When it comes to learning about ETL (Extract, Transform, and Load) processes, there are a plethora of online courses available. These courses aim to provide learners with the knowledge and skills necessary to efficiently transfer data from one system to another. The availability of ETL courses online has made it possible for individuals to learn and improve their skills from the comfort of their own home or office. In this article, we will explore the best ETL courses available online, highlighting their features, content, and overall value.
Here’s a look at the Best Etl Courses and Certifications Online and what they have to offer for you!
10 Best Etl Courses and Certifications Online
- 10 Best Etl Courses and Certifications Online
- 1. Data Manipulation in Python: A Pandas Crash Course by Samuel Hinton, Ligency I Team, Ligency Team (Udemy) (Our Best Pick)
- 2. ETL Testing: From Beginner to Expert by Sid Inf (Udemy)
- 3. Learn ETL Testing With Informatica PowerCenter Today by The Startup Central Co., Tuhina Mehta (Udemy)
- 4. Apache NiFi Complete Master Course – HDP – Automation ETL by MUTHUKUMAR Subramanian (Udemy)
- 5. SQL/ETL Developer – T-SQL/Stored Procedures/ETL/SSIS by Bluelime Learning Solutions (Udemy)
- 6. ETL Framework for Data Warehouse Environments by Sid Inf (Udemy)
- 7. Writing production-ready ETL pipelines in Python / Pandas by Jan Schwarzlose (Udemy)
- 8. Learn to master ETL data integration with Pentaho kettle PDI by Itamar Steinberg (inflow systems) (Udemy)
- 9. Data integration (ETL) with Talend Open Studio by Andrejs Zaharovs (Udemy)
- 10. Talend : ETL Data Integration Guide with Talend Open Studio by Elementary Learners (Udemy)
1. Data Manipulation in Python: A Pandas Crash Course by Samuel Hinton, Ligency I Team, Ligency Team (Udemy) (Our Best Pick)
This course, titled “Data Manipulation in Python: A Pandas Crash Course”, is designed to teach individuals how to use Python and Pandas for analyzing and manipulating data. With data manipulation accounting for up to 80% of a data scientist’s work, this course aims to teach participants advanced data munging techniques to turn raw data into a final product for analysis quickly and efficiently. The course instructor, Ph.D. Samuel Hinton, provides a comprehensive curriculum that covers basic and advanced Pandas data manipulation techniques, loading and creating Pandas DataFrames, displaying data with basic plots and visualizations, and more. Participants will also receive a cheatsheet and practical exercises to gain hands-on experience.
Pandas, the most popular Python library in data science, is used by data scientists at major companies like Google, Facebook, and JP Morgan. However, it has a steep learning curve and inadequate documentation when it comes to advanced functions, making it difficult for users to grasp complex techniques. This course, taught by an experienced instructor, aims to guide beginners and intermediate users through every aspect of Pandas with ease.
The course curriculum covers topics like basic and advanced DataFrame manipulations, multiIndexing, stacking, hierarchical indexing, pivoting, melting, and more. Participants will also learn how to perform grouping, aggregation, imputation, and time series manipulations. With this course, individuals will learn how to efficiently utilize Pandas to manipulate, transform, pivot, stack, merge, and aggregate data for visualization, statistical analysis, or machine learning.
Upon completing the course, individuals will feel confident in analyzing complex and heterogeneous datasets and producing useful results for the next stage of data analysis. The course provides a practical approach with real-life examples of data manipulation techniques, which enables participants to gain hands-on experience.
The ETL Testing course, offered by instructor Sid Inf, is designed to provide essential training for software testing professionals at all levels. The course covers topics related to Data Warehouse (DW), Business Intelligence (BI), and ETL set up, as well as Database Testing Vs Data Warehouse Testing, Data Warehouse Workflow and Case Study, and more. Students will also receive hands-on experience using the Informatica ETL tool, with step-by-step guidance on how to set up the environment on their personal computer.
