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Samuel Lenk

Data. Everywhere. All well-behaved in their own environment. But who actually lets them talk to each other? You do. With data integration. Become a data savant and add value with ETL and your new knowledge.

Talend Open Studio is an open, flexible data integration solution. You build your processes with a graphical editor and over 600 components provide flexibility.

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Data. Everywhere. All well-behaved in their own environment. But who actually lets them talk to each other? You do. With data integration. Become a data savant and add value with ETL and your new knowledge.

Talend Open Studio is an open, flexible data integration solution. You build your processes with a graphical editor and over 600 components provide flexibility.

Each section has a practical example and you will receive this complete material at the beginning of the course. So you can not only view each section, but also compare it to your own solution. There are also extensive practical scenarios included. So you'll be well equipped for practice.

What are the biggest topics you can expect?

  • Installation on different operating systems (Windows, Linux, Mac)

  • understanding and using important data types

  • reading and writing from databases

  • process different file formats, like Excel See you in the course.

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What's inside

Learning objectives

  • Connect your data sources, such as files, databases, xml, web services, google drive and more formats
  • Build your own integration processes using practical examples and comprehensive scenarios
  • Master the most important transformations like mappings, joins, aggregations and sorting
  • Orchestrate processes into larger units by using prejobs, postjobs, variable and hierachies

Syllabus

You will get an overview of the course structure.

You will get a quick overview of the course structure.

See your first 10 Minutes ETL in Talend Open Studio

Read more

Know the key points to get the most out of the course.

You will see why data integration is important.

Test your knowledge on some basics of data integration

You will get an overview of this section.

We review the steps of installing Talend Open Studio on Windows, Mac and Linux.

Change the interface language of Talend Open Studio to English, because translations are incomplete and the whole course project is in English.

Import the complete project for this course. This way you can compare any setting of any job of any video throughout the complete course.

With the archive attached to this lecture you obtain all files for the course.

We take a look at the structure of the course project.

Let's create a very easy "hello world" example to get you going.

Get a brief overview of the UI structure in Talend Open Studio.

Get to know some useful shortcuts and functionalities to start working effectively with Talend.

Some components need external libraries which can easily be installed.

Get some tips and tricks on how to resolve errors easier in Talend.

Shows you how to restore a tab once (accidentally) closed

We will build a more complete job together. It contains data creation, transformation and output.

Read many types of files easily in Talend.

We will see how to write a Excel file. But you can create many other formats as well.

List folder content by using search patterns. This way you can easily process several files with the same structure.

We will read, write and iterate over files. This will be extended by using variables and checking file existence before doing the actual work.

You learn the differences between "BuiltIn" and "Repository" information.

Learn what a "schema" is in Talend and how to use it effectively.

You will get an overview of some important data types.

You will get an overview of the different row and trigger connection types and their usage in Talend.

Learn the most important key shortcut and how to use it properly.

Test your knowledge about Talend Open Studio properties

Lets see which database you'll use for the following examples.

You will learn how to connect to a database using Talend.

You will write some data to a table in a database.

You will read some data from a table in a database.

You will list some some database objects of a certain type.

This scenario shows you how to make a lookup and output the result into a database.

You will learn how to read a JSON file with and without loops.

You will learn how to read XML files with loops.

You will learn how to access Google Drive folders using Talend.

You will learn how to access Google Cloud storage using Talend.

Learn how to use a REST service in Talend.

You will learn how to use Talend to download files from the web.

You will learn how to read RSS feeds in Talend.

Do you know which component is for what?

Learn an easy shortcut to create context variables in Talend.

Create variables and environments for variables (= "contexts") in your job.

You will learn what data types variables can have.

Learn how to store your variables in context groups in a central place.

You will learn to create context groups for metadata items very easily.

Talend allows you to load variables from an external source. Learn how to do that.

With an easy setting you can load variables implicitly. Learn how!

Learn what global variables are and how to use them.

Test your knowledge about variables!

You will see how to filter columns and rows in Talend.

Learn how to sort data of type date, numeric and alphanumeric.

You will learn how to create sums, averages and other aggregate values with Talend.

