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
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.
- Provide 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.
- Provide examples of 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.
- UDFs.
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.
- 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.
- Transformation 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.
