The best Data Engineering online courses & Tutorials to Learn Data Engineering for beginners to advanced level.

Data engineering courses is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In order for that work to ultimately have any value, there also have to be mechanisms for applying it to real-world operations in some way. Those are both engineering tasks: the application of science to practical, functioning systems.

Top Data Engineering Courses and Tutorials List

  1. Data Engineering on Google Cloud platform
  2. Data Engineering with GCP Professional Certificate
  3. Preparing for the Google Cloud Professional Data Engineer Exam
  4. Google: Professional Cloud Data Engineer
  5. Big Data for Data Engineers
  6. Big Data Engineering courses
  7. Become a Data Engineer: Mastering the Concepts
  8. Data Engineer Online Hands-On Courses – Dataquest
  9. Become a Data Engineer

1.Data Engineering on Google Cloud platform

End to end batch processing,data orchestration and real time streaming analytics on GCP.

⭐ : 4.0 (9 ratings)

With this data engineering courses, you will:

  • Pyspark for ETL/Batch Processing on GCP using Bigquery as data warehousing component
  • Automate and orchestrate SparkSql batch jobs using Apache Airflow and Google Workflows
  • Sqoop for Data ingestion from CloudSql and using Airflow to automate the batch ETL
  • Difference between Event-time data transformations and process-time transformations
  • Pyspark Structured Streaming - Real Time Data streaming and transformations
  • Save real time streaming raw data as external hive tables on Dataproc and perform ad-hoc queries using HiveSql
  • Run Hive-SparkSql jobs on Dataproc and automate micro-batching and transformations using Airflow
  • Pyspark Structured Streaming - Handling Late Data using watermarking and Event-time data processing
  • Using different file formats - AVRO and Parquet . Different scenarios in which to use the file formats

This data engineering course provides the most practical solutions to real world use cases in terms of data engineering on Cloud . This course is designed keeping in mind end to end lifecycle of a typical Big data ETL project both batch processing and real time streaming and analytics . Considering the most important components of any batch processing or streaming jobs , this course covers: Writing ETL jobs using Pyspark from scratch, Storage components on GCP (GCS & Dataproc HDFS), Loading Data into Data-warehousing tool on GCP (BigQuery), Handling/Writing Data Orchestration and dependencies using Apache Airflow(Google Composer) in Python from scratch, Batch Data ingestion using Sqoop , CloudSql and Apache Airflow, Real Time data streaming and analytics using the latest API , Spark Structured Streaming with Python, Micro batching using PySpark streaming & Hive on Dataproc.

The coding tutorials and the problem statements in this course are extremely comprehensive and will surely give one enough confidence to take up new challenges in the Big Data / Hadoop Ecosystem on cloud and start approaching problem statements & job interviews without inhibition . Most importantly , this course makes use of Linux Ubuntu 18.02 as a local operating system. Though most of the codes are run and triggered on Cloud , this course expects one to be experienced enough to be able to set up Google SDKs , python and a GCP Account by themselves on their local machines because the local operating system does not matter in order to succeed in this course.

You can take Data Engineering courses on Google Cloud platform Certificate Course on Udemy.

2.Data Engineering with GCP Professional Certificate

Learn Data Engineering Courses with GCP Professional Certificate from Google Cloud. This program provides the skills you need to advance your career in data engineering and provides a pathway to earn the industry-recognized Google Cloud Professional Data.

With this data engineering course, you will:

  • Learn the skills needed to be successful in a data engineer role
  • Prepare for the Professional Data Engineer certification
  • Learn about the infrastructure and platform services provided by Google Cloud Platform

This program provides the skills you need to advance your career in data engineering and provides a pathway to earn the industry-recognized Google Cloud Professional Data Engineer certification. Through a combination of presentations, demos, and labs, you'll enable data-driven decision making by collecting, transforming, and publishing data; and you'll gain real world experience through a number of hands-on Qwiklabs projects that you can share with potential employers.

