Szkolenie Azure Databricks

Program szkolenia

• Dzień: 1

  Module 1  – Databricks Lakehouse Fundamentals

  • What is the Databricks Lakehouse Platform

  • Describe what the Databricks Lakehouse Platform is.
  • Explain the origin of the Lakehouse data management paradigm.
  • Outline fundamental challenges related to managing and using data.
  • Describe security features of the Databricks Lakehouse Platform.
  • Give examples of organizations that have benefited from using the Databricks Lakehouse Platform

  • What is Databricks SQL

  • Summarize fundamental concepts for using Databricks SQL effectively
  • Identify tools and features in Databricks SQL for querying data and sharing insights
  • Explain how Databricks SQL supports data analysis workflows that allow users to extract and share business insights

  • What is Databricks Machine Learning

  • Describe the basic overview of Databricks Machine Learning.
  • Identify how using Databricks Machine Learning benefits data science and machine learning teams
  • Summarize the fundamental components and functionalities of Databricks Machine Learning
  • Exemplify successful use cases of Databricks Machine Learning by real Databricks customers

  • What is Databricks Data Science and Data Engineering Workspace

  • Describe the basic overview of Databricks Data Science and Engineering Workspace
  • Identify assets provided by the workspace
  • Describe a simple development workflow that queries and aggregates data

• Dzień: 2

  Module 2  – Data Engineering with Databricks

  • Databricks Workspaces And Services

  • Databricks Architecture and Services
  • Data Science and Engineering Workspace
  • Create and Manage Interactive Clusters
  • Notebook Basics
  • Git Versioning with Databricks Repos
  • Using Databricks Repos
  • Getting Started with the Databricks Platform

  • Delta Lakehouse

  • What is Delta Lake
  • Managing Delta Tables
  • Manipulating Tables with Delta Lake
  • Advanced Delta

  • Relational entities on Databricks

  • Databases and Views
  • Views and CTEs

  • ETL with Spark Sql

  • Query Files Directly
  • Providing Options
  • Creating Delta Tables
    Writing to Tables
    Cleaning Data
    Advanced SQL Transformations
    UDF
    

  Module 3  – Data Analysis with Databricks Sql

  • Getting Started with Databricks Sql

  • Getting Started with Databricks Sql
  • Navigating Databricks SQL
  • Unity Catalog on Databricks Sql
  • Schemas, Tables and Views on Databricks SQL

  • Basic SQL on Databricks Sql

  • Ingesting Data for Databricks Sql
  • Ingesting Data
  • Joins
  • Delta Commands in Databricks Sql

  • Presenting Data Visually

  • Data Visualization
  • Data Visualizations on Databricks Sql
  • Dashboards on Databricks Sql
  • Notifying Stakeholders

• Dzień: 3

  Module 4  – Apache Spark Programming with Databricks

  • Apache Spark Programming – Dataframes

  • Databricks platform
    • Databricks Ecosystem

  • Spark Sql
    • Spark Sql
    • Dataframes
    • SparkSession

  • Reader and writer
    • Data Sources

  • Dataframe and column
    • Column and Expression
    • Transformation ACtions and Rows

  • Apache Spark Programming – Transformations

  • Aggregation
    • Aggregation Functions

  • Datetimes
    • Dates and Timestamps

  • Complex Types
    • Complex Types

  • Additional Functions
    • Additional Functions

  • UDFS
    • UDFs Vectorized Functions

  • Apache Spark Programming – Spark Internals

  • Spark Architecture
    • Spark Cluster, Spark Execution
    • Shuffling and Caching

  • Query Optimization
    • Query Optimization

  • Partitioning
    • Partitioning

  • Apache Spark Programming – Structured Streaming

  • Apache Spark Programing
    • Streaming Concepts