Databricks on AWS Training Logo

Databricks on AWS Training

Live Online & Classroom Enterprise Training

Learn how to build, manage, and optimize data analytics and AI workloads using Databricks on Amazon Web Services (AWS).

Looking for a private batch ?

REQUEST A CALLBACK

Need help finding the right training?

Your Message

  • Enterprise Reporting

  • Lifetime Access

  • CloudLabs

  • 24x7 Support

  • Real-time code analysis and feedback

What is Databricks on AWS Course about?

This course provides a practical introduction to using Databricks on AWS for data engineering, data analytics, and machine learning. Participants will explore how to set up Databricks workspaces, integrate with AWS services, manage data using Delta Lake, and run scalable data pipelines. The training emphasizes hands-on learning to help learners confidently design, deploy, and optimize cloud-based big data solutions.

What are the objectives of Databricks on AWS Course ?

  • Understand Databricks architecture on AWS
  • Create and manage Databricks workspaces
  • Use Delta Lake for reliable data management
  • Build scalable data pipelines with Apache Spark
  • Monitor and optimize performance and costs

Who is Databricks on AWS Course for?

  • Data engineers and ETL developers
  • Data analysts and BI professionals
  • Cloud engineers working with AWS
  • Machine learning practitioners
  • IT professionals transitioning to big data

What are the prerequisites for Databricks on AWS Course?

Prerequisites:

  • Basic knowledge of Python or SQL
  • Understanding of data concepts (tables, schemas, ETL)
  • Familiarity with cloud computing basics
  • Introductory knowledge of AWS services
  • Willingness to learn Apache Spark concepts


Learning Path:

  • Introduction to Databricks and AWS integration
  • Working with notebooks and clusters
  • Data engineering with Delta Lake
  • Building and scheduling data pipelines
  • Performance tuning and best practices


Related Courses:

  • Apache Spark Fundamentals
  • AWS Data Engineering
  • Data Engineering with Python
  • Delta Lake Essentials

Available Training Modes

Live Online Training

3 Days

Course Outline Expand All

Expand All

  • A look at how AWS DataBricks works with the rest of AWS.
  • Some of the most crucial pieces include Apache Spark, Delta Lake, and Databricks Notebooks.
  • Learning about DataBricks clusters, workspaces, and the analytics platform that can work with any kind of data.
  • Creating an account and a place to use AWS DataBricks.
  • Making IAM roles and VPCs and linking them to AWS S3 for storage.
  • Understanding how the systems that keep data safe and control access work.
  • Getting data from places like S3, Redshift, and others into AWS DataBricks.
  • Using Databricks with Apache Spark to build ETL workflows.
  • Changing data with PySpark and Spark SQL.
  • A look at Delta Lake and what it can do.
  • Making Delta Lake tables, keeping track of their versions, and altering their schemas.
  • ACID transactions and making sure that data stays the same in AWS DataBricks.
  • RDDs and Spark DataFrames let you do more advanced things with data.
  • Getting Spark to work faster.
  • You may alter, mix, and filter data with Spark SQL.
  • A look at how structured streaming works in AWS DataBricks.
  • Making ETL pipelines and bringing streaming data into the system in real time.
  • Dealing with late data, windowing, and actions that depend on the condition of the data.
  • Using Databricks SQL to look at data and ask it things.
  • You can create charts and dashboards in the Databricks workspace that you may use.
  • Connecting to third-party tools like Tableau and Power BI that let you look at data.
  • Using Databricks to train and use machine learning models.
  • MLflow lets you construct machine learning pipelines.
  • If you want to use more complex models, link Databricks to AWS SageMaker.
  • Putting data governance into action with Delta Sharing and Unity Catalog.
  • Role-based access control (RBAC) and other approaches to keep data safe.
  • Keeping track of metadata and data lineage for compliance and audits.
  • Making Apache Spark jobs go faster.
  • Cluster management includes autoscaling, making more than one cluster, and lowering costs.
  • Watching things and fixing problems with Databricks Metrics and Spark UI.

Who is the instructor for this training?

The trainer for this Databricks on AWS Training has extensive experience in this domain, including years of experience training & mentoring professionals.

Reviews