You'll also have the opportunity to practice key job skills, including designing, building, and running data processing systems; and operationalizing machine-learning models. For learners looking to get certified, this program will also provide sample questions similar to those on the exam, including solutions and practice exam quizzes that simulate the exam-taking experience. Upon successful completion of this program, you will earn a certificate of completion to share with your professional network and potential employers. If you'd like to earn your Google Cloud certification, you will need to register for and pass the certification exam. Please note that this program helps equip you with the skills you need to take the certification exam, but the certification and certification fee is not included in the cost of this training program.

You can take Data Engineering with GCP Professional Certificate Certificate Course on Coursera.

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3.Preparing for the Google Cloud Professional Data Engineering Exam

Learn Preparing for the Google Cloud Professional Data Engineering Exam from Google Cloud. From the course: "The best way to prepare for the exam is to be competent in the skills required of the job."  This course uses a top-down approach to.

⭐ : 4.7 (1,85 ratings)

With this data engineering course, you will:

  • Review each section of the exam using highest-level concepts to identify what is already known and surface gap areas for study.
  • Practice case study analysis and solution proposal methods and thinking skills.
  • Learn information, tips, and general advice about how to prepare for the exam.
  • Integrate prior technical skills into practical skills for the job role. Help you become a Data Engineer.

This data engineering course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. You can use this course to help create your own custom preparation plan. It helps you distinguish what you know from what you don't know. And it helps you develop and practice skills required of practitioners who perform this job.

This data engineering courses follows the organization of the Exam Guide outline, presenting highest-level concepts, "touchstones", for you to determine whether you feel confident about your knowledge of that area and its dependent concepts, or if you want more study. You also will learn about and have the opportunity to practice key job skills, including cognitive skills such as case analysis, identifying technical watchpoints, and developing proposed solutions.

These are job skills that are also exam skills. You will also test your basic abilities with Activity Tracking Challenge Labs. And you will have many sample questions similar to those on the exam, including solutions. The end of the course contains an ungraded practice exam quiz, followed by a graded practice exam quiz that simulates the exam-taking experience.

You can take Preparing for the Google Cloud Professional Data Engineer Exam Certificate Course on Coursera.

4.Google: Professional Cloud Data Engineering Courses

What you will learn Dataproc Dataflow and Apache Bean GCP Pub/Sub BigQuery GCP Cloud SQL GCP Cloud Spanner Cloud Datastore Frestore BigTable Datalab ML Engine Machine Learning APIs Data Architecture on GCP.

With this data engineering courses, you will learn:

  • Dataproc
  • Dataflow and Apache Bean
  • GCP Pub/Sub
  • BigQuery
  • GCP Cloud SQL
  • GCP Cloud Spanner
  • Cloud Datastore
  • Frestore
  • BigTable
  • Datalab
  • ML Engine
  • Machine Learning APIs
  • Data Architecture on GCP

In this beginning section of the path you’ll learn how to use Dataproc, Google Composer, Dataflow, and Apache Stream Processing. You’ll be architecting solutions, and beginning to build out the pipeline for your data projects. After this section you’ll be ready for more intermediate topics like incorporating machine learning models.

In the second part of the course you’ll begin incorporating more intermediate products and functions in Google Cloud such as Big Query and Big Query ML, Google SQL Instances, Datastores, and Bigtable. This is the data heavy portion of the process and you’ll also spend time developing repositories. After this section you’ll be ready for the advanced section that will dive deeper into designing machine learning and working with APIs.

In that part you’ll cover machine learning heavy topics such as working with AutoML, ML Engine, and designing data architectures that are specific to Google Cloud. After this section you will have learned the main critical functions and services you need on Google Cloud to work on the job as a Data Engineer.

You can take Google: Professional Cloud Data Engineer Certificate Course on Pluralsight.

5.Big Data for Data Engineering courses

Learn Big Data for Data Engineers from Yandex. This specialization is made for people working with data (either small or big). If you are a Data Analyst, Data Scientist, Data Engineer or Data Architect (or you want to become one) — don’t miss the.

This specialization is made for people working with data (either small or big). If you are a Data Analyst, Data Scientist, Data Engineer or Data Architect (or you want to become one) — don’t miss the opportunity to expand your knowledge and skills in the field of data engineering and data analysis on the large scale.