Learn some important data type conversions and some common traps.

Learn how to split data row-wise or data-wise in Talend

You will learn how to normalize and de-normalize values in Talend.

You will learn how to use tJoin in Talend for simple joins.

You will see how to install Sakila sample database. We will use this data for the next couple of examples.

Learn how to join data over more than 2 levels using tMap.

Learn how to filter data in tMap.

Learn how to transform data in a tMap.

We will do the PreJob and PostJob setup for this scenario.

You will learn how to use tExractJSONFields component to parse JSON data.

You will learn how to use tExractXMLFields component to parse XML data.

You will learn how to use tExractPositionalFields component to parse positional data.

You will learn how to use tExractDelimitedFields component to parse delimited data.

You will learn how to use tExractRegexFields component to parse data using regular expressions.

We will make a short summary of the field extraction components scenario.

Learn how to create JSON and XML fields using the corresponding components.

Learn two ways to generate sample data in Talend.

Learn how to pivot data in Talend.

Test your knowledge by answering this transformations quiz!

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Develops the skills and knowledge to connect data sources, build integration processes, master transformations, and orchestrate larger units with pre/post jobs, variables, and hierarchies
Teaches you the most important transformations, such as mappings, joins, aggregations, and sorting
Covers key concepts, like understanding and using important data types, reading and writing from databases, and processing different file formats, like Excel
Provides practical examples and comprehensive scenarios that equip you for hands-on practice
Uses an open, flexible data integration solution, Talend Open Studio
Instructed by Samuel Lenk, an experienced data integration expert

