This report aims to provide an overview of the best online courses available for learning Databricks, a cloud-based data processing platform. The report is based on an analysis of various online courses and their key features, such as course content, instructor expertise, learning outcomes, and user reviews. Databricks is widely used in industries such as finance, healthcare, and technology to process and analyze large amounts of data quickly and efficiently. As such, there is a growing demand for professionals who can work with Databricks and leverage its capabilities. Online courses can provide a convenient and flexible way for individuals to learn Databricks, and this report aims to assist those seeking high-quality courses to find the best options available.
Here’s a look at the Best Databricks Courses and Certifications Online and what they have to offer for you!
10 Best Databricks Courses and Certifications Online
- 10 Best Databricks Courses and Certifications Online
- 1. Azure Databricks & Spark Core For Data Engineers(Python/SQL) by Ramesh Retnasamy (Udemy) (Our Best Pick)
- 2. Databricks Fundamentals & Apache Spark Core by Wadson Guimatsa (Udemy)
- 3. Databricks Basics Guide 2022 by Learn Tech Plus (Udemy)
- 4. Databricks Essentials for Spark Developers (Azure and AWS) by Durga Viswanatha Raju Gadiraju, Asasri Manthena (Udemy)
- 5. Mastering Databricks & Apache spark -Build ETL data pipeline by Priyank Singh (Udemy)
- 6. Azure Databricks administration – ETL Workflow by Shantanu Das (Udemy)
- 7. Apache Spark with Databricks by Big Data Trunk (Udemy)
- 8. Azure Databricks Masterclass: Beginners Guide to perform ETL by Amit Navgire (Udemy)
- 9. Azure Cloud Azure Databricks Apache Spark Machine learning by Bigdata Engineer (Udemy)
- 10. Databricks Certified Data Engineer Associate Practice Exams by Akhil Vangala (Udemy)
1. Azure Databricks & Spark Core For Data Engineers(Python/SQL) by Ramesh Retnasamy (Udemy) (Our Best Pick)
The Azure Databricks & Spark Core For Data Engineers (Python/SQL) course aims to equip data engineers with the necessary skills to implement data engineering solutions using Azure Databricks, Delta Lake, Azure Data Factory, and PowerBI. The course is designed to provide a hands-on, real-world project experience, focusing primarily on Azure Databricks and Spark core. Other technologies such as Azure Data Lake Storage Gen2 and Azure Data Factory are also covered, but the course does not cover other aspects of Spark, such as Spark streaming and Spark ML. The course follows a logical progression of a real-world project implementation, with technical concepts being explained and built simultaneously.
The course is not specifically designed to teach the skills required for passing the Azure Data Engineer Associate Certification Exam DP203, but it can greatly help learners acquire most of the necessary skills. The instructor has designed the course to be fast-paced and to the point, using simple English and no jargons. The course starts from basics, and learners are expected to become proficient in the technologies used by the end of the course.
The course teaches learners various skills such as building a solution architecture for a data engineering solution, creating and using Azure Databricks service, working with Databricks notebooks, creating and monitoring Databricks clusters, mounting Azure Storage in Databricks, using Delta Lake to implement a solution using Lakehouse architecture, creating dashboards to visualise outputs, connecting to Azure Databricks tables from PowerBI, and more. Learners are also taught Spark architecture, Data Sources API, and Dataframe API, PySpark ingestion of CSV, simple and complex JSON files into the data lake as parquet files/tables, transformations such as Filter, Join, Simple Aggregations, GroupBy, Window functions, and more.
This course titled “Databricks Fundamentals & Apache Spark Core” aims to provide learners with the necessary skills to process big-data using Databricks & Apache Spark 2.4 and 3.0.0. The course is instructed by Wadson Guimatsa and is designed to teach Spark Applications using Scala and SQL.
Databricks, a company founded by the creator of Apache Spark, offers a managed and optimized version of the framework that runs in the cloud. The course focuses on using the DataFrame API & SQL to perform tasks such as writing and running Apache Spark code using Databricks, reading and writing data from the Databricks File System (DBFS), and explaining how Apache Spark runs on a cluster with multiple nodes.
Additionally, the course covers using the DataFrame API and SQL to manipulate data by selecting, renaming, and manipulating columns, filtering, dropping, and aggregating rows, joining DataFrames, creating UDFs, and using them with DataFrame API or Spark SQL. The course also explains the elements of Apache Spark execution hierarchy, including jobs, stages, and tasks.
The course content is divided into several sections, including setup, introduction to Databricks and Apache Spark, the DataFrame API basics, transforming data with the DataFrame API, Spark SQL & SQL fundamentals, working with different types of data, and data sources.
The Databricks Basics Guide 2022 course is designed to teach individuals about Databricks and its fundamentals. Led by instructor Josh Werner, the course covers everything from scratch and is great for students who are starting and want to learn about Databricks. The course is packed with over 1 hour of hands-on tutorials and requires no pre-requisites.
