Learn DataBricks

Databricks is an integrated platform built on Apache Spark designed to provide a unified solution for data engineering, machine learning, and analytics. The Databricks Programmer/Analyst course introduces Databricks and its key features, covering data ingestion, transformation, and analysis using Spark and Delta Lake.

1. Overview of Databricks:
   • What is Databricks?
   • Key features and advantages of Databricks
   • Use cases for Databricks in data analytics

1. Setting up Databricks Environment:
   • Creating a Databricks account
   • Navigating the Databricks interface (Workspace, Clusters, and Notebooks)
   • Cluster management basics (starting, stopping, configuring)

1. Introduction to Apache Spark:
   • Overview of Spark's architecture (Driver, Executors, and Partitions)
   • Understanding Spark Operations: Transformations and Actions

1. Working with Spark DataFrames:
   • Creating DataFrames from various file formats (CSV, JSON)
   • Basic DataFrame operations (filter, select, group by)
   • Introduction to Spark SQL: Writing SQL queries on DataFrames

1. Loading Data into Databricks:
   • Reading data from local files (CSV, JSON)
   • Loading data from cloud storage (AWS S3, Azure Blob)

1. DataFrame Transformations:
   • Basic data cleaning: Handling missing values, filtering, and sorting
   • Performing simple aggregations (sum, average, count)

1. What is Delta Lake?
   • Overview of Delta Lake and its benefits
   • Creating and querying Delta Tables

1. Basic Delta Operations:
   • Performing updates, deletes, and merges on Delta Tables
   • Understanding Delta's Time Travel feature

1. Creating and Managing Notebooks:
   • Writing and running code in notebooks (Python, SQL, Scala)
   • Using markdown to document notebooks
   • Sharing and collaborating with notebooks

1. Visualizing Data:
   • Built-in Databricks visualization tools
   • Creating basic charts and graphs from DataFrames

1. Writing SQL Queries:
   • Introduction to Databricks SQL
   • Performing basic SQL operations (SELECT, JOIN, GROUP BY)

1. Creating Views and Tables:
   • Creating temporary and permanent views
   • Querying tables with SQL in Databricks notebooks

1. Cluster Basics:
   • Understanding cluster types and use cases
   • Scaling clusters to handle larger datasets

1. Installing Libraries on Clusters:
   • Adding Python or Spark libraries to clusters

1. Real-Time Data with Structured Streaming:
   • Basic concepts of streaming data
   • A simple example of setting up a streaming job

The Databricks Programmer/Analyst course also includes hands-on exercises and case studies to build practical skills. By completing this course, participants will gain foundational knowledge of Databricks and how to use it for data engineering, analytics, and machine learning tasks.