The course is divided into several sections covering the basics of Data warehouse Concepts, Dimensional Modeling, Data Integration and ETL, Defect Management, and more. It also includes information on the Typical Roles In DWH Project, DW/BI/ETL Implementation Approach, and Different Categories Of Projects where DW/BI/ETL Testing is required. Students will learn the Different Tools/Technologies used in DWH/BI/ETL Testing, with a special focus on Informatica Power Center (ETL Tool).
The course includes an introduction to Transformations, covering their functions in brief, Data Types in Informatica, Different Types of Ports in Informatica, Functions in Informatica, Workflow Manager, Informatica PowerCenter Monitor, and Other important objects and activities in Informatica. Students will also have access to a Practical Scenario that takes them from Flat File Source to Flat File and Relational Targets.
In addition to the above, the course covers the Possible Reasons for Defects & Bugs, Issues/Defect Management Process, Issue Severity Table, Quality Center Defect Category Descriptions, Categories of ETL Testing, and Roles and Responsibilities of an ETL Testing Professional. There is also a Bonus Section and a Retired Lectures section.
Overall, the ETL Testing course is an essential resource for software testing professionals looking to build their skills in DW/BI/ETL Testing.
3. Learn ETL Testing With Informatica PowerCenter Today by The Startup Central Co., Tuhina Mehta (Udemy)
The course “Learn ETL Testing with Informatica PowerCenter Today” by The Startup Central Co. and Tuhina Mehta is designed to make learners familiar with the concepts of ETL testing. The course covers data warehousing concepts, SQL, normalization to structure data in a database, testing loaded data by ETL process after transformations, and creating a set of test cases for ETL mapping. Each section of the course has relevant exercises to practice new skills.
ETL testing stands for Extract-Transform-Load testing, which is done to ensure that the data loaded from the source system to a data warehouse is accurate after transformations. ETL testing professionals are in high demand in the market, and ETL testing jobs are generally well paid, including many benefits. Learning ETL testing can also improve data analytical skills.
Before opting for ETL testing, learners must have good knowledge of structured query language (SQL), data warehousing concepts, and playing with large data in databases. A short refresher to SQL will be given in the course.
The course comes with a 30-day money-back guarantee. Learners can press the “Take This Course” button to start learning within two minutes. The course includes sections on Introduction to Dataware Housing, SQL, ETL Overview, ETL Testing, ETL Test scenarios in detail, and Conclusion.
The Apache NiFi Complete Master Course – HDP – Automation ETL is a comprehensive course that covers all the basic and advanced concepts available in Apache NiFi, including Flowfile, Controllers, Processors, Connections, Process Group, Funnel, Data Provenance, Processor relationships, and Input and Output Ports. The course also covers Apache NiFi subprojects like Nifi Registry, and provides practical demonstrations of handling throughput and latency, prioritizing data, and error handling.
Additionally, the course covers processors and controllers used in production scenarios, such as HTTP, RDBMS, NoSQL S3, CSV, JSON, Hive, and SSL ConnectionPool, along with demonstrations of creating and using KeyStore and Trust Store for SSL communication, and using Maven and Eclipse EE for custom processor and deploying nar file to NiFi libraries.
The course is divided into several sections, including Introduction to Apache NiFi, First Baby Step – Flow file Demo, Processors and Connections, Integrating Apache NiFi with Distributed Messaging System – Apache Kafka, and Nifi Registry. Other sections cover Nifi Cluster, Nifi and Bigdata Ecosystem, HTTP Processors, Nifi and AWS, Nifi and NoSQL Database, Nifi and Apache Solr, Custom Processor and Custom Controller, Practical Use Cases, Reference Resources, and a Bonus Lecture.
Each concept is presented with a demo and real-time implementation, and all demonstrated flowfile templates are uploaded as part of the course. The course emphasizes the importance of handling data latency and throughput, and demonstrating how it can be controlled with relationship, yield, and back pressure.
Bluelime Learning Solutions offers a SQL/ETL Developer course that focuses on using T-SQL/Stored Procedures/ETL/SSIS to develop ETL solutions. The course is designed to address the challenge of gathering data from multiple sources in different formats and moving it to one or more data stores. ETL is a data pipeline that collects data from various sources, processes it according to business rules, and loads it into a destination data store.
