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Welcome to the Introduction to Business Intelligence with Databricks course. This short course provides a introduction to business intelligence (BI) and the powerful Databricks platform. In today's data-driven business landscape, understanding BI and utilizing cutting-edge tools like Databricks is essential for optimizing decision-making processes and gaining valuable insights from data.

By completing this course, you will be able to:

• Gain an understanding of business intelligence and its importance in modern business operations.

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Welcome to the Introduction to Business Intelligence with Databricks course. This short course provides a introduction to business intelligence (BI) and the powerful Databricks platform. In today's data-driven business landscape, understanding BI and utilizing cutting-edge tools like Databricks is essential for optimizing decision-making processes and gaining valuable insights from data.

By completing this course, you will be able to:

• Gain an understanding of business intelligence and its importance in modern business operations.

• Acquire proficiency in utilizing the Databricks platform for data integration, preparation, visualization, and governance.

• Develop practical skills in creating queries, visualizing data, and administering work within the Databricks environment.

This course is suitable for beginners and professionals seeking to enhance their knowledge of BI and Databricks. Whether you're a data analyst, business professional, or IT specialist, this course will equip you with the skills to leverage data for informed decision-making effectively.

Learners are expected to have a basic working knowledge of the Azure services.

Join us on this journey to unlock the full potential of business intelligence with Databricks, and take your data-driven decision-making to the next level.

Enroll now

What's inside

Syllabus

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Provides a strong foundation for beginners in Business Intelligence and Databricks
Teaches advanced skills in data visualization and governance, essential for data-driven decision-making
Utilizes the powerful Databricks platform, widely recognized in the industry
Assumes basic knowledge of Azure services, which may limit accessibility for some learners

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Reviews summary

Foundational databricks for business intelligence

According to students, this course offers a solid introduction to Business Intelligence with Databricks. Learners appreciated the clear explanations and hands-on exercises, which are particularly helpful for beginners. While it provides a strong foundation in data integration, preparation, and visualization, some noted it could delve deeper into advanced topics and real-world scenarios. A few mentioned needing a strong base in Azure to avoid initial hurdles. Overall, it's considered a valuable starting point for professionals aiming to leverage Databricks for BI.
The instructor's explanations are clear and easy to follow.
"The instructor's explanations were clear."
"The instructor explained complex topics simply."
"Absolutely brilliant! The concepts are presented clearly, and the instructor is top-notch."
Hands-on labs are crucial for practical skill development.
"I particularly liked the hands-on labs, which made the concepts much clearer."
"The hands-on exercises were invaluable."
"I appreciated the emphasis on practical application of concepts."
A strong starting point for beginners in Databricks BI.
"The course provides a solid introduction to Databricks for BI."
"It provided a fantastic foundation for using Databricks for business intelligence."
"It's exactly what I needed to jumpstart my understanding of Databricks for BI."
Basic Azure knowledge is strongly recommended for seamless learning.
"Prerequisite on Azure was helpful."
"Assumed more Azure knowledge than I had, which caused some initial difficulty."
"Learners are expected to have a basic working knowledge of the Azure services, so come prepared."
Occasional issues with lab instructions or setup were noted.
"I struggled a bit with the labs. The instructions sometimes felt unclear or outdated, leading to errors."
"The lab environments occasionally had issues that made learning frustrating."
"Labs were functional but didn't always reflect complex scenarios, sometimes feeling less polished."
The course is often considered too superficial for advanced learners.
"Some parts could delve deeper into advanced topics."
"I already had some BI knowledge and found it a bit too superficial."
"I found myself needing to consult external resources for deeper understanding on several occasions."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Business Intelligence with Databricks with these activities:
Review Azure services
Familiarize yourself with the basics of Azure services to gain a stronger foundation for the upcoming course.
Browse courses on Azure Services
Show steps
  • Review Microsoft's official documentation on Azure services.
  • Watch tutorial videos on YouTube or Coursera.
  • Complete the Azure Fundamentals certification course.
Learn SQL basics
Gain a basic understanding of SQL to enhance your ability to work with data in Databricks.
Browse courses on SQL
Show steps
  • Enroll in an online SQL course on platforms like Coursera or edX.
  • Follow interactive tutorials on websites like W3Schools or SQLZoo.
  • Practice writing SQL queries using an online SQL editor.
Work with Databricks notebooks
Gain hands-on experience with Databricks notebooks to enhance your practical skills.
Browse courses on Databricks
Show steps
  • Create a free Databricks community account.
  • Explore the Databricks notebook interface.
  • Run sample notebooks provided by Databricks.
  • Create your own notebooks to experiment with data.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a Databricks community forum
Connect with other learners and industry professionals to share knowledge and support.
Browse courses on Peer Support
Show steps
  • Identify relevant Databricks community forums or groups.
  • Join the forums and introduce yourself.
  • Ask questions, share insights, and engage in discussions.
  • Attend virtual meetups or webinars organized by the community.
Build a basic data pipeline
Apply your knowledge by building a basic data pipeline to gain practical experience.
Browse courses on Data Pipeline
Show steps
  • Define the data sources and data destination.
  • Use Databricks notebooks to extract, transform, and load data.
  • Create visualizations to analyze the processed data.
  • Document your pipeline for future reference and sharing.
Connect with a Databricks expert
Seek guidance and support from experienced professionals to enhance your learning journey.
Browse courses on Mentorship
Show steps
  • Attend industry events or webinars.
  • Reach out to professionals on LinkedIn.
  • Join online communities or forums.
  • Offer to assist with projects or research.
Contribute to Databricks open-source projects
Gain real-world experience and contribute to the Databricks community.
Browse courses on Data Analytics
Show steps
  • Identify open-source projects related to Databricks.
  • Review the project documentation and code.
  • Identify areas where you can contribute.
  • Submit pull requests with your contributions.

