maintain, and optimize Spark clusters.

Upon completing this training, participants will be able to:

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

While Apache Spark initially presents a steep learning curve requiring considerable effort to yield early results, this course is designed to help learners navigate and overcome that initial hurdle. Upon completion, participants will gain a solid grasp of Apache Spark fundamentals, clearly distinguish between RDDs and DataFrames, become proficient with the Python and Scala APIs, and understand core concepts such as executors and tasks. Aligned with industry best practices, the curriculum places a strong emphasis on cloud deployment, specifically targeting Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, which represents one of AWS's newer Spark-based services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

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

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

• Utilize 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 Greece (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.

Spark SQL serves as the module within Apache Spark designed for processing both structured and unstructured data. It offers insights into the data's structure and the computations being executed, enabling performance optimizations. Spark SQL is commonly utilized to:
- execute SQL queries;
- access data from existing Hive installations.

Through this instructor-led live training (available onsite or remotely), participants will learn to analyze diverse datasets using Spark SQL.

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

• Install and configure Spark SQL.
• Conduct data analysis with Spark SQL.
• Query datasets in various formats.
• Visualize data and query outcomes.

Course Format

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

Customization Options

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

Stratio offers 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-grade environments.

This instructor-led live training, available online or on-site, is designed for intermediate-level data professionals seeking to harness the full potential of Stratio’s Rocket and Intelligence modules using PySpark. The curriculum emphasizes looping structures, user-defined functions, and sophisticated data logic to enhance workflow efficiency.

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 within the Stratio ecosystem.
• Implement 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 paired with group discussions.
• Extensive hands-on exercises and practical practice sessions.
• Real-world implementation through a live-lab environment.

Customization Options

• For tailored training solutions, please reach out to us to discuss your specific requirements.

This instructor-led, live training in Greece (online or on-site) is designed for 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 Greece (online or onsite) is designed for beginner to intermediate data analysts who wish to deepen 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 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.