The course is divided into 5 sections, beginning with an introduction that provides an overview of Databricks, followed by a section on language guide – Python, which covers the Python APIs, visualizations, interoperability, notebooks, and libraries. The next section is language guide – R, which provides an overview of R APIs, visualizations, tools, and resources. The last section is the conclusion, which summarizes the course content covered in the previous sections.
Each section is further divided into subsections, with a total of 30 subsections in the course. The subsections cover topics such as signing up for a free trial, user and administrator roles, training, setup and deployment of a Databricks account, Databricks concepts, Apache Spark, and jobs.
Overall, the Databricks Basics Guide 2022 course offers a comprehensive introduction to Databricks and its fundamentals, making it an in-demand skill for 2022. The course requires an open mind and readiness to learn, and individuals can preview the course description and videos before enrolling.
4. Databricks Essentials for Spark Developers (Azure and AWS) by Durga Viswanatha Raju Gadiraju, Asasri Manthena (Udemy)
The Databricks Essentials for Spark Developers course is designed for experienced Spark Developers who want to learn about the Databricks platform. In this course, participants will explore the features of Databricks, a cloud option that separates storage from compute to significantly reduce infrastructure costs for Big Data Clusters.
The course covers the essentials of Databricks, including different editions such as Community, Databricks (AWS), and Azure Databricks. Participants will learn how to sign up for the community edition, upload data to DBFS, and develop using Databricks Notebook with Scala, Python, and Spark SQL. Additionally, the course delves into the development life cycle using Scala with IntelliJ as IDE and configuring jobs using Jar files.
The course content is divided into several sections, including Getting Started with Databricks, Databricks Notebook using Scala with Spark, Databricks Notebook using Python (pyspark), Databricks Notebook using Spark SQL, Databricks Jobs and Clusters, and Databricks Development and Deployment Life Cycle using Scala. Overall, the Databricks Essentials for Spark Developers course provides a comprehensive introduction to the Databricks platform and its essential features.
The Mastering Databricks & Apache Spark – Build ETL Data Pipeline course, instructed by Priyank Singh, provides a comprehensive understanding of databricks and big data processing. The course covers various operations using Scala, Python, and Spark SQL, allowing students to build solutions in different languages. The course focuses on building end-to-end solutions in Azure Databricks, creating value and a mindset to build batch processes based on the client’s needs.
The key learning points of the course include building clusters, processing data, loading data to Azure SQL and Delta tables, preparing dashboards, and deploying infrastructure on Azure cloud. All activities are performed in Azure Databricks, providing students with a 360-degree exposure to cloud platforms and resources.
The course covers the fundamentals of Databricks, Delta tables, versions, and vacuum on Delta tables, Apache Spark SQL, filtering dataframes, renaming, dropping, selecting, casting, aggregation operations such as SUM, AVERAGE, MAX, MIN, rank, row number, dense rank, building dashboards, and analytics.
This course is suitable for data engineers, BI architects, data analysts, ETL developers, and BI managers. The course is divided into sections that cover getting started with Databricks, extraction of data, transformation of data, processing XML, JSON, Delta tables, loading data, and building ETL data pipelines with dashboards.
The course titled “Azure Databricks Administration – ETL Workflow” is aimed at individuals who have no prior knowledge of Databricks and Azure and want to start a career in data administration. The course is designed to prepare individuals for the Azure Databricks Certified Associate Platform Administrator certification by providing them with practice questions and quizzes at the end of each session.
The course covers a wide range of topics that include Databricks components such as Notebook, Clusters, Pool, Secrets, Databricks CLI, and Cluster Policy. The course also focuses on automating administration activities using Terraform, mounting Azure Blob Storage with Databricks, and loading CSV data in Azure Blob Storage. Additionally, students will learn how to transform data using Scala and SQL queries before loading it into Azure Blob Storage.
The course covers topics such as Databricks tables and filesystem, configuring Azure Databricks logging using Log4j and Spark listener library via Log Analytics Workspace, configuring CI CD using Azure DevOps, Git provider integration, and configuring notebook deployment via Databricks Jobs.
Overall, the course provides a 30,000 ft. overview of the agenda, what students will learn, and how they can apply their learning in real-world scenarios. It is designed to prepare students for a career in data administration by providing them with the knowledge and skills needed to work with spark-based Azure Databricks in a fully managed and scalable environment with the global availability of Azure.
The Apache Spark with Databricks course teaches the implementation of Big Data’s Apache Spark on Databricks using Microsoft’s cloud service, Azure. The course focuses on the basics of creating Spark jobs, loading data, and working with data, while also introducing machine learning algorithms and streaming data. Databricks enables users to start writing Spark queries immediately, allowing them to focus on their data problems. Azure Databricks is a collaborative Apache Spark-based analytics service that accelerates big data analytics and AI solutions. It is productive, scalable, trusted and flexible, allowing users to build machine learning and AI solutions with their preferred language and deep learning frameworks.
