Best Data Engineering Courses, Tutorials, and Certifications Online – Updated [April 2024]

Here at SkillComplete, our team of experienced web developers and technical enthusiasts has conducted thorough research of all the available courses on leading online learning platforms such as Udemy, Coursera, Edx, Linkedin learning, etc and compiled the list of the Best Data Engineering courses, tutorials, training, certifications and classes for you to learn in 2022. We have also listed free resources to help you in your learning journey. The listed programs are suitable for beginners, intermediate, and advanced developers. First, let us look at a brief introduction to Data Engineering.

The amount of data being generated daily through our digital devices is enormous. This situation presents challenges for its storage, processing, and analysis. In the last two years, the total industry data volume has gone up from approximately one petabyte (PB) to 2.02 petabytes, which makes it a 42.2% average annual growth over these two years. To handle the accuracy, consistency, and security of such data volume, businesses have to hire people with specific skill sets such as data governance and strategy, including data engineers, data scientists, and machine learning engineers.

The role of a data engineer is to store, extract, transform, load, aggregate, and validate data. The responsibilities include:

• Building data pipelines and efficiently storing data for tools that need to query the data. 
• Analyzing the data, ensuring it adheres to data governance rules and regulations.
• Understanding the pros and cons of data storage and query options.

Now that we have a brief idea about Data engineering. Let’s dive into the est Data engineering courses, that would help you in your learning journey to acquire a role of Data engineer.

Best online courses, classes, and tutorials to learn Data Engineering

Start building the Foundation for a Data Engineering Career. This course will help you gain a hands-on experience with Python, SQL, and Relational Databases and grasp the fundamentals of the Data Engineering ecosystem. This Specialization course consists of 5 self-paced online tutorials teaching skills required for data engineering, including the data engineering ecosystem and lifecycle, Python, SQL, and Relational Databases. This online course contains several hands-on labs and exercises to help you acquire practical experience and skills.

The projects offered in this program let you work with data in multiple formats. You will practice transforming and packing that data into a single source to analyze socio-economic data with SQL and working with advanced SQL techniques. You will learn to work with real-world databases and tools such as MySQL, PostgresSQL, IBM Db2, PhpMyAdmin, pgAdmin, IBM Cloud, Python, Jupyter notebooks, Watson Studio, etc.

Key points :

• Practical knowledge of Data Engineering Ecosystem and Lifecycle. Outlooks and recommendations from Data professionals on starting a career in the Data engineering domain.
• Python programming basics such as data structures, logic, working with files, invoking APIs, using libraries such as Pandas and Numpy, and doing ETL.
• Relational Database fundamentals such as Database Design, Creating Schemas, Tables, Constraints, and working with MySQL, PostgreSQL & IBM Db2.
• SQL query language, SELECT, INSERT, UPDATE, DELETE statements, database functions, stored procs, working with multiple tables, joins, & transactions.

Pre-requisites: No prior knowledge or experience in Data Engineering is needed.

Duration: Self-paced | Level: Beginner | Access: Lifetime | Certificate: Certificate of completion

The course is designed by 20+ years experienced professional, who has more than 10 years of Data Engineering and Big Data experience with several certifications. As part of this course, you will learn Data Engineering Essentials such as SQL, and Programming using Python and Spark.

Key points :

• Learn to set up the development environment for building Data Engineering Applications on GCP.
• Learn the database Essentials for Data Engineering using Postgres such as creating tables, and indexes, running SQL Queries, using pre-defined functions, etc.
• Work with Data Engineering programming essentials using Python such as basic programming constructs, collections, Pandas, Database Programming, etc.
• Learn Data Engineering using Spark Dataframe APIs (PySpark) and Spark Data Frame APIs such as select, filter, groupBy, orderBy, etc.
• Work on Data Engineering using Spark SQL (PySpark and Spark SQL). Learn to write efficient Spark SQL queries using SELECT, WHERE, GROUP BY, ORDER BY, ETC.
• Understand the use of Spark Metastore and integration of Dataframes and Spark SQL.
• Gain the skills to build Data Engineering Pipelines using Spark and Python as programming languages.
• Use different file formats such as Parquet, JSON, CSV, etc to build Data Engineering Pipelines.
• Learn to set up self-support single-node Hadoop and Spark Cluster to get enough practice on HDFS and YARN.
• Understand the complete Spark Application Development Life Cycle to build applications using Pyspark. Review the applications using Spark UI.

Pre-requisites: No prior knowledge or experience in Data Engineering is needed.

