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Data

Azure Databricks

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.

Duration
24h · 3 days
Ask about the program → See all
Who it's for

Ideal for teams that…

1 Individuals who want to leverage data to optimize processes.
2 Those who wish to deepen their understanding of Apache Spark.
3 Individuals with basic knowledge of data analysis.
4 Developers, Data Engineers, and Data Scientists.
Outcomes after the program

Hands-on AI and data analytics workshops — built around your team's real cases.

✓

Fundamentals of the Azure Databricks platform.

✓

Data processing and preparation techniques.

✓

Data analysis using Databricks SQL.

✓

Utilization of Apache Spark for data processing.

Program · 15 modules

What we actually do

M01
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.
M02
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.
M03
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.
M04
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.
M05
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.
M06
Delta Lakehouse
  • · What is Delta Lake.
  • · Managing Delta Tables.
  • · Manipulating Tables with Delta Lake.
  • · Advanced Delta.
M07
Relational Entities on Databricks
  • · Databases and Views.
  • · Views and CTEs.
M08
ETL with Spark SQL
  • · Query Files Directly.
  • · Providing Options.
  • · Creating Delta Tables.
  • · Writing to Tables.
  • · Cleaning Data.
  • · Advanced SQL Transformations.
  • · UDFs.
M09
Getting Started with Databricks SQL
  • · Navigating Databricks SQL.
  • · Unity Catalog on Databricks SQL.
  • · Schemas, Tables, and Views on Databricks SQL.
M10
Basic SQL on Databricks SQL
  • · Ingesting Data for Databricks SQL.
  • · Joins.
  • · Delta Commands in Databricks SQL.
M11
Presenting Data Visually
  • · Data Visualization.
  • · Data Visualizations on Databricks SQL.
  • · Dashboards on Databricks SQL.
  • · Notifying Stakeholders.
M12
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.
M13
Apache Spark Programming – Transformations
  • · Aggregation.
  • · Aggregation Functions.
  • · Datetimes.
  • · Dates and Timestamps.
  • · Complex Types.
  • · Additional Functions.
  • · UDFs.
  • · UDFs Vectorized Functions.
M14
Apache Spark Programming – Spark Internals
  • · Spark Architecture.
  • · Spark Cluster, Spark Execution.
  • · Shuffling and Caching.
  • · Query Optimization.
  • · Partitioning.
M15
Apache Spark Programming – Structured Streaming
  • · Apache Spark Programming.
  • · Streaming.
Every module is adapted to your stack and context. The above is a starting point — not a fixed agenda.
How we work

From brief to retro in 30 days.

01

Brief & diagnosis

A call with the team lead + a short survey for participants. We define goals, gap and context.

02

Program customization

We adapt modules, case studies and code examples to your stack. Approval in 5 days.

03

Workshop

Trainer-led sessions, hands-on, code review. Mentor available between sessions too.

04

Retro + report

Outcome report for the team and lead. 30 days of consulting included.

Inquiry

Send a brief. We'll reply within 1 day.

After a short brief we'll prepare a program and a quote. No obligations — it's just a starting point.

✓Quote within 48h of the brief
✓First session within 30 days
✓Pilot before the full decision
✓VAT invoice, payment in instalments possible

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