Databricks Corporate Training Course

Edstellar's Databricks instructor-led training course is for organizations seeking to upskill employees in DataBricks, learning advanced techniques for data processing, analytics, and machine learning. Train teams with Edstellar's expertise to enhance your organization's data capabilities.

24 - 32 hrs

Instructor-led (On-site/Virtual)

Enquire Now

About Learning Outcomes Benefits Course Outline Pricing Target Audience Pre-Requisites Trainers

Drive Team Excellence with Databricks Corporate Training

On-site or Online Databricks Training - Get the best Databricks training from top-rated instructors to upskill your teams.

Databricks provides an interactive and collaborative environment for data engineers, data scientists, and analysts to work together on big data projects. This virtual and onsite Databricks training combines data engineering, data science, and business analytics into a single unified workflow, enabling employees to derive valuable insights from the data efficiently.

Edstellar’s Databricks instructor-led training course focuses on enhancing data-related skills within organizations. The training course is designed to cater to professionals at all levels of expertise, from beginners to experienced data analysts and scientists.

Databricks Training for Employees: Key Learning Outcomes

Develop essential skills from industry-recognized Databricks training providers. The course includes the following key learning outcomes:

Create and implement machine learning models using Databricks' ML libraries to solve business problems and predict future outcomes
Evaluate the quality and reliability of data by applying data validation and cleansing techniques, ensuring accurate and trustworthy data analysis results
Apply data manipulation techniques using Databricks' DataFrame API and SQL queries to cleanse, transform, and manipulate large datasets effectively
Integrate data from various sources and formats into Databricks, allowing employees to work with diverse data sets and leverage the full potential of the platform
Analyze complex data sets using advanced analytics tools and techniques in Databricks, enabling them to extract meaningful insights and make data-driven decisions
Understand the fundamentals of data governance and compliance, including data privacy regulations and best practices for ensuring data security and compliance within an organization
Assess and visualize data using Databricks Data Validation Tool (DVT), enabling employees to create insightful charts, graphs, and dashboards to communicate findings and insights to stakeholders

Key Benefits of the Training

Provides a deep understanding of data concepts, tools, and techniques
Enables organizations to evaluate the effectiveness of their data strategies and initiatives
Enables employees to apply advanced data analysis techniques and tools in real-world scenarios
Trains employees to refine the organization's data practices and improve overall business performance
Fosters a culture of creativity and innovation by empowering employees to create data-driven solutions
Encourages professionals to synthesize their knowledge and skills to solve complex data-related challenges
Enables employees to manipulate and transform data, build machine learning models, and visualize insights
Leverage the newfound skills of employees to analyze complex business problems, make data-driven decisions, and drive innovation within the organization
Enables the professionals to integrate data from various sources, clean and prepare data for analysis, and synthesize insights to generate actionable recommendations
Develops a solid foundation in data manipulation, data governance, statistical analysis, and visualization, allowing them to understand the underlying principles and best practices in data management and analysis.

Databricks Training Topics and Outline

This Databricks Training curriculum is meticulously designed by industry experts according to the current industry requirements and standards. The program provides an interactive learning experience that focuses on the dynamic demands of the field, ensuring relevance and applicability.

Overview of Apache Spark and Databricks

1. What is Apache Spark?

2. Defining big data

3. Spark languages

Scala
Python
R
Java
SQL

4. Introduction to Databricks community edition

5. Databricks architecture

6. Understanding data analytics

7. Understanding machine learning

8. Azure implementation of Databricks

9. AWS implementation of Databricks

Getting Started with Databricks

Creating a Databricks workspace on Azure
- Account setup
- Workspace creation and configuration
Creating and configuring your Databricks cluster
- Cluster types and specifications
- Instance sizing and scaling options
- Cluster configuration settings
Creating and attaching your first notebook in Databricks
- Notebook creation and organization
- Notebook settings and configurations
Testing and running your Notebook in Databricks
- Executing code cells
- Debugging and troubleshooting
- Monitoring and logging

Uploading Data

Creating a table in Databricks
- Table creation options
- Schema definition and data types
Connecting to a Spark data source
- Importing data from various sources (e.g., CSV, JSON, databases)
- Configuring connection settings
Previewing data in your Table
- Sampling data
- Exploring data structure and content
Basics of columns and datatypes in Databricks
- Working with columns and column operations
- Understanding data types and conversions

Bringing Data into Your Notebook

Writing SQL queries to import data into Databricks notebook
- SELECT, INSERT, and other SQL operations
- Filtering and sorting data
Viewing aggregates in Databricks
- Aggregation functions (e.g., COUNT, SUM, AVG)
- Grouping and aggregating data
Performing joins in Databricks
- Different types of joins (e.g., inner, outer, left, right)
- Joining multiple tables
- Handling join conditions and duplicates