The course includes some of the important Spark interview questions, which can be helpful in cracking interviews. The course is divided into several sections: Overview, Apache Spark Introduction, Databricks with Microsoft Azure, Understanding Cluster and Notebooks in Databricks, Working with Spark in Databricks, Spark Interview, and Bonus Section.
The course is designed to offer an introduction to Spark and Databricks, while also providing practical knowledge on how to work with them. It is tailored to individuals who want to learn and apply the concepts of Spark and Databricks in real-world scenarios.
Overall, the Apache Spark with Databricks course is a useful tool for those who want to gain an understanding of Spark and Databricks and how to implement them using Azure. The course provides a practical approach to learning, with a focus on applying the concepts in real life.
The Azure Databricks Masterclass is a course designed to teach beginners how to perform ETL operations in Azure Databricks. Developed by the original founders of Apache Spark, Databricks was created to address complex data engineering and data science problems using distributed cluster based programming with the power of Spark framework under the hood.
The course focuses on creating, managing, and performing ETL operations using the Azure platform. It covers everything from the basics of Azure Databricks to advanced topics of performing ETL operations through practical hands-on lab sessions. The course is ideal for beginners with little or no knowledge of Databricks.
The course starts with an overview of cloud computing and Azure, and then progresses to Databricks related topics. All the topics of Azure Databricks are covered using practical hands-on lab sessions with easy-to-understand examples. The course is helpful for those preparing for Azure Data Engineer Certification (DP-200, DP-201, DP-203).
The course contains code written in Scala and SQL. For any questions or concerns, students can drop a message or question in the communication section of Udemy.
The course includes sections on the fundamentals of cloud computing, an introduction to Azure Databricks, building blocks of Azure Databricks, and performing ETL operations in Databricks.
This course titled “Azure Cloud Azure Databricks Apache Spark Machine learning” is designed to provide a strong foundation for Microsoft Azure Cloud and Databricks. The course is suitable for individuals with no prior Azure experience.
Azure Databricks is a collaboration between Microsoft and Databricks that offers a collaborative Apache Spark-based analytics service. It is highly reliable and productive, making it an ideal choice for data science at scale.
The course covers topics such as Spark SQL, Machine Learning, Graph Computing, and Structured Streaming Computing in Azure Databricks. The course contains both theory lectures and hands-on demos to provide a comprehensive learning experience for the participants.
Azure Databricks is a highly productive, scalable, trusted, and flexible tool that can help you build machine learning and AI solutions with your choice of language and deep learning frameworks. The course includes sections on Introduction, Databricks Quickstart, Apache Spark, Databricks Developer Tools, Databricks Notebook, Databricks Delta Lake, Databricks REST API, Databricks Machine Learning, Structured Streaming, Databricks Graph Analysis, and Bonus: An Open Source Alternative Solution for Databricks.
Overall, this course is intended for individuals who want to gain practical knowledge and experience in Azure Databricks, Spark SQL, Hadoop, Kafka, Data Lake, Transfer Learning, Zeppelin Notebook, Graph, Hortonworks HDP, and Cloudbreak. The course can help build a strong foundation in preparation for Microsoft Azure Cloud and Databricks.
The Databricks Certified Data Engineer Associate Practice Exams course is designed to prepare individuals for the corresponding certification exam. The course consists of five practice exams and detailed explanations to help learners understand all the exam topics and concepts of the Databricks platform. The course instructor, Akhil Vangala, aims to help learners pass the exam and learn the Databricks platform simultaneously. It is essential to complete all five practice exams as each exam focuses on specific features of Databricks.
The Databricks Certified Data Engineer Associate certification exam assesses an individual’s ability to perform introductory data engineering tasks using the Databricks Lakehouse Platform. The exam covers topics such as the platform’s architecture, capabilities, and workspace, as well as performing ETL tasks using Apache Spark SQL and Python. The exam also assesses the ability to put basic ETL pipelines and Databricks SQL queries and dashboards into production while maintaining entity permissions. Passing this certification exam indicates that an individual can complete basic data engineering tasks using Databricks and its associated tools.
The certification exam consists of 45 multiple-choice questions, with a 90-minute duration. The questions are distributed by high-level topics, with the Databricks Lakehouse Platform, ELT with Spark SQL and Python, and Incremental Data Processing being the most heavily weighted topics, each accounting for 24%, 29%, and 22% of the questions, respectively. Production Pipelines and Data Governance account for 16% and 9% of the questions, respectively.
The minimally qualified candidate should have a comprehensive understanding of the Databricks Lakehouse Platform and its tools, build ETL pipelines using Apache Spark SQL and Python, incrementally process data, build production pipelines for data engineering applications and Databricks SQL queries and dashboards, and understand and follow best security practices. The course content consists of practice tests covering these topics, with each exam focusing on specific features of Databricks.