Data is everywhere. In this digital era, with the growing number of connected devices and the expansion of their use, the world has been producing a large amount of data that is difficult to handle. This is where data engineers come into play. Data engineering forms the backbone of modern data-driven enterprises. It involves maintaining crucial systems and infrastructure for managing data throughout its lifecycle.
There are many career opportunities in the field of Data Engineering. To build a career in this field, you will require specific qualifications. This article will teach you about free data engineering courses for beginners and professionals. These courses will teach you data ingestions, data pipelines, SQL, ETL/ELT, analytical engineering, batch processing, data streaming, and automation
This 90-minute-long YouTube course will help you learn data engineering using Amazon Web Services. It is for both beginners and seasoned professionals.
You will learn about AWS tools such as Kinesis, DMS, and Glue supported by theory and practical examples. You will use these tools to learn streaming data analytics, data ingestions, transformation, query and visualization, and automation.
This course is offered by DataCamp. This is a no-code visual introduction to data engineering. There are no prerequisites, and anyone can take this course for free, even pure management professionals.
You will learn what data engineers do and get an overview of SQL databases, data warehousing, and data lakes, processing the data, parallel computing, scheduling the task, and cloud computing. The course is 2 hours long with interactive exercises and video tutorials.
This 9-week long course by DataTalks.Club is a complete course on data engineering. All material of the course is free, including project files, video tutorials, and tools. You will learn about data engineering and setting up an environment in the first week. Followed by data ingestions, data Lake, workflow orchestration, and creating data pipelines locally and on a cloud, data warehouse, BigQuery, and Airflow in the second and third week.
In the fourth week, you will dive deep into analytical engineering using dbt. In the next couple of weeks, you will learn about batch processing using Spark and data streaming using Kafka. The final three weeks are about working on an end-to-end project using the tools you have learned. The project will be reviewed by your peers.
You will also learn about popular data engineering tools such as Airflow, PostgreSQL, BigQuery, Terraform, Docker, dbt (data build tool), Spark, and Kafka.
In this course offered by Coursera lessons can be accessed on any device and anytime. You can communicate with your peers by participating in the forums and sharing ideas and doubts. This course offers work on real-life projects and adds them to your portfolio that can be showcased in job interviews. It improves the business value of your company by building data models, and database systems and using business intelligence tools. The duration of this course is Self-paced.
This nanodegree offered by Udacity has been designed with the sole purpose of helping you to learn about the techniques to design a data model, build warehouses, automate the processing, and handle various scales of information. This will teach you to use NoSQL, PostgreSQL, and Apache Cassandra to create databases and models.
This certification will help you explore how cloud-based warehouses are built and how they function, get acquainted with Apache Spark, and understand how to work with large datasets. The learning schedule is customized to fit your personal goals. Duration is 5 months, 5 hours per week.
Data engineering is all about cleaning, processing, and preparing data for analytical, data science, and machine learning tasks. Data engineers manage huge sets of data in a real-time environment. They are supposed to provide high-quality information that is usable by different business departments.