Databricks Training Course
Building and implementing machine learning models, working together on data science and analytics projects, and creating and executing data pipelines are all made simple by the Databricks Lakehouse Platform.
Overview
The goal of the Databricks course is to give students the information and abilities they need to work with Databricks and Apache Spark. It’s helpful for anyone who want to become certified in Databricks and develop their big data processing, analytics, and machine learning skills. The course covers the fundamentals of big data, the several programming languages offered by Spark, and how to use the unified platform of Databricks, including its architecture and community edition.Learners will be able to integrate Databricks into data pipelines, configure workspaces and clusters, and apply Databricks on AWS and Azure cloud services. In addition, data visualization, query execution, data intake, and using Delta Lake for data reliability are covered in the course.
Who should go for Databricks Training course?
- Data Scientists
- Data Engineers
- Big Data Analysts
- Machine Learning Engineers
- Data Architects
- Cloud Solutions Architects
- IT Professionals with a focus on data analytics and processing
- Software Developers interested in Big Data and analytics
- DevOps Engineers involved in data pipeline integration
- Database Administrators looking to expand into Big Data platforms
- Technical Managers overseeing data or analytics teams
- Business Analysts who require a deeper understanding of Big Data tools and frameworks
- System Administrators aiming to manage and deploy Databricks environments
What you will Learn in Databricks training course?
In this Databricks course, participants will gain comprehensive knowledge of Apache Spark, Databricks, data analytics, machine learning, and cloud implementations, leading to mastery in data engineering and analysis.
Our Package
Overview of Apache Spark and Databricks
- What is Apache Spark?
- How do we define Big Data?
- Spark languages – Scala, Python, R, Java, SQL
- Databricks Community Edition
- Databricks Architecture
- Defining Data Analytics
- Defining Machine Learning
- Azure implementation
- AWS implementation
Databricks benefits
- Collaboration
- Scaling
- Integrating into Pipelines
Getting started with Databricks
- Creating a Databricks Workspace on Azure
- Creating and configuring your Cluster
- Creating and attaching your first Notebook
- Testing your Notebook
Uploading data
- Creating a Table
- Connecting to a Spark data source
- Previewing your Table
- Columns and Datatypes basics
Bringing your data into your Notebook
- Writing the initial SQL query to import
- View aggregates
- Perform Joins
Visualisations & DataFrames
- Datatypes
- DataFrames
- Images
- Structured Streaming DataFrames
- Plots
- Choosing Chart types
- Chart Toolbar
- Layout and styling considerations
- Machine Learning visualisations
Databricks Jobs
- Creating a Job
- View Jobs and Job details
- Running your first Job
- Scheduling Jobs
- Setting Parameters
- Viewing completed jobs
- Managing Dependencies
- Setting up Alerts
Delta Lake and Delta Tables
- Getting data into Delta Lake
- Reads and Writes
o Batch
o Streaming - Delete, update, merge
- Constraints
- Versioning
- Concurrency
- Integrations
- Overview of Delta Engine
Upcoming Batch
April 20th (Weekends)
FRI & SAT (4 Weeks)
08:30 PM to 01:00 AM (CDT)
April 18th (Weekdays)
MON – FRI (18 Days)
10:00 AM to 12:00 PM (CDT)
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