Visualizations & DataFrames

Understanding data types in Databricks
- Numeric, string, boolean, and complex data types
- Type conversions and casting
Working with DataFrames in Databricks
- DataFrame creation and manipulation
- Filtering, transforming, and aggregating data
Handling Images in Databricks
- Image data formats and processing
- Image manipulation and analysis
Exploring structured streaming DataFrames in Databricks
- Real-time data processing and analysis
- Windowing and time-based operations
Creating plots in Databricks
- Data visualization libraries and tools in Databricks
- Plotting charts, graphs, and histograms
Choosing chart types and using the chart toolbar
- Customizing chart appearance and styling
- Selecting appropriate chart types for different data scenarios
Layout and styling considerations for visualizations
- Arranging multiple charts and visuals
- Adding titles, labels, and annotations
Visualizations for machine learning in Databricks
- Visualizing model outputs and predictions
- Interpreting model performance metrics

Databricks Jobs

Creating a Job in Databricks
- Job configuration settings
- Defining job tasks and workflows
Viewing Jobs and Job details in Databricks
- Monitoring job status and progress
- Accessing job logs and history
Running your first Job in Databricks
- Executing job tasks
- Handling job dependencies and inputs
Scheduling Jobs in Databricks
- Setting up recurring and automated job runs
- Configuring scheduling options
Setting parameters for Jobs in Databricks
- Parameterizing job tasks and inputs
- Passing parameters at runtime
Viewing completed jobs in Databricks
- Analyzing job results and outputs
- Reviewing job performance metrics
Managing dependencies in Databricks
- Handling interdependent tasks and workflows
- Managing job dependencies and execution order
Setting up alerts for Databricks jobs
- Configuring email notifications and alerts
- Monitoring job failures and critical events

Delta Lake and Delta Tables

Getting data into Delta Lake in Databricks
- Creating Delta tables
- Importing data into Delta format
Performing delete, update, and merge operations in Delta tables
- Modifying data in Delta Tables
- Handling updates and deletes efficiently
Working with constraints in Delta Tables
- Defining constraints (e.g., uniqueness, referential integrity)
- Enforcing data integrity in Delta Tables
Versioning in Delta Tables
- Tracking and managing table versions
- Rollbacks and time travel capabilities
Concurrency considerations in Delta Tables
- Handling concurrent read and write operations
- Managing conflicts and consistency
Integrations with other systems in Databricks
- Integrating Delta tables with other data systems
- Data sharing and interoperability
Overview of Delta engine in Databricks
- Accelerating query performance with Delta engine
- Benefits and use cases of Delta engine

Databricks Benefits

Collaboration features in Databricks
- Notebooks collaboration
- Workspace sharing and permissions
- Version control and Git integration
Scaling capabilities of Databricks
- Auto-scaling clusters
- High-performance distributed computing
Integrating Databricks into data pipelines
- Data ingestion and ETL processes
- Integration with data storage systems
- Streamlining data workflows

This Corporate Training for Databricks is ideal for:

What Sets Us Apart?

Databricks Corporate Training Prices

Elevate your team's Databricks skills with our Databricks corporate training course. Choose from transparent pricing options tailored to your needs. Whether you have a training requirement for a small group or for large groups, our training solutions have you covered.

Request for a quote to know about our Databricks corporate training cost and plan the training initiative for your teams. Our cost-effective Databricks training pricing ensures you receive the highest value on your investment.

Request for a Quote

Our customized corporate training packages offer various benefits. Maximize your organization's training budget and save big on your Databricks training by choosing one of our training packages. This option is best suited for organizations with multiple training requirements. Our training packages are a cost-effective way to scale up your workforce skill transformation efforts..

Starter Package

125 licenses

64 hours of training (includes VILT/In-person On-site)

Tailored for SMBs

This Corporate Training for Databricks is ideal for:

Databricks training course is designed to upskill the managers, data analysts, data engineers, data scientists, machine learning engineers, IT professionals, business professionals and developers.

Prerequisites for Databricks Training

The professionals attending the Databricks training course need to have a basic understanding of big data concepts and with relevant tools and technologies. Experience in working with databases and SQL.

Assess the Training Effectiveness

Bringing you the Best Databricks Trainers in the Industry

The instructor-led Databricks Training training is conducted by certified trainers with extensive expertise in the field. Participants will benefit from the instructor's vast knowledge, gaining valuable insights and practical skills essential for success in Databricks practices.