In four concise of data  engineering courses you will learn the basics of Hadoop, MapReduce, Spark, methods of offline data processing for warehousing, real-time data processing and large-scale machine learning. And Capstone project for you to build and deploy your own Big Data Service (make your portfolio even more competitive). Over the course of the specialization, you will complete progressively harder programming assignments (mostly in Python).

This data engineering course will master your skills in designing solutions for common Big Data tasks: creating batch and real-time data processing pipelines, doing machine learning at scale, deploying machine learning models into a production environment.

You can take Big Data for Data Engineers Certificate Course on Coursera.

6.Big Data engineering Courses

7.Become a Data Engineering courses: Mastering the Concepts

Build extensive data engineering and DevOps skills as you learn essential concepts. With this learning path, master the tools of the trade and how to apply them in real-world data project environments and platforms.

With this data engineering courses, you will:

  • Build a foundation in data engineering and data science DevOps.
  • Explore techniques deployed in common tools and platforms.
  • Develop deeper skills in data science application programming.

This data engineering course include Data science expert Ben Sullins explains the basics of big data and demonstrates how to perform core data engineering tasks including staging, profiling, cleansing, and migrating data, explore the possibilities NoSQL databases offer developers for unparalleled flexibility and performance, learn the basics of HBase.

The Hadoop data for big data analytics, get an understanding of the HBase architecture and basic read/write commands, discover how to make Apache, spark work with other big data technologies to build data pipelines for data engineering and DevOps, learn about use cases and best practices for architecting batch mode applications using big data technologies such as Hive and Apache Spark, learn about use cases and best practices for architecting real-time applications using big data technologies, such as Hazelcast and Apache Spark, get the data you need for analysis and reporting by writing your own SQL code.

Learn how to write basic SQL queries, sort and filter data, and join results from different tables and data sets, explore the fundamentals of NoSQL, learn the differences between NoSQL and traditional relational databases, discover how to perform common data science tasks with NoSQL, and more, get Ben Sullins's 12 must-have SQL techniques for data science pros—engineers, DevOps, data miners, programmers, and other systems specialists, learn how to choose a NoSQL database solution that's right for your organization, including options that work with Microsoft SQL Server and on the cloud, you can take Become a Data Engineer: Mastering the Concepts Course on LinkedIn Learning.

You can take Become a Data Engineering courses: Mastering the Concepts Certificate Course on LinkedIn Learning.

8.Data Engineering Online Hands-On Courses – Dataquest

Learn how to build data pipelines to work with large data sets.

With this data engineering courses, you will:

  • How to work with production data
  • Key computer science concepts like data structures, algorithms, and recursion
  • How to handle larger data sets

This path teach you how to utilize Python along with pandas to work with large data sets and loading them into a Postgres data. In this path, you'll learn how to work with big data, building data pipelines, and more.

You can take Data Engineer Online Hands-On Courses – Dataquest Certificate Course on Dataquest.

9.Become a Data Engineering courses

Break into a career in Data Engineering with this step-by-step path. Learn to design, build, and maintain big data pipelines for data-driven applications.

With this data engineering courses, you will:

  • Design, build, and maintain big data pipelines for data-driven applications
  • Learn Hadoop, Spark, software engineering, and cloud computing
  • Drive business value through data warehouse design, SQL database systems, and integrating ETL and business intelligence tools
  • Build scalable data pipelines by enhancing your data toolkit with functional programming in Scala and parallel computing
  • Explore how to design, build, and deploy machine learning systems on Google Cloud Platform, using a production environment to develop application

This data engineering courses aims to teach everyone the basics of programming computers using Python. We cover the basics of how one constructs a program from a series of simple instructions in Python. This database engineering course has no pre-requisites and avoids all but the simplest mathematics. Anyone with moderate computer experience should be able to master the materials in this course. This course will cover Chapters 1-5 of the textbook “Python for Everybody”. Once a student completes this course, they will be ready to take more advanced programming courses. This database engineering courses covers Python 3.

You can take Become a Data Engineer Certificate Course on Coursera.

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