SQL Server Integration Services (SSIS) is a powerful Business Intelligence Tool that is best suited to work with SQL Server Database. It is added to SQL Server Database when you install SQL Server Data Tools (SSDT) which adds the Business Intelligence Templates to Visual Studio used to create Integration projects. SSIS can be used for data integration, transformation, providing solutions to complex business problems, updating data warehouses, cleaning data, mining data, managing SQL Server objects and data, extracting data from a variety of sources, and loading data into one or several destinations.
The course covers a range of topics, including installing SQL Server Database, downloading and attaching a database to SQL Server, downloading and installing SQL Server Data Tools, creating a new Integration Services Project, configuring a Flat File Connection Manager and an OLEDB Connection Manager, adding a Data Flow Task to the Package, configuring the Flat File Source, configuring Lookup Transformations, and creating new Connection Managers. It also includes topics such as writing data to a SQL Server database, executing a package from SQL Server Data Tools, controlling data flow for Flat Files, testing Packages, and using SQL Functions and T-SQL Stored procedures to extract data from multiple tables.
The course is divided into several sections including Introduction, Setup Visual Studio, SQL Server Setup, T-SQL Functions, Extracting Data From Multiple Tables, T-SQL Stored Procedure, CRUD Stored Procedures, and Developing ETL with SSIS.
The course “ETL Framework for Data Warehouse Environments” is designed to provide a practical approach to implement an ETL (extract, transform and load) framework in typical Data Warehouse environments. The course covers the guidelines, standards, developer/architect checklist, and benefits of reusable code along with best practices and standards for implementing ETL solutions. The course can be incorporated to any ETL tool in the market, including Informatica 10x, Oracle 11g, IBM DataStage, Pentaho, Talend, and Ab-intio. The course includes multiple reusable code bundles from the marketplace, checklists, and the material required to get started on UNIX for basic commands and Shell Scripting.
The course content is divided into sections, which includes “Getting Started,” “Metadata Categories,” “ETL Framework- Process Flow,” “Data Sourcing,” “Data Sourcing – Classification,” “Script Requirements for Data Sourcing,” “File Validation,” “The Staging Layer,” “Business Validation Layer,” “DataWarehouse Layer,” “Exception Handling/Error Handling,” “Project Setup,” “Extending the Operational Metadata’s Data Model,” “Error Handling Data Model,” “Mapping examples,” “Audit, Balance and Control,” and “Configuration Management.”
The course is suitable for those who require a high-level approach to implement an ETL framework in any Data Warehouse environment. The practical approaches can be used to design and implement an ETL solution that is highly reusable with different data loading strategies, error/exception handling, audit balance and control handling, job scheduling, and restartability features. The course is also beneficial for those who have an existing ETL implementation and need to embed the ETL framework into the existing environment, jobs, and business requirements. The course may also require redesigning the whole mapping/mapplets and the workflows (ETL jobs) from scratch to improve design standards and reusability.
The “Writing production-ready ETL pipelines in Python / Pandas” course is led by instructor Jan Schwarzlose and aims to teach best practices in Python and Data Engineering for creating professional ETL pipelines. The course covers the use of Python 3.9, Jupyter Notebook, Git and Github, Visual Studio Code, Docker and Docker Hub, and Python packages such as Pandas, boto3, pyyaml, awscli, jupyter, pylint, moto, coverage, and memory-profiler. The course offers two approaches to coding in the Data Engineering field – functional and object oriented programming – as well as best practices in developing Python code, such as design principles, clean coding, virtual environments, project/folder setup, configuration, logging, exception handling, linting, dependency management, performance tuning with profiling, unit testing, integration testing, and dockerization.
The goal of the course is to use the Xetra dataset – derived from Deutsche Börse Group’s trading system and saved in an AWS S3 bucket – to create an ETL pipeline that extracts data from the source bucket, transforms it, and loads it into another AWS S3 target bucket. The pipeline is designed to be easily deployable in most production environments that can handle containerized applications, such as a GitHub Code repository, a DockerHub Image Repository, an execution platform like Kubernetes, and an Orchestration tool like the container-native Kubernetes workflow engine Argo Workflows or Apache Airflow.
