Overview of Azure Databricks
Databricks is a cloud-based big data processing platform used for processing large volumes of data and performing advanced analytics. This training program will teach participants how to work with Databricks to build and deploy scalable data pipelines, perform data engineering tasks, and perform advanced analytics using Databricks’ in-built tools and libraries.
Participants will learn how to create and manage Databricks clusters, use Databricks Notebooks to perform data exploration and analysis, and perform machine learning and deep learning tasks using Databricks’ MLflow and Horovod tools. Additionally, the program will cover best practices for working with Databricks, including version control, collaboration, and deployment.
This training program is ideal for data scientists, data engineers, and other professionals who work with big data and want to learn how to leverage Databricks to build scalable data pipelines and perform advanced analytics.
Keyword: Stalwart Learning
- At Stalwart Learning, we provide hands-on Databricks training programs that are designed to equip participants with the skills needed to work with big data and perform advanced analytics using Databricks. Our comprehensive training program covers all aspects of working with Databricks, from creating clusters and notebooks to using advanced analytics tools and libraries.
- Our experienced instructors will guide participants through the program, providing real-world examples and use cases to help reinforce learning. Participants will also have access to a range of learning resources, including Databricks documentation, video tutorials, and hands-on labs.
- Whether you’re new to Databricks or have experience working with the platform, our training program will provide you with the knowledge and skills needed to succeed in the field of big data analytics.
Duration
24-40 Hours
Module 1: Introduction to Databricks
- Overview of Databricks and its features
- Understanding the Databricks Unified Analytics Platform
- Setting up a Databricks workspace
- Exploring the Databricks notebook environment
Module 2: Databricks Basics
- Introduction to Databricks clusters
- Managing and configuring clusters in Databricks
- Running code in Databricks notebooks
- Importing and exporting data in Databricks
Module 3: Data Manipulation and ETL
- Working with DataFrames in Databricks
- Transforming and cleaning data using Databricks APIs
- Performing advanced data manipulations with SQL
- Loading and saving data from various data sources
Module 4: Advanced Analytics with Databricks
- Introduction to Databricks SQL Analytics
- Performing complex SQL queries and aggregations
- Window functions and advanced SQL techniques
- Data visualization and reporting in Databricks
Module 5: Machine Learning with Databricks
- Overview of Databricks MLflow for machine learning lifecycle management
- Building and training machine learning models in Databricks
- Evaluating and tuning models with Databricks MLflow
- Deploying and serving models using Databricks serving capabilities
Module 6: Streaming and Real-time Analytics
- Introduction to Databricks Delta for stream processing
- Ingesting and processing streaming data with Databricks
- Structured Streaming in Databricks
- Real-time analytics and visualization with Databricks
Module 7: Databricks Administration and Security
- Managing users, roles, and permissions in Databricks
- Monitoring and optimizing Databricks performance
- Data security and compliance in Databricks
- Automating tasks and workflows in Databricks
Module 8: Advanced Databricks Concepts
- Introduction to Databricks ML Runtime and Delta Lake
- Advanced data engineering techniques with Databricks
- Databricks integration with external systems and tools
- Best practices for Databricks development and deployment