Data Analytics Solutions with Azure Databricks training course

Master dimensional modeling for optimized querying and analysis, with skills applicable to databases like Oracle and SQL Server. Utilize powerful clusters on Databricks, whether on Azure, AWS, or Google Cloud.

JBI training course London UK

" I enjoyed the depth that we covered analytical techniques such as anomaly detection and  cluster analysis, whilst improving my knowledge on DAX and KPIs."BC, Performance analyst, Data Analysis with Power BI, April 2021

Public Courses

17/08/26 - 2 days
£2000 +VAT
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28/09/26 - 2 days
£2000 +VAT
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09/11/26 - 2 days
£2000 +VAT
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Customised Courses

* Train a team
* Tailor content
* Flex dates
From £1200 / day
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JBI training course London UK
  • Learn Azure Databricks for data engineering and analytics
  • Master Apache Spark for large-scale data processing
  • Utilize powerful clusters on the Azure platform
  • Designed for Data Engineers and Data Scientists
  • Hands-on experience with cloud-based data engineering tasks
  • Intermediate-level training for real-world data challenges
  • Utilize powerful clusters on Databricks, whether on Azure, AWS, or Google Cloud

Course Details

Learn Azure Databricks for Data Engineering and Analytics

  1. Gain a comprehensive understanding of the Azure Databricks platform
  2. Learn to integrate Databricks with Azure tools for efficient data analytics
  3. Explore the basics of creating and managing Databricks workspaces and notebooks

Intermediate-Level Training for Real-World Data Challenges

Build on existing data skills and gain expertise in cloud data engineering Tackle complex data engineering workflows with Databricks and Spark Prepare for real-world applications and industry-specific data solutions

Hands-on Experience with Cloud-Based Data Engineering Tasks

Work on real-world data engineering problems using Databricks Perform data transformation, ETL, and data analysis in the cloud Collaborate on projects that simulate actual data challenges

Designed for Data Engineers and Data Scientists

Tailored curriculum for professionals in data engineering and data science Focus on practical skills for managing big data in cloud environments Build a strong foundation for advanced data engineering and analytics

Utilize Powerful Clusters on the Azure Platform

Set up and manage Azure Databricks clusters for optimal performance Scale clusters to handle large datasets and complex computations Optimize workloads for cost-effectiveness and resource management

Master Apache Spark for Large-Scale Data Processing

Dive into Apache Spark’s core concepts and architecture Learn Spark's capabilities for distributed data processing at scale Implement Spark operations using Databricks for real-time data processing

Utilize powerful clusters on Databricks, whether on Azure, AWS, or Google Cloud

JBI training course London UK
  • Data Engineers looking to enhance their cloud-based data processing skills
  • Data Scientists wanting to leverage Azure Databricks for advanced analytics
  • Professionals with experience in data handling who want to scale up to large datasets
  • Anyone interested in mastering Apache Spark and Azure Databricks for big data solutions
5 star

4.8 out of 5 average

" I enjoyed the depth that we covered analytical techniques such as anomaly detection and  cluster analysis, whilst improving my knowledge on DAX and KPIs."BC, Performance analyst, Data Analysis with Power BI, April 2021

Watch live client feedback from Data Analytics courses: 

“JBI  did a great job of customizing their syllabus to suit our business  needs and also bringing our team up to speed on the current best practices. ” Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022

JBI training course London UK

Certification


Every delegate will be entitled to a certificate of achievement on completion of the course.

If you are missing your certificate - please use the link below to apply - you can also use this link to sign up for the JBI Training newsletter to receive technology tips directly from our instructors - Analytics, AI, ML, DevOps, Web, Backend and Security.
 



More about this course

This intermediate-level course is designed to equip data professionals with the skills needed to effectively use Azure Databricks for large-scale data engineering and analytics. You'll learn how to leverage Apache Spark within the Azure cloud platform to handle complex data processing tasks, optimize cluster management, and enhance your data workflows.

 

Unlock the potential of Databricks with our intermediate-level course designed for data professionals, including Data Engineers and Data Scientists. Dive into the world of Apache Spark and master the utilization of powerful clusters on Databricks across various cloud platforms, including Azure, AWS, and Google Cloud, to tackle large-scale data engineering tasks in the cloud.