The course primarily offers practical interactive lessons where students code and implement the pipeline, with theory lessons provided as needed. Python code for each lesson is included in the course material, along with the whole project on GitHub and a ready-to-use docker image with the application code on Docker Hub. PowerPoint slides and useful links are also provided for each theoretical lesson and topic.
8. Learn to master ETL data integration with Pentaho kettle PDI by Itamar Steinberg (inflow systems) (Udemy)
The “Learn to master ETL data integration with Pentaho kettle PDI” course, taught by Itamar Steinberg of Inflow Systems, is designed to teach individuals how to develop ETL with Pentaho PDI 8. The course covers a full project, providing hands-on experience, tips and tricks, and homework assignments.
Participants will become masters in transformation steps and jobs, learn how to set up the Pentaho kettle environment, and become familiar with the most commonly used steps of Pentaho kettle. They will also learn to solve issues and start making money as an ETL developer.
The target audience includes SQL developers, ETL developers, code developers (Python, PHP, etc.), automation developers, BI developers, software project managers, and anyone interested in understanding what ETL is. The course is designed for individuals who have some background with SQL syntax, queries, and database design.
It is recommended that individuals who have no experience with SQL take a course specific to that topic before enrolling in this course. The course is intended for serious, hands-on learners who are willing to practice and complete homework assignments.
The course is broken down into several sections, including Introduction, Installations, PDI Walkthrough, The Project, Dim time, Dim Customers, Dim film, Dim store, Fact rentals, Main job, Theory, and What’s next.
This course, entitled “Data Integration (ETL) with Talend Open Studio,” is taught by Andrejs Zaharovs. The course covers the basics of Talend Studio and progresses to advanced techniques. The course is designed to help simplify data interactions for those seeking an affordable alternative to expensive training or books.
The course is comprised of 8 hours of video content in 720p quality with full voice-over. Throughout the course, students will learn how to install and navigate Talend, import data, perform data transformations, cleansing, filtering, lookups, concatenations, and more. They will also explore advanced features of Talend and work with various databases. Finally, students will learn how to debug, log job information and monitor job statistics.
As a bonus, a new video will be uploaded each week for at least 13 weeks after the course is published. These additional videos will cover advanced topics, obscure Talend features, tips, and tricks. For example, students will learn how to inject other application functionality into Talend.
The course is divided into 7 main sections: Talend Basics, Data Mapping, Conversion, Extraction, Join, Helpful Features, Java, Databases, Debugging, Logging, Building, and Scheduling, and Extras. All examples used in the course will be available to students as a zip file attachment.
In conclusion, this course provides an efficient and comprehensive introduction to Talend Studio. Students will gain valuable skills in data integration and learn how to use Talend effectively without spending a fortune on training. If interested, students can sign up for this course to begin their journey into Talend.
Course Title: Talend : ETL Data Integration Guide with Talend Open Studio
This course is designed to provide a complete practical guide for using Talend Open Studio, an open source data integration platform. Talend offers a wide range of software and services for data integration, data management, enterprise application integration, data quality, cloud storage, and Big Data. The company was founded in 2005 as the first commercial open source vendor of data integration software. Talend Open Studio for Data Integration was launched in October 2006 and has since released a variety of products that are highly regarded in the market.
The course covers all topics related to Talend, from fundamentals to advanced concepts in data integration and Big Data, with plenty of examples. However, before taking this course, it is recommended that students have a basic understanding of Data Warehousing concepts and ETL (Extract, Transform, Load) fundamentals. If you are unfamiliar with these concepts, it is suggested that you first learn about them in order to gain a solid understanding of Talend.
The course is broken down into sections, starting with the introduction to Talend and its capabilities. From there, you will learn about Talend’s architecture, the ETL process, and Talend’s user interface. You will also receive hands-on training in Talend Open Studio, including creating and executing jobs, using Talend components, and working with Big Data technologies such as Hadoop and Spark.
Overall, this course is an excellent resource for anyone interested in learning about Talend Open Studio and data integration. Students will gain practical skills and knowledge to use Talend effectively for data integration and Big Data tasks.