Azure Databricks Training

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Azure Databricks is a big data service based on the Apache Spark platform that enables the creation, learning, and exploration of data in the cloud. It is a data processing platform that provides scalability, performance, and ease of use. Azure Databricks allows teams to coordinate work and share code more easily.

• For individuals who want to use data to optimize processes.
• For those who want to better understand Apache Spark.
• For individuals with basic knowledge of data analysis.
• For programmers, Data Engineers, and Data Scientists.

• You will learn the fundamentals of the Azure Databricks platform.
• You will learn data processing and preparation.
• You will learn how to analyze data with Databricks SQL.
• You will learn to use Apache Spark.

• 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

• 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

• 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

• Apache Spark Programming – DataFrames

  • Databricks platform

  • Databricks ecosystem

  • Spark SQL

  • DataFrames

  • SparkSession

  • Reader and writer

  • Data sources

  • DataFrame and column

  • Column and expression

  • Transformations, actions and rows

• Apache Spark Programming – Transformations

  • Aggregation

  • Aggregation functions

  • Datetimes

  • Dates and timestamps

  • Complex types

  • Additional functions

  • UDFs

  • UDFs vectorized functions

• Apache Spark Programming – Spark Internals

  • Spark architecture

  • Spark cluster, Spark execution

  • Shuffling and caching

  • Query optimization

  • Partitioning

• Apache Spark Programming – Structured Streaming

  • Apache Spark programming

  • Streaming concepts

24 h/3 days

• Certificate of completion
• Monthly access to training recordings (for online format)
• Customization of the training program to client needs