This Databricks with AWS teaches you how to use AWS services like Apache Spark, Redshift, and S3 to make data pipelines and analytics solutions that can grow with your needs. You will learn how to work with, analyze, and use large datasets to get useful business insights. Participants will learn to efficiently manage big data, automate data pipelines, and utilize machine learning tools to derive insights from vast datasets. By the end of the course, attendees will have a solid understanding of best practices for collaborative data science and will be able to apply concepts like data engineering, notebook workflows, and SQL analytics in real-world scenarios.

Course Objectives:

In this course, you will learn to:

• Understand the architecture and core components of Databricks on AWS.
• Use Apache Spark for data processing and analysis in Databricks.
• Implement and manage Delta Lake for reliable data storage.
• Build and deploy machine learning models using MLflow.
• Develop interactive notebooks for data analysis and visualization.
• Integrate Databricks with various AWS services (e.g., S3, Redshift).
• Optimize Spark jobs for better performance and cost efficiency.
• Manage clusters and jobs effectively within the Databricks environment.
• Apply best practices for data governance and security within Databricks.
• Collaborate with teams using Databricks workspace features and tools.

Course content

Module 1: Fundamentals of Data Engineering

Understanding Data Engineering Concepts and Principles

• Core Concepts and Principles in Data Engineering
• Overview of Data Pipelines, Data Integration, and Data Transformation

Overview of Databricks

• Databricks as a Unified Analytics Platform
• Key Features and Benefits of Using Databricks for Data Engineering
• Overview of Databricks Architecture and Components
• Components of Databricks Architecture
• Understanding the Databricks Workspace and Its Functionalities
• Introducing Databricks Notebooks and Its Role in Data Engineering

Module 2: Setting Up Databricks Environment and Workspace

Databricks Utilities

• File System Utilities (dbutils.fs)
• Library Utility (dbutils.library)
• Notebook Utility (dbutils.notebook)
• Secrets Utility (dbutils.secrets)
• Widgets Utility (dbutils.widgets)

Configuring Databricks Clusters

• Selection Criteria for Different Workloads
• Databricks Runtime Versions
• Cluster Sizing
• Cluster Sizing Considerations
• Cluster Sizing Examples
• Differences Between Standard and High Concurrency Clusters
• SetUp Databricks Workspace on AWS
• Databricks UI walkthrough
• Configure Databricks cluster on AWS
• Bring your data to Databricks UI Dashboard
• Creating Databricks Notebooks
• Develop Spark application using notebook

Module 3: Delta Lakes and Delta Tables

• Understanding the Benefits of Delta Tables in Databricks
• Enhanced Data Reliability and Consistency with Delta Tables
• Efficient Data Processing and Query Optimization
• Overview of Delta Lake Architecture and Concepts
• Introduction to Delta Lake
• Delta Lake Architecture

Module 4: Data Ingestion and Extraction

• Ingesting Data into Databricks
• Understanding the Importance of Data Ingestion
• Choosing the Appropriate Data Ingestion Method
• Identifying Key Considerations for Data Ingestion
• Implementing Data Ingestion Best Practices

Module 5: Data Pipelines with Databricks

• Overview of the ETL Process
• Reading Data from Different Sources in Databricks
• Using Pre-built Connectors in ETL Pipeline Tool
• Building Scalable Data Transformation Pipelines
• Understanding the Importance of Data Transformation in Data Engineering
• Designing Scalable and Efficient Data Transformation Pipelines
• Techniques for Data Transformation in Databricks
• Applying ETL Methodologies
• Overview of the ETL Process and Its Key Components
• Optimizing Data Processing and Performance in Databricks
• Strategies for Optimizing Data Processing in Databricks
• Leveraging Databricks Runtime Configurations for Performance Improvements
• Performance Tuning for Data Transformation Operations in Databricks

Module 6: Data Orchestration and Workflow Management

Implementing Workflow Automation with Databricks

• Overview of Workflow Automation
• Introduction to Databricks Jobs and Notebooks for Workflow Automation
• Designing and Implementing Automated Data Pipelines in Databricks

Managing Dependencies and Scheduling Data Pipelines

• Understanding Dependencies Between Data Pipelines and Tasks
• Techniques for Managing Dependencies in Databricks Workflows
• Scheduling and Orchestrating Data Pipelines Using Databricks Jobs
• Best Practices for Handling Complex Workflows and Task Dependencies

Monitoring and Error Handling in Workflow Execution

• Strategies for Monitoring Workflow Execution
• Implementing Logging and Alerting Mechanisms for Error Detection
• Techniques for Handling Workflow Failures and Retries
• Utilizing Databricks Monitoring and Debugging Tools for Workflow Optimization

Module 7: Data Security and Governance

Unity Catalog

• Understanding the Unity Catalog in Databricks
• Overview of Metadata Management and Data Discovery in Unity Catalog
• Leveraging Unity Catalog for Efficient Data Governance and Metadata Management

Ensuring Data Privacy and Compliance in Databricks

• Importance of Data Privacy and Compliance
• Implementing Privacy Measures and Techniques in Databricks
• Ensuring Compliance with Data Protection Regulations
• Techniques for Anonymization, Pseudonymization, and Data Masking

Implementing Access Controls and Data Encryption

• Overview of Access Controls and Authorization in Databricks
• Designing and Implementing Access Policies for Data Protection
• Techniques for Encrypting Data at Rest and in Transit in Databricks
• Implementing Key Management and Secure Credential Storage Practices

Data Governance Best Practices

• Importance of Data Governance
• Techniques for Data Lineage, Metadata Management, and Data Cataloging
• Best Practices for Data Documentation, Stewardship, and Data Lifecycle Management

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