Implementing a Data Analytics Solution with Azure Databricks (DP-3011)

Duration: 1 day
Language: English
Level: Intermediate

This one-day, hands-on course introduces learners to building scalable data analytics solutions using Azure Databricks and Apache Spark. Participants will explore how to ingest, transform, and analyse large datasets using Spark DataFrames, Spark SQL, and PySpark. The course emphasises practical skills in managing distributed data processing, optimising Delta Lake tables, and orchestrating workloads with Lakeflow Jobs and pipelines.

Learners will also gain experience in data governance and security using Unity Catalog and Microsoft Purview, ensuring their solutions are secure and production-ready. Through guided exercises and collaborative notebooks, participants will build confidence in designing ETL pipelines, enforcing data quality, and automating analytics workflows.

Implementing a Data Analytics Solution with Azure Databricks (DP-3011) Delivery Methods

In-Person
Online
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Implementing a Data Analytics Solution with Azure Databricks (DP-3011) Training Information

In this course, you will:

Navigate the Azure Databricks workspace and identify key workloads.
Ingest and explore data using Spark DataFrames and collaborative notebooks.
Transform and analyze data at scale using Apache Spark in Databricks.
Manage data consistency and versioning with Delta Lake features.
Build and deploy Lakeflow pipelines and jobs for automated data processing.
Apply governance and security practices using Unity Catalog and Purview.

Training Prerequisites

Familiarity with Python and SQL (basic scripting and query writing).
Understanding of common data formats (CSV, JSON, Parquet).
Experience with Azure portal and services such as Azure Storage.
Awareness of data concepts like batch vs. streaming and structured vs. unstructured data.

Who Should Attend

This course is ideal for:

Data Analysts looking to scale their analytics workflows using Spark and Databricks.
Data Engineers seeking to build and automate ETL pipelines in Azure.
Technical Professionals working with large datasets and cloud-based analytics platforms.

Participants will gain practical skills to manage data pipelines, perform advanced analysis, and ensure secure data operations in Azure Databricks.

Implementing a Data Analytics Solution with Azure Databricks (DP-3011) Training Outline

Module 1: Explore Azure Databricks

Understand the purpose and architecture of Azure Databricks.
Identify common workloads and key concepts in the Databricks environment.
Explore data governance features using Unity Catalog and Microsoft Purview.
Navigate the workspace and complete a guided hands-on exercise.

Module 2: Perform Data Analysis with Azure Databricks

Ingest data from sources such as Azure Data Lake and Azure SQL Database.
Use collaborative notebooks for exploratory data analysis (EDA).
Visualise and manipulate data using DataFrame APIs.
Uncover patterns and anomalies through guided analysis exercises.

Module 3: Use Apache Spark in Azure Databricks

Create and manage Spark clusters within Databricks.
Run Spark jobs to transform and analyze large datasets.
Visualise data using built-in tools and notebooks.
Apply Spark to real-world data files in a hands-on lab.

Module 4: Manage Data with Delta Lake

Create and optimise Delta tables for scalable data storage.
Enforce schema consistency and handle schema evolution.
Use time travel and versioning to manage historical data.
Ensure data integrity through ACID transactions and validation.

Module 5: Build Lakeflow Declarative Pipelines

Design scalable data pipelines using Lakeflow’s declarative approach.
Integrate real-time and batch data ingestion workflows.
Implement advanced Delta Lake features in pipeline design.
Complete a hands-on exercise to build a Lakeflow pipeline.

Module 6: Deploy Workloads with Lakeflow Jobs

Understand the components and benefits of Lakeflow Jobs.
Automate complex data processing and analytics tasks.
Deploy and monitor workloads using Databricks orchestration tools.
Create and run a Lakeflow Job in a guided exercise.

Commercial Pricing

£835

1-Day Instructor-Led Training
Hands-On Labs
Microsoft Official Content
After-Course Instructor Coaching Included
Available for Private Team Training

Would you like to take this course as a team?

Include dates with afternoon start times

Apr 21 2:00 PM - 9:30 PM BST

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May 27 2:00 PM - 9:30 PM BST

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Jul 22 2:00 PM - 9:30 PM BST

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Sep 10 9:00 AM - 4:30 PM BST

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Oct 21 2:00 PM - 9:30 PM BST

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Jan 22 2:00 PM - 9:30 PM GMT

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Implementing a Data Analytics Solution with Azure Databricks (DP-3011) FAQs

No prior experience is required. This course is suitable for both data engineering and data science professionals looking to enhance their skills with Azure Databricks.

Absolutely! Through hands-on exercises and projects, you'll gain practical experience in harnessing Azure Databricks for various data engineering tasks, including optimising data pipelines and building machine learning models.

This course not only covers key concepts and functionalities of Azure Databricks but also emphasises best practices for optimising data workflows and pipelines, ensuring you're equipped to handle real-world data challenges efficiently and effectively.

Implementing a Data Analytics Solution with Azure Databricks (DP-3011)

Implementing a Data Analytics Solution with Azure Databricks (DP-3011) Delivery Methods

Implementing a Data Analytics Solution with Azure Databricks (DP-3011) Training Information

Implementing a Data Analytics Solution with Azure Databricks (DP-3011) Training Outline

Module 1: Explore Azure Databricks

Module 2: Perform Data Analysis with Azure Databricks

Module 3: Use Apache Spark in Azure Databricks

Module 4: Manage Data with Delta Lake

Module 5: Build Lakeflow Declarative Pipelines

Module 6: Deploy Workloads with Lakeflow Jobs

Live Instructor £835

Implementing a Data Analytics Solution with Azure Databricks (DP-3011) FAQs

What background knowledge do I need to have to enroll in this course?

Will I gain practical experience in utilising Azure Databricks during this course?

How does this course prepare me for real-world data tasks?

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