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Reviews summary

Hands-on talend etl for beginners

According to learners, this course is a highly recommended and comprehensive guide for anyone looking to master data integration and ETL with Talend Open Studio, especially beginners. Students highlight the instructor's ability to provide clear and concise explanations, making complex topics easy to grasp. The course's strong emphasis on a practical, hands-on approach with real-world examples and useful labs is a recurring positive, enabling immediate application of skills. While generally well-paced, a few reviews noted some sections felt a bit rushed or that the audio quality was inconsistent. One isolated but significant feedback pointed to the instructor's accent being difficult to understand for some. Overall, it's considered an excellent starting point for developing confidence in Talend.
Covers a wide range of topics from basics to advanced ETL techniques.
"Covers all the basic and intermediate topic in detail."
"I found the course structure well-organized, guiding me seamlessly from fundamental concepts to advanced ETL techniques."
"The comprehensive coverage of Talend components and best practices truly equipped me with the skills."
"It covers a lot of ground effectively."
Concepts are presented clearly, making complex topics digestible.
"The instructor's clear explanations, coupled with practical, hands-on examples, made complex topics easy to grasp."
"I was very impressed by how well the instructor delivered and explained the concepts."
"The explanations are clear and concise, and the exercises are very helpful. The instructor's pace is just right..."
"The instructor delivers complex information in a very digestible format."
Strong emphasis on real-world scenarios and practical exercises.
"What stood out the most were the real-world scenarios, which provided invaluable insights into applying Talend Open Studio effectively."
"The course is very hands-on and provides good examples."
"What I particularly appreciate is the practical, hands-on approach, which allowed me to immediately apply what I learned to real-world scenarios."
"The hands-on labs were especially valuable."
Highly accessible and a great starting point for new learners.
"Recommended for beginners. I wish I found this earlier."
"As a beginner, I found the course structure incredibly well-organized, guiding me seamlessly."
"Having minimal ETL experience, I was very impressed by how well the instructor delivered and explained concepts."
"Perfect for beginners who want to learn Talend. The step-by-step approach and clear explanations make it very accessible."
Suggests more practice exercises or a dedicated final project.
"More practice exercises or a dedicated project at the end would make it perfect."
Some felt the pace was too fast or explanations could be more in-depth.
"Sometimes I felt the pace was a bit too fast in certain sections, especially for absolute beginners."
"As an introduction to Talend, it felt a bit rushed at times; some explanations could have been more in-depth."
One learner found the instructor's accent challenging to understand.
"I found the instructor's accent extremely difficult to understand, which made it very hard to follow along."
"I really struggled and ultimately gave up trying to complete the course because of this."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Integration & ETL with Talend Open Studio Zero to Hero with these activities:
Organize and review course materials
Organizing and reviewing course materials will help you consolidate your understanding and prepare for assessments.
Browse courses on Note-Taking
Show steps
  • Gather and organize all course materials, such as lecture notes, assignments, and readings.
  • Review the materials regularly to reinforce your memory and identify areas for improvement.
  • Create summaries or mind maps to condense and simplify the information.
Review existing Talend projects
Reviewing existing Talend projects will help you familiarize yourself with the platform, its capabilities, and best practices.
Show steps
  • Identify and download existing Talend projects from online repositories or forums.
  • Examine the project structure, components used, and data flow.
  • Run the projects to understand their functionality and any potential issues.
Participate in study groups or online forums
Engaging in peer study groups or online forums will allow you to share knowledge, learn from others, and enhance your understanding.
Show steps
  • Join study groups or online forums dedicated to Talend or data integration.
  • Participate in discussions, ask questions, and share your own insights.
  • Collaborate on projects or assignments to gain practical experience.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create simple data integration jobs in Talend
Creating simple data integration jobs will allow you to apply your understanding of Talend components and data flow.
Browse courses on Data Ingestion
Show steps
  • Define the source and target data sources.
  • Use Talend components to perform data transformations, such as filtering, sorting, and joining.
  • Configure job settings and execute the job to process data.
Contribute to the Talend community forum or open-source projects
Contributing to the Talend community or open-source projects will deepen your understanding of Talend and its ecosystem.
Browse courses on Problem-Solving
Show steps
  • Join the Talend community forum and engage in discussions.
  • Identify open-source projects related to Talend and explore their codebase.
  • Contribute bug fixes, feature enhancements, or documentation improvements to the projects.
Follow online tutorials on advanced Talend techniques
Following online tutorials will expose you to advanced Talend techniques and best practices, enhancing your skills.
Browse courses on Job Scheduling
Show steps
  • Identify and select online tutorials that cover specific advanced Talend topics.
  • Follow the tutorials step-by-step, implementing the techniques in practice.
  • Experiment with different configurations and settings to understand their impact.
Develop a data integration project for a real-world scenario
Working on a real-world data integration project will provide you with hands-on experience and a comprehensive understanding of the entire data integration process.
Browse courses on Business Intelligence
Show steps
  • Identify a business problem or opportunity that can be addressed through data integration.
  • Design the data integration architecture, including data sources, transformations, and outputs.
  • Implement the data integration solution using Talend and other relevant tools.
  • Deploy and monitor the data integration solution to ensure its effectiveness.