Duration: 61 hours | Level: All levels | Access: Lifetime | Certificate: Certificate of completion

This course helps you gain the skills needed to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Data Engineer certification. This professional certificate course contains hands-on labs using the Qwiklabs platform. These hands-on exercises will let you practically apply the skills you learn. The projects include Google Cloud products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.

Key points:

• Understand the data-to-AI lifecycle on Google Cloud and the big data and machine learning products.
• Learn to analyze big data at scale with BigQuery.
• Identify different possibilities to build machine learning solutions on Google Cloud.
• Learn to describe a machine learning workflow and the steps with Vertex AI.

Pre-requisites: No prior knowledge or experience in Data Engineering is needed.

Duration: Self-paced | Level: Intermediate | Access: Lifetime | Certificate: Certificate of completion

This course provides the most practical knowledge of real-world use cases in data engineering on the Cloud. This course helps you understand the working of a Big data ETL project’s batch processing and real-time streaming and analytics. The coding exercises and the problem statements in this course are real-world exercises that will enable you to take up new challenges in the Big Data / Hadoop Ecosystem on the Cloud. Also, you will start approaching problem statements & job interviews without inhibition.

Key points :

• Learn to use Pyspark for ETL/Batch Processing on GCP using Bigquery as a data warehousing component.
• Automate and orchestrate SparkSql batch jobs using Apache Airflow and Google Workflows.
• Learn to use Sqoop for Data ingestion from CloudSql and Airflow to automate the batch ETL.
• Understand the difference between Event-time data transformations and process-time transformations.
• Work with Pyspark Structured Streaming for Real-Time Data streaming and transformations.
• Learn to save real-time streaming raw data as external hive tables on Dataproc and execute ad-hoc queries using HiveSql.
• Execute Hive-SparkSql jobs on Dataproc and automate micro-batching and transformations using Airflow.
• Learn the concepts of Pyspark Structured Streaming such as Handling Late Data using watermarking and Event-time data processing.
• Use different file formats such as AVRO and Parquet and learn their use cases in different scenarios.

Pre-requisites: Basic Python Skills and experience with Linux commands are desirable.

Duration: 10 hours | Level: Intermediate | Access: Lifetime | Certificate: Certificate of completion

This course is one of the bestseller’s on the Udemy platform to learn Data Engineering online. The tutor will guide you on the fundamentals to build Data Engineering Pipelines using AWS Analytics Stack. It includes services such as Glue, Elastic Map Reduce (EMR), Lambda Functions, Athena, EMR, Kinesis, and many more.

Key points :

• Learn Data Engineering using AWS Analytics features.
• Manage Tables using Glue Catalog.
• Engineer Batch Data Pipelines using Glue Jobs.
• Stage Batch Data Pipelines using Glue Workflows.
• Run Queries using Athena – Serverless query engine service.
• Use AWS Elastic Map Reduce (EMR) Clusters for building Data Pipelines.
• Use AWS Elastic Map Reduce (EMR) Clusters for reports and dashboards.
• Perform Data Ingestion using Lambda Functions.
• Learn Scheduling using Events Bridge.
• Engineer Streaming Pipelines using Kinesis.
• Stream Web Server logs using Kinesis Firehose.
• Understand the concept of data processing using Athena.
• Run Athena queries or commands using CLI.
• Run Athena queries using Python boto3.
• Create Redshift Cluster, tables, and perform CRUD Operations.
• Copy data from s3 to Redshift Tables.
• Understand the Distribution Styles and create tables using Distkeys.
• Run queries on external RDBMS Tables using Redshift Federated Queries.
• Run queries on Glue or Athena Catalog tables using Redshift Spectrum.

Pre-requisites: Programming experience using Python and Spark are desirable.

Duration: 24.5 hours | Level: Beginner | Access: Lifetime | Certificate: Certificate of completion

This course is one of the highest-rated courses on the Udemy platform to learn Data-engineering. It is self-paced with reference material, code snippets, and video tutorials. It teaches the basics of Databricks, the most popular cloud platform-agnostic data engineering tech stack. They are the committers of the Apache Spark project. Databricks run time provide Spark leveraging the elasticity of the cloud. You need to sign up for your own Databricks environment to practice all the core features of Databricks.