Career center

Learners who complete Business Intelligence with Databricks will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Business Intelligence with Databricks.
Comprehensive guide to Apache Spark, providing deep technical insights and best practices for using the framework.
Seminal work on the grammar of graphics, a formal language for describing data visualizations. It covers a wide range of topics, from the basics of the grammar of graphics to the use of the grammar of graphics to create a variety of charts and graphs.
This classic book by Edward Tufte must-read for anyone interested in data visualization. It covers a wide range of topics, from the principles of visual perception to the design of effective charts and graphs.
Is specifically designed for individuals preparing for the AZ-900 certification exam. It provides a broad understanding of cloud concepts and core Azure services, making it an excellent starting point for beginners. It is commonly used as a study guide at academic institutions and by industry professionals seeking foundational Azure knowledge.
This book, written by the creator of the pandas library, practical introduction to the tools needed for data manipulation, cleaning, and preparation in Python. It is highly relevant for anyone working with data in Python and serves as an excellent resource for both beginners and those looking to solidify their understanding of using pandas and NumPy for data preparation tasks. is widely used and considered a standard reference in the field.
Offers a practical and accessible approach to data governance by focusing on leveraging existing organizational activities. It emphasizes making progress without striving for immediate perfection, making it valuable for those new to implementing governance programs. It's highly recommended for its plain language and actionable advice.
An excellent resource for those using R, this book provides a comprehensive introduction to data wrangling, transformation, and visualization using the tidyverse suite of packages. It fundamental text for anyone learning data science with R, covering essential data preparation steps. is often used as a textbook in introductory data science courses.
Serving as a comprehensive guide for the AZ-900 exam, this book covers essential cloud principles, Azure architecture, services, and management. It is suitable for a wide audience, from students to IT professionals new to Azure, helping to build a strong foundational knowledge beyond just exam preparation. It's a useful reference tool for core concepts.
Focusing specifically on the Extract, Transform, Load (ETL) process, this book practical guide for anyone involved in building data integration pipelines. It covers essential techniques for data extraction, cleaning, transformation, and delivery. serves as a valuable reference for practitioners and complements the theoretical concepts presented in 'The Data Warehouse Toolkit'.
Written by an experienced Chief Data Officer, this handbook provides an exhaustive guide to understanding data governance, its components, and positioning solutions for tangible business outcomes. It covers the entire lifecycle of a data governance program with a focus on practical implementation and value creation.
This handbook provides a comprehensive guide to securing Azure environments, covering implementation of security controls, setting guardrails, and protecting various workloads. It's an essential reference for security professionals and architects aiming to deepen their understanding of Azure security best practices and design secure solutions.
Focuses on building data lakehouses on Azure using a combination of contemporary technologies like Databricks, Synapse Analytics, and Delta Lake. It addresses a highly relevant and modern data architecture pattern, making it essential for data professionals working with large-scale data on Azure.
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Feature engineering critical part of data preparation for machine learning. focuses specifically on this topic, providing techniques and strategies for creating effective features from raw data. It valuable resource for anyone working on machine learning projects.
Focuses on practical data preprocessing specifically for machine learning applications using popular Python libraries like scikit-learn and pandas. It's highly relevant for those preparing data for modeling and provides hands-on examples. This book is particularly useful for students and professionals in the machine learning domain.
Breaks down core Azure concepts into bite-sized lessons, ideal for busy professionals or students. It provides a practical introduction to setting up VMs, deploying web servers, and using data stores through hands-on exercises. While the latest edition is from 2018, its practical approach and clear explanations make it a valuable resource for beginners gaining a broad understanding.
This handbook is designed for data and AI architects working with Azure, covering core data architecture concepts and relevant Azure services. It provides guidance on building end-to-end data and AI solutions, making it a valuable resource for designing modern data platforms on Azure.
Provides a comprehensive overview of data visualization, covering the basics of visual perception, data types, and chart types. It great resource for beginners who want to learn the fundamentals of data visualization.
