Data Engineering using Databricks on AWS and Azure

Master Data Engineering with Databricks: A Comprehensive Course

Unlock the Power of Cloud-Agnostic Data Pipelines

This advanced course empowers you to become a proficient Data Engineer using Databricks, the leading cloud platform for modern data processing. Learn to build robust and scalable data pipelines for various needs, including batch processing and streaming data. This course caters to experienced developers, data engineers, and testers looking to enhance their skills with the power of cloud data engineering.

What is Data Engineering?

Data Engineering is the crucial process of preparing data for downstream consumption. This involves building and maintaining data pipelines to ingest, transform, and deliver data to meet business requirements. Key roles within data processing, such as ETL development and data warehouse development, fall under the umbrella of Data Engineering.

Why Databricks?

Databricks is a highly sought-after, cloud-agnostic data engineering platform built around the open-source Apache Spark project. It offers a unified environment for data engineering, data science, and machine learning. Databricks' innovative Lakehouse architecture combines the best features of data lakes and data warehouses, enabling both traditional BI and advanced analytics.

Key Features You'll Master:

• Spark: Leverage distributed computing for high-performance data processing.
• Delta Lake: Implement reliable and efficient data management with ACID transactions, versioning, and schema evolution.
• CloudFiles: Efficiently access and manage files in your cloud storage.
• Databricks SQL: Utilize a powerful, Photon-accelerated interface for data analysis and reporting.

Course Curriculum: A Deep Dive into Data Engineering with Databricks

Getting Started with Databricks on Azure:

• Azure Account Setup and Quota Management
• Launching and Configuring Azure Databricks Workspaces and Clusters
• Navigating the Azure Databricks User Interface
• Creating and Managing Single Node Clusters
• Data Upload and Validation using the Azure UI

Azure Essentials for Databricks:

• Utilizing the Azure CLI for resource management
• Working with Azure AD and creating service principals
• Managing Azure Data Lake Storage (ADLS)
• Mounting ADLS to Databricks Clusters

Setting Up Your Local Development Environment:

• Installing and configuring Databricks CLI
• Integrating Databricks Connect with IDEs like PyCharm
• Working with Databricks dbutils
• Utilizing Databricks Jobs and Clusters for development

Deploying and Running Spark Applications:

• Preparing PyCharm for Databricks development
• Refactoring code for Databricks
• Validating data within Databricks
• Deploying and running Spark applications using notebooks and interactive clusters
• Managing and validating data using Databricks Jobs

Deep Dive into Delta Lake with Spark DataFrames:

• Creating and manipulating Delta Lake tables using Spark DataFrames
• Data manipulation operations: insert, update, delete, merge/upsert
• Data versioning and time travel using Delta Logs
• Optimizing Delta Lake performance with vacuuming and compaction

Deep Dive into Delta Lake with Spark SQL:

• Creating and querying Delta Lake tables using Spark SQL
• Data manipulation using SQL commands
• Utilizing Delta Lake for data recovery and historical analysis

Accessing Databricks Cluster Terminals:

• Accessing the Web Terminal and SSH for cluster management
• Installing software on Databricks clusters using init scripts

Spark Structured Streaming for Real-time Data Processing:

• Understanding Spark Structured Streaming concepts
• Validating Netcat connectivity
• Reading and writing streaming data
• Performing incremental loads with Spark Structured Streaming

Automated Data Ingestion with Databricks Auto Loader:

• Overview of Auto Loader for efficient data ingestion from cloud storage
• Configuring Auto Loader for incremental loads
• Utilizing cloudFiles for data discovery and processing

Databricks SQL: Data Analysis and Reporting:

• Exploring the Databricks SQL platform and its features
• Writing and executing SQL queries
• Creating and managing Databricks SQL endpoints
• Analyzing data using Databricks SQL Data Explorer

Who Should Take This Course?

• Experienced Application Developers seeking to expand their skillset into Data Engineering with Spark.
• Experienced Data Engineers looking to leverage the power of Databricks.
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