Azure Databricks Training
Level
IntermediateDuration
24h / 3 daysDate
Individually arrangedPrice
Individually arrangedAzure Databricks Training
Azure Databricks is a big data service based on the Apache Spark platform that enables the creation, training, 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 more easily and share code.
What You Will Learn
- Fundamentals of the Azure Databricks platform.
- Data processing and preparation techniques.
- Data analysis using Databricks SQL.
- Utilization of Apache Spark for data processing.
Who is this training for?
Individuals who want to leverage data to optimize processes.-
Those who wish to deepen their understanding of Apache Spark.
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Individuals with basic knowledge of data analysis.
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Developers, Data Engineers, and Data Scientists.
Training Program
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What is the Databricks Lakehouse Platform
- Description of the Databricks Lakehouse Platform
- Origin of the Lakehouse data management paradigm
- Fundamental challenges in managing and using data
- Security features of the Databricks Lakehouse Platform
- Examples of organizations benefiting from Databricks Lakehouse
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What is Databricks SQL
- Fundamental concepts for using Databricks SQL effectively
- Tools and features for querying data and sharing insights
- Supporting data analysis workflows and business insight sharing
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What is Databricks Machine Learning
- Overview of Databricks Machine Learning
- Benefits for data science and machine learning teams
- Core components and functionalities
- Examples of real-world customer use cases
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Databricks Data Science and Data Engineering Workspace
- Overview of the workspace
- Assets provided by the workspace
- Example development workflow for querying and aggregating data
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Databricks Workspaces and Services
- Databricks architecture and services
- Data Science and Engineering Workspace
- Creating and managing interactive clusters
- Notebook basics
- Git versioning with Databricks Repos
- Using Databricks Repos
- Getting started with the Databricks platform
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Delta Lakehouse
- What is Delta Lake
- Managing Delta tables
- Manipulating tables with Delta Lake
- Advanced Delta features
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Relational Entities on Databricks
- Databases and views
- Views and Common Table Expressions (CTEs)
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ETL with Spark SQL
- Querying files directly
- Providing options
- Creating Delta tables
- Writing to tables
- Cleaning data
- Advanced SQL transformations
- User-defined functions (UDFs)
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Getting Started with Databricks SQL
- Navigating Databricks SQL
- Unity Catalog on Databricks SQL
- Schemas, tables, and views
- Basic SQL operations
- Ingesting data
- Joins
- Delta commands in Databricks SQL
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Presenting Data Visually
- Data visualization concepts
- Visualizations in Databricks SQL
- Dashboards
- Notifying stakeholders
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Apache Spark Programming – DataFrames
- Databricks platform and ecosystem
- Spark SQL
- DataFrames and SparkSession
- Reader and writer APIs
- Data sources
- DataFrame, column, and expressions
- Transformations, actions, and rows
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Apache Spark Programming – Transformations
- Aggregation and aggregation functions
- Date and time processing
- Dates and timestamps
- Complex data types
- Additional functions
- UDFs and vectorized UDFs
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Apache Spark Programming – Spark Internals
- Spark architecture
- Spark cluster and execution model
- Shuffling and caching
- Query optimization
- Partitioning
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Apache Spark Programming – Structured Streaming
- Streaming fundamentals in Apache Spark
