Talend Big Data Integration Training Course

This instructor-led live training in Italy (online or onsite) is intended for intermediate-level data scientists and engineers seeking to utilize Google Colab and Apache Spark for big data processing and analytics.

Upon completing this training, participants will be capable of:

• Establishing a big data environment using Google Colab and Spark.
• Efficiently processing and analyzing large-scale datasets with Apache Spark.
• Visualizing big data within a collaborative framework.
• Integrating Apache Spark with cloud-based tools.

Apache Hadoop stands as the leading framework for processing Big Data across server clusters. This intensive three-day course (with an optional fourth day) equips attendees with a deep understanding of the business value and practical use cases of Hadoop and its surrounding ecosystem. Participants will learn how to plan for cluster deployment and scalability, as well as master the installation, maintenance, monitoring, troubleshooting, and optimization of Hadoop systems. The curriculum includes hands-on practice with bulk data loading, exploration of various Hadoop distributions, and the management of ecosystem tools. The course concludes with an in-depth discussion on securing clusters using Kerberos.

\u201c\u2026The materials were exceptionally well-prepared and thoroughly covered. The lab sessions were very helpful and meticulously organized.\u201d
\u2014 Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Professionals serving as Hadoop administrators.

Format

A blend of lectures and hands-on laboratories, maintaining an approximate split of 60% theoretical instruction and 40% practical labs.

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, supported by a web-based interface that offers granular control.

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

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

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows connecting various sources and sinks.
• Implement logic for flow automation, routing, and transformation.
• Optimize performance, monitor operations, and troubleshoot issues.

Format of the Course

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

Course Customization Options

• For information on arranging customized training for this course, please contact us.

In this instructor-led, live training in Italy, 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 course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Attendees will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process extensive datasets efficiently by leveraging distributed computing principles.

This instructor-led, live training in Italy (online or onsite) is designed for engineers who want to set up and deploy an Apache Spark system for processing vast 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 datasets.
• Understand the distinctions between Apache Spark and Hadoop MapReduce, and identify when to use each.
• Integrate Apache Spark with other machine learning tools.

This instructor-led, live training in Italy (online or onsite) targets beginner to intermediate 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 considerable effort before yielding tangible results. This course is designed to help learners navigate this 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. They will also gain insight into executors, tasks, and other core concepts. Aligned with best practices, the course places a strong emphasis on cloud deployment, with specific attention to Databricks and AWS. Students will also explore the distinctions between AWS EMR and AWS Glue, one of AWS's more recent Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

In this instructor-led, live training in Italy, participants will learn how to combine Python and Spark to analyze big data while engaging in 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 Italy (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 represents Apache Spark’s module designed for managing structured and unstructured data. It offers insights into the data structure as well as the computations being executed, enabling the system to perform optimizations. Spark SQL is commonly utilized for two primary purposes: executing SQL queries and accessing data from an existing Hive installation.

Through this instructor-led live training (available on-site or remotely), participants will gain the skills to analyze diverse data sets using Spark SQL.

Upon completing this training, participants will be equipped to:

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

Course Format

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

Customization Options

• For those seeking a tailored training experience, please contact us to arrange it.

Stratio serves as a unified, data-centric platform that seamlessly combines big data capabilities, artificial intelligence, and governance. Its Rocket and Intelligence modules empower organizations to rapidly explore, transform, and analyze data with advanced analytics tailored for enterprise needs.

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 within Stratio using PySpark. The curriculum emphasizes looping structures, user-defined functions (UDFs), and sophisticated data logic.

Upon completion, participants will gain the ability to:

• Effectively navigate and utilize the Rocket and Intelligence modules within the Stratio platform.
• Implement PySpark for efficient data ingestion, transformation, and analytical processes.
• Control data workflows and execute feature engineering tasks using loops and conditional logic.
• Develop and manage user-defined functions (UDFs) to facilitate reusable data operations in PySpark.

Training Format

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

Customization Options

• For tailored training requests, please contact us to make arrangements.

This instructor-led, live training in Italy (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.

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

• Install and configure Talend Administration Center.
• Understand and implement core Talend management principles.
• Build, deploy, and execute business projects or tasks within Talend.
• Monitor data security and develop business routines based on the TAC framework.
• Gain a broader understanding of big data applications.

This instructor-led live training in Italy (online or onsite) targets beginner to intermediate data analysts aiming to deepen their expertise in managing and enhancing data quality using Talend Data Stewardship.

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

• Acquire a comprehensive grasp of the role data stewardship plays in maintaining high data quality.
• Apply Talend Data Stewardship for the management of data quality tasks.
• Create, assign, and manage tasks within Talend Data Stewardship, including customizing workflows.
• Utilize the tool’s reporting and monitoring features to track data quality and stewardship activities.