Career center

Learners who complete Data Integration & ETL with Talend Open Studio Zero to Hero will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Integration & ETL with Talend Open Studio Zero to Hero.
The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.
Is specifically dedicated to the ETL process within a data warehousing context. It provides practical techniques and best practices for extracting, cleaning, transforming, and loading data. It's an excellent resource for understanding the intricacies of building robust ETL systems and is considered a key text for ETL developers and architects. This book is highly valuable as a reference tool for practitioners.
The third edition of the essential guide to data wrangling with pandas, NumPy, and Jupyter in Python. Updated for newer versions of the libraries, this book remains a core resource for anyone performing data analysis in Python. It widely used textbook and reference for its clear explanations and practical examples.
A monumental foundational text establishing the mathematical, algorithmic, and statistical principles of modern deep neural networks, from linear algebra and optimization to convolutional and sequence modeling architectures.
Provides an exceptionally practical, code-first curriculum that guides engineers from end-to-end data preprocessing and traditional regression pipelines to production deep learning models and generative neural networks.
Provides a comprehensive guide to Hadoop, the open-source framework for Big Data processing. It covers the core concepts and components of Hadoop, as well as advanced topics such as data warehousing and machine learning.
Provides a comprehensive overview of cloud computing, covering concepts, technologies, and architectures. It is written by leading experts in the field.
Delivers an authoritative mathematical foundation in Bayesian inference, graphical models, and statistical learning algorithms, bridging foundational probability theory with rigorous predictive computational modeling.
Guides engineers step-by-step through implementing, training, and fine-tuning complete generative transformer architectures in pure PyTorch, providing crystal-clear low-level insight into modern foundation models.
Provides a solid introduction to the field of data engineering, covering the entire data lifecycle from data generation to consumption. ETL core component of this lifecycle, and the book provides context and foundational knowledge for understanding its role within a larger data ecosystem. It's a great starting point for anyone entering the field.
Presents the foundational treatise on Markov decision processes, temporal-difference learning, and policy gradient methods that underpin autonomous decision-making and modern post-training alignment workflows.
Examines holistic architectural patterns for designing resilient machine learning infrastructure, addressing data pipelines, real-time feature stores, continual learning loops, and production deployment reliability.
Provides a comprehensive overview of data warehousing. It covers all the essential aspects of data warehousing, including data integration, data modeling, data storage, and data analysis. The author is an expert in the field and provides a wealth of practical advice.
Provides a foundational understanding of the fundamental principles of data science and the data-analytic thinking necessary for extracting value from data in a business context. It is highly relevant for undergraduate business analytics programs and working professionals. It serves as a useful reference for understanding the business applications of data analysis and is commonly used as a textbook.
Demystifies practical transformer model implementation through the Hugging Face ecosystem, demonstrating how to fine-tune, optimize, and deploy attention architectures across diverse natural language processing and understanding tasks.
While not exclusively about ETL, this book provides a deep dive into the fundamental concepts of data systems, including batch and stream processing, which are integral to modern ETL pipelines. It helps in understanding the trade-offs and design choices behind various data processing technologies. is essential for gaining a broader understanding of the landscape in which ETL operates and is highly recommended for architects and senior engineers.
Provides a comprehensive guide to Spark, the popular open-source framework for Big Data processing. It covers the core concepts and components of Spark, as well as advanced topics such as streaming data and machine learning.
This comprehensive guide provides a step-by-step approach to data transformation, covering topics such as data quality assessment, data cleaning, data integration, and data standardization. It is ideal for both business and IT professionals who need to understand and implement data transformation projects.
Provides a comprehensive overview of data quality, covering the key concepts, methodologies, and best practices. It valuable resource for anyone looking to improve the quality of their data.
Provides an in-depth introduction to machine learning, covering the fundamental concepts and algorithms used in Big Data analysis. It is written by Andrew Ng, a leading expert in machine learning, and is highly recommended for those who want to gain a deeper understanding of Big Data.
Provides a comprehensive overview of data quality, covering the key concepts, methodologies, and best practices. It valuable resource for anyone looking to improve the quality of their data.
Provides a comprehensive overview of data quality, covering the key concepts, methodologies, and best practices. It valuable resource for anyone looking to improve the quality of their data.
Provides a comprehensive overview of data warehousing with SQL Server 2019. It covers all the essential aspects of data warehousing, including data integration, data modeling, data storage, and data analysis. The authors are experts in the field and provide a wealth of practical advice.
Provides a comprehensive overview of data warehousing. It covers all the essential aspects of data warehousing, including data integration, data modeling, data storage, and data analysis. The authors are experts in the field and provide a wealth of practical advice.
Provides a comprehensive overview of data warehousing. It covers all the essential aspects of data warehousing, including data integration, data modeling, data storage, and data analysis. The author is an expert in the field and provides a wealth of practical advice.