This guidebook offers a comprehensive overview of the Business Intelligence lifecycle, from data integration to analytics. It provides clear explanations of architectural design and processes, making it valuable for gaining a broad understanding of the field. It useful reference for navigating the complexities of BI projects and includes practical solutions.
Aimed at aspiring and practicing architects, this book delves into designing robust, secure, scalable, and highly available solutions on Azure. It covers key design patterns, best practices, and architectural considerations. It is essential for graduate students and professionals seeking to deepen their understanding of Azure solution design.
Written by a Microsoft insider, this book details best practices for configuring and deploying Azure's native security services, from zero trust principles to defense in depth and DevSecOps. It's a practical guide with hands-on examples for securing Azure applications and is valuable for security-focused professionals.
A classic in the field, this book provides a comprehensive overview of the data governance lifecycle. It's an excellent starting point for understanding how to design, deploy, and maintain a successful data governance program within an organization. While published in 2012, its foundational principles remain highly relevant.
This cookbook provides practical recipes for performing scalable data analytics using Azure Databricks and Apache Spark. It covers data ingestion, building data warehouses, and working with large datasets. It's a valuable resource for data engineers and data scientists using Databricks on Azure.
This cookbook provides practical, step-by-step recipes for managing Azure networking services. It covers essential topics like virtual networks, security groups, load balancers, and hybrid connectivity. It's a valuable reference and learning tool for administrators and architects focusing on the networking aspects of Azure.
Combines practical Python data wrangling techniques with a focus on data quality. It covers using Python to read, write, and transform data while addressing quality issues. It's a practical resource for those who want to integrate data quality checks into their Python data preparation workflows.
Dives into the crucial role of data stewards in a data governance program. It provides actionable guidance on setting up and running a data stewardship effort, including organizing, training, and working with data stewards. The second edition is updated for the Big Data era and international business considerations.
Offers a broader perspective on data wrangling principles beyond specific tools. It delves into the process and techniques for preparing data effectively, regardless of the software or language used. It's valuable for gaining a solid understanding of the underlying concepts of data preparation. This book is suitable for both students and professionals seeking a deeper understanding of data wrangling methodologies.
For those working with Microsoft BI tools like Power BI, SSAS, and Excel, a strong understanding of DAX is essential. is considered a definitive guide to the DAX language, providing in-depth knowledge for creating powerful calculations and data models.
Given the strong regulatory landscape surrounding data, this book provides clear guidance on data protection, privacy, and information law, including GDPR and the Data Protection Act 2018. It's essential for understanding the legal context of data governance and compliance.
Apache Kafka widely used platform for building real-time data pipelines. provides a hands-on guide to Kafka Connect, a key component for integrating Kafka with various data sources and sinks. It is essential reading for anyone working with streaming data integration and Kafka.
This handbook takes a pragmatic approach to dealing with messy, real-world data. It provides a collection of techniques and war stories for handling various data quality issues. It valuable resource for practitioners who encounter challenging data problems regularly and offers practical solutions and insights.
Comprehensive guide to the entire Business Intelligence project lifecycle, from planning to deployment. It provides a structured approach to implementing BI solutions and emphasizes understanding business needs and ensuring data quality. It valuable resource for project managers and those involved in BI implementation.
For those focusing on cloud-based data integration using Microsoft Azure, this cookbook provides practical recipes for building ETL and ELT pipelines with Azure Data Factory. It covers various aspects of ADF, including connecting to data sources, transforming data, and orchestrating pipelines. valuable resource for practitioners working with Azure's data integration services.
A follow-up to the popular 'Non-Invasive Data Governance,' this book offers further experience and perspective on implementing data governance with minimal disruption. It reinforces the practical approach of recognizing and formalizing existing data-related activities. It's valuable for those looking to deepen their understanding of the non-invasive methodology.
Takes a practical approach to building and managing applications using core Azure services like compute, networking, and storage. It covers deployment, security, and performance optimization. It's a good resource for developers and IT professionals looking for a hands-on guide to getting started and becoming proficient with Azure.
This practