Databricks Curriculum - From Zero to Hero

Stage 1: Beginner

Topic 1: Introduction to Databricks

• Prerequisites: None
• Enables: Understanding of what Databricks is and what it can do.

• Reasoning: As a starting point, you need to understand what Databricks is and why it's used.

• Understand the concept of Databricks

• Learn about the history and evolution of Databricks
• Understand the benefits and use-cases of Databricks
• Explore the architecture of Databricks

Topic 2: Setting up Databricks

• Prerequisites: Introduction to Databricks
• Enables: Ability to setup and navigate the Databricks environment.

• Reasoning: Before you can use Databricks, you need to know how to set it up and navigate the platform.

• Create a Databricks account

• Understand the Databricks workspace
• Learn how to create a Databricks cluster
• Learn how to create notebooks and libraries
• Understand how to manage and monitor clusters

Topic 3: Introduction to Apache Spark

• Prerequisites: Setting up Databricks
• Enables: Understanding of Apache Spark and its importance in Databricks.

• Reasoning: Databricks is built on Apache Spark, so understanding Spark is crucial.

• Understand the concept of Apache Spark

• Learn about the history and evolution of Apache Spark
• Understand the architecture of Apache Spark
• Explore the core components of Spark: Spark SQL, Spark Streaming, MLlib, and GraphX
• Understand how Spark integrates with Databricks

Topic 4: Basic Data Processing with Databricks

• Prerequisites: Introduction to Apache Spark
• Enables: Ability to perform basic data processing tasks in Databricks.

• Reasoning: Data processing is a key function of Databricks.

• Understand the concept of data processing

• Learn how to load and inspect data in Databricks
• Understand the basic operations on data such as filtering, aggregation, and transformation
• Learn how to visualize data in Databricks
• Understand how to save and export processed data

Stage 2: Intermediate

Topic 5: DataFrames and SQL in Databricks

• Prerequisites: Basic Data Processing with Databricks
• Enables: Ability to use DataFrames and SQL for data manipulation in Databricks.

• Reasoning: DataFrames and SQL are essential tools for data manipulation in Databricks.

• Understand the concept of DataFrames in Spark

• Learn how to create DataFrames from different data sources
• Perform operations on DataFrames such as select, filter, and aggregate
• Understand the concept of SQL in Spark
• Learn how to perform SQL queries on DataFrames
• Understand how to convert between DataFrames and SQL

Topic 6: ETL Processes in Databricks

• Prerequisites: DataFrames and SQL in Databricks
• Enables: Understanding and implementation of ETL processes in Databricks.

• Reasoning: ETL (Extract, Transform, Load) processes are a key part of data processing in Databricks.

• Understand the concept of ETL (Extract, Transform, Load)

• Learn how to extract data from different sources in Databricks
• Understand how to transform data using Spark transformations
• Learn how to load data into different destinations
• Perform a complete ETL process on a sample dataset

Topic 7: Machine Learning with Databricks

• Prerequisites: ETL Processes in Databricks
• Enables: Ability to use Databricks for machine learning tasks.

• Reasoning: Machine learning is a powerful tool for data analysis, and Databricks provides robust support for machine learning tasks.

• Understand the concept of machine learning

• Learn about the machine learning library in Spark (MLlib)
• Understand the machine learning workflow: data preparation, model training, model evaluation, and model deployment
• Learn how to prepare data for machine learning
• Train and evaluate a machine learning model on a sample dataset

Stage 3: Advanced

Topic 8: Stream Processing in Databricks

• Prerequisites: Machine Learning with Databricks
• Enables: Ability to handle real-time data streams in Databricks.

• Reasoning: Real-time data processing is a critical capability in many data-intensive applications.

• Understand the concept of stream processing

• Learn about Spark Streaming and its integration with Databricks
• Understand how to ingest real-time data streams
• Learn how to perform transformations and actions on data streams
• Understand how to output data streams to various destinations

Topic 9: Advanced Spark Programming in Databricks

• Prerequisites: Stream Processing in Databricks
• Enables: Mastery of advanced Spark programming techniques in Databricks.

• Reasoning: To fully leverage the power of Databricks, you need to be proficient in advanced Spark programming techniques.

• Deepen understanding of Spark's core concepts

• Learn about Spark's advanced features such as Spark's Catalyst Optimizer, Tungsten Execution Engine, and GraphX for graph processing
• Understand how to optimize Spark applications for performance
• Learn how to debug and troubleshoot Spark applications
• Understand how to manage and monitor Spark applications in Databricks

Topic 10: Databricks for Data Science

• Prerequisites: Advanced Spark Programming in Databricks
• Enables: Ability to use Databricks as a tool for advanced data science tasks.

• Reasoning: Databricks is a powerful tool for data science, and mastering its use for these tasks will enable you to tackle complex data science problems.

• Understand how Databricks can be used for advanced data science tasks

• Learn about Databricks' integration with popular data science libraries and tools
• Understand how to perform exploratory data analysis in Databricks
• Learn how to build, evaluate, and tune advanced machine learning models
• Understand how to deploy machine learning models in Databricks

This curriculum provides a comprehensive path from beginner to advanced user of Databricks. By following this path, you will gain a deep understanding of Databricks and be able to use it effectively for a wide range of data processing and data science tasks.