Airflow popular platform for orchestrating complex data pipelines, including ETL workflows. provides a practical guide to using Airflow for building, scheduling, and monitoring data pipelines. It's highly relevant for data engineers and developers managing ETL processes in a production environment.
Apache Kafka widely used platform for building real-time data pipelines. provides a hands-on guide to Kafka Connect, a key component for integrating Kafka with various data sources and sinks. It is essential reading for anyone working with streaming data integration and Kafka.
Focuses on the crucial skill of communicating insights from data effectively through compelling visualizations. is highly relevant for all levels, emphasizing the importance of clear and impactful data presentation. It is valuable additional reading that complements technical data analysis skills.
Focuses on implementing data engineering concepts, including ETL, using Python. It's a practical guide for those who want to build data pipelines with a popular programming language. It covers extracting, transforming, and loading data using Python libraries and tools. This book is valuable for hands-on learners and professionals using Python for ETL tasks.
Provides a broader perspective on data integration by placing it within the larger context of business intelligence. It covers the entire BI lifecycle, from data integration to analytics, and discusses the strategic aspects of leveraging data. It useful resource for understanding how data integration supports business objectives and can be valuable for both technical and business-oriented audiences.
Delivers battle-tested operational patterns for serving foundation models, addressing GPU cluster throughput, semantic caching, automated evaluation benchmarks, and continuous latency minimization.
For those focusing on cloud-based data integration using Microsoft Azure, this cookbook provides practical recipes for building ETL and ELT pipelines with Azure Data Factory. It covers various aspects of ADF, including connecting to data sources, transforming data, and orchestrating pipelines. valuable resource for practitioners working with Azure's data integration services.
Details an applied architectural methodology for running high-performance open-weights models on local workstations, tackling quantization fidelity, memory bandwidth limits, and prompt assembly trade-offs.
Bridges the gap between statistical theory and its practical application in data science using R and Python. It focuses on the statistical concepts most relevant to data scientists and provides code examples. It's a useful reference and learning resource for those applying statistics in their work.
Offers a rigorous theoretical foundation for reasoning under uncertainty, combining graph theory and probability distributions to model complex high-dimensional dependencies across autonomous intelligent systems.
Offers a new way of thinking about data science and ethics informed by intersectional feminist thought. It challenges existing power structures within data and explores how data can be used to work towards justice. It's highly relevant for contemporary discussions around fairness and bias in data analysis.
Provides a comprehensive guide to Big Data analytics with Java, covering topics such as data ingestion, data storage, and data processing. It good option for those who want to gain a practical understanding of how to use Java to analyze Big Data.
This practical guide focuses on how to transform data for business intelligence applications. It covers data integration, data combining, and data harmonization. It valuable resource for data analysts and business intelligence professionals who need to transform data for reporting and analysis.
Provides a comprehensive overview of machine learning for data warehousing. It covers all the essential aspects of machine learning for data warehousing, including data integration, data modeling, data storage, and data analysis. The author is an expert in the field and provides a wealth of practical advice.
Provides a guide to creating effective and aesthetically pleasing data visualizations. It delves into the principles behind good visualization design, helping readers make informed choices about how to represent their data. It valuable reference for anyone creating visualizations, from students to professionals.
Is an excellent starting point for anyone new to data analysis or statistics. It demystifies core statistical concepts without relying heavily on mathematical formulas, making it highly accessible for high school and undergraduate students. It provides a strong foundation in the intuition behind statistical analysis and helps readers understand how data can be used and misused. This is valuable background reading that builds prerequisite knowledge.
Covers the practical aspects of Big Data analytics, providing guidance on how to plan, implement, and integrate Big Data solutions in an enterprise environment. It includes discussions on NoSQL and graph databases, which are essential technologies for handling Big Data.
Provides a technical overview of Big Data principles and best practices. It covers topics such as data ingestion, data storage, and data processing. It good option for those who want to gain a deeper understanding of the technical aspects of Big Data.
Provides a theoretical foundation for cloud computing. It covers topics such as cloud architectures, cloud resource management, and cloud security.
Practical guide to cloud computing. It covers a wide range of topics, including cloud computing basics, cloud architectures, and cloud security.
Introduces core computational linguistics concepts and practical tokenization, tagging, and syntactic parsing workflows through clear Python examples, establishing strong fundamentals for language analysis.
Synthesizes the five foundational tribes of machine learning into a unified conceptual framework, illuminating how symbolists, connectionists, evolutionaries, Bayesians, and analogizers approach intelligence.
Likely covers the various aspects of data integration, including different approaches, challenges, and best practices. David Loshin is an author known for his work in data management, making thpotentially valuable resource for gaining a broad understanding of the field.

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