Key points :

• Data Engineering leveraging Databricks features
• Databricks CLI to manage files, Data Engineering jobs, and clusters for Data Engineering Pipelines
• Deploying Data Engineering applications developed using PySpark on job clusters
• Deploying Data Engineering applications developed using PySpark using Notebooks on job clusters
• Perform CRUD Operations leveraging Delta Lake using Spark SQL for Data Engineering Applications or Pipelines
• Perform CRUD Operations leveraging Delta Lake using Pyspark for Data Engineering Applications or Pipelines
• Setting up a development environment to develop Data Engineering applications using Databricks
• Building Data Engineering Pipelines using Spark Structured Streaming on Databricks Clusters
• Incremental File Processing using Spark Structured Streaming leveraging Databricks Auto Loader cloud files
• Overview of AutoLoader cloud files File Discovery Modes – Directory Listing and File Notifications
• Differences between Auto Loader cloud files File Discovery Modes – Directory Listing and File Notifications
• Understand the difference between traditional Spark Structured Streaming and Databricks Auto Loader cloud files for incremental file processing.

Pre-requisites: Programming experience using Python and Spark are desirable.

Duration: 14 hours | Level: Beginner | Access: Lifetime | Certificate: Certificate of completion

This course teaches you the core concepts, processes, and tools you need to know to get a foundational knowledge of data engineering. You will understand the modern data ecosystem and the role of Data Engineers, Data Scientists, and Data Analysts in this ecosystem. You will gain an understanding of the Data Engineering Ecosystem that includes several different components such as disparate data types, formats, and sources of data. Work with Data Pipelines to gather data from multiple sources, transform it into analytics-ready data, and make it available to data consumers for analytics and decision-making. Build various Data repositories, such as relational and non-relational databases, data warehouses, data marts, data lakes, and big data that process and store this data. Data Integration Platforms combine disparate data into a unified view for the data consumers. You will learn about each of these components in this course. You will also learn about Big Data and the use of its processing tools. 

Key points :

• Learn to explain basic skills required for a data engineer.
• Put to use the various concepts in the data engineering lifecycle.
• Showcase hands-on knowledge with Python, Relational Databases, NoSQL Data Stores, Big Data Engines, Data Warehouses, and Data Pipelines.
• Learn to define data security, governance, and compliance.

Pre-requisites: Programming experience using Python and Spark are desirable.

Duration: 12 hours | Level: Beginner | Access: Lifetime | Certificate: Certificate of completion

The main objective of this course is to help you to learn Data Engineering techniques for building Metadata-Driven frameworks with Azure Data Engineering tools such as Data Factory, Azure SQL, and others.

Building Frameworks are now an industry norm, and it has become an important skill to know how to visualize, design, plan and implement data frameworks. You will learn Azure Data Engineering by building a Metadata-driven Ingestion Framework as an industry standard.

Key points :

• Learn about Azure Data Factory.
• Use Azure Blob Storage for storing large volumes of data.
• Learn to describe the Azure Gen 2 Data Lake Storage.
• Learn the use of the Azure Data Factory Pipelines.
• Familiarize yourself with Data Engineering Concepts.
• Grasp the Data Lake Concepts to put to your application.
• Understand the metadata Driven Frameworks Concepts.
• Work on the industry application of How to build Ingestion Frameworks.
• Work on Dynamic Azure Data Factory Pipelines.
• Establish the email Notifications with Logic Apps.
• Tracking of Pipelines and Batch Runs.
• Learn the version management with Azure DevOps.

Pre-requisites: No prior knowledge of Data engineering is required.

Duration: 6.5 hours | Level: Beginner | Access: Lifetime | Certificate: Certificate of completion

This Professional Certificate incorporates hands-on labs using our Qwiklabs platform. These hands-on components will let you apply the skills you learn in the video lectures. The projects include fundamentals such as Google BigQuery used and configured within Qwiklabs. You can expect to gain practical hands-on experience with the concepts explained throughout the modules. This professional certificate course includes hands-on labs using the Qwiklabs platform. These hands-on exercises will let you apply the skills you learn.

Key points :

• Identify the intent and significance of the fundamental Big Data and Machine Learning products in Google Cloud.
• Use BigQuery to carry out interactive data analysis.
• Utilize Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.
• Learn to choose the most appropriate different data processing products on Google Cloud.

Pre-requisites: You should have basic proficiency with a query language such as SQL; experience developing applications using programming languages.

Duration: Self-paced | Level: Intermediate | Access: Lifetime | Certificate: Certificate of completion

Conclusion

That’s all about the best Data Engineering courses online. Data Engineering is nothing but processing the data depending on our downstream requirements. This enormous data handling requires us to build different pipelines such as Batch Pipelines, Streaming Pipelines, etc as part of Data Engineering. All roles related to Data Processing are consolidated under Data Engineering. Conventionally, they are known as ETL Development, Data Warehouse Development, etc. This makes Data engineers the sought-after jobs in the industry. Thus, learning Data Engineering concepts is a must. Hope the above list of consolidated best courses helps you in your learning journey. 

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