Key Highlights:

  • In-depth Azure Databricks Training: Learn the ins and outs of working within the Databricks environment, including workspace creation, notebook management, and integration with Azure services.
  • Advanced Data Processing Techniques: Master Apache Spark to process large volumes of data, perform real-time analytics, and implement powerful ETL pipelines.
  • Cloud-Based Data Engineering Skills: Gain hands-on experience with cloud-based tools and services that allow for scalable, cost-effective data engineering solutions.
  • Real-World Applications: Work on practical, industry-relevant projects to simulate real-world data engineering and analytics tasks, ensuring you're prepared for challenges in the field.

By the end of this course, you'll be equipped with the expertise to tackle complex data challenges and optimize workflows in cloud environments using Azure Databricks and Apache Spark.

FAQs

JBI Training offers four data analytics courses covering different levels and specialisms. Available courses are Introduction to Data Analytics (three days), Excel for Data Analysts (one day), Dimensional Data Modelling (three days), and Data Analytics Solutions with Azure Databricks (two days). All courses are available as scheduled classroom sessions in London, as live online instructor-led training, or as customised onsite programmes for data and analytics teams.
The Introduction to Data Analytics course is a three-day programme designed for professionals who are new to data analytics or who want to build a solid foundation in analytical thinking and practice. It covers the core concepts of data analysis, working with data from different sources, data cleaning and preparation, descriptive and exploratory analysis techniques, data visualisation principles, and interpreting and communicating analytical findings. It is suited to business analysts, reporting professionals, operations staff, and anyone moving into a more data-focused role who needs a structured foundation before progressing to tool-specific training such as Power BI, Python, or SQL.
Dimensional data modelling is the practice of structuring data in a way that is optimised for analytical querying and business intelligence reporting — as opposed to transactional database design. It is the foundational approach behind data warehouses and data marts, using concepts such as fact tables, dimension tables, star schemas, and snowflake schemas. JBI's three-day Dimensional Data Modelling course covers the theory and practice of dimensional modelling in depth, including how to identify and define business processes, granularity decisions, slowly changing dimensions, and how to design models that perform well at scale and integrate cleanly with BI tools such as Power BI and Tableau. It is suited to data engineers, BI developers, data architects, and analytics engineers who design or work with data warehouse structures.
The two-day Data Analytics Solutions with Azure Databricks course covers how to use the Azure Databricks platform — built on Apache Spark and optimised for the Azure cloud — to build scalable data analytics solutions. Topics include the Databricks workspace environment, ingesting and transforming data using notebooks and Spark DataFrames, Spark SQL for analytical querying, Delta Lake for reliable, versioned data storage, integrating Databricks with Azure Data Lake and Azure Synapse Analytics, and building analytics workflows that support both batch and streaming data. It is suited to data engineers, analytics engineers, and data scientists who work within the Azure data ecosystem and need to process and analyse data at scale.
The Excel for Data Analysts course is a one-day focused programme covering the Excel features and techniques most relevant to analytical work — going beyond basic spreadsheet use to cover tools that professional analysts use for data exploration and reporting. Topics include advanced lookup and reference functions, dynamic arrays, Power Query for importing and transforming data from external sources, PivotTables and PivotCharts for summarising and exploring data, data validation, conditional formatting for visual analysis, and an introduction to the Excel Data Model for handling larger datasets. It is suited to analysts who use Excel regularly but want to work more efficiently and handle more complex datasets without switching to a specialist BI or programming tool.
Yes. All data analytics courses at JBI can be delivered as customised onsite or online programmes for corporate data and analytics teams. Content and exercises can be tailored to the team's existing tools, data sources, skill levels, and analytical objectives. For example, a team new to analytics can receive a structured programme starting with foundations and progressing to tool-specific skills, while an experienced team adopting Azure Databricks can receive training focused specifically on that platform and their cloud architecture. JBI has delivered data analytics training for teams at organisations including the BBC, NHS, RBS, Sky, EDF, and Capita.
Yes. Data analytics is a rapidly evolving field and JBI's training content is continuously reviewed to reflect the latest developments across the tools and techniques covered. This includes updates to Azure Databricks and Delta Lake capabilities, new Excel features including Copilot integration and dynamic array functions, evolving best practices in dimensional modelling for modern cloud data warehouse platforms such as Microsoft Fabric, Snowflake, and BigQuery, and the growing role of AI-assisted analytics in day-to-day analytical workflows. Delegates learn skills that are current and directly applicable to the analytical environments and tools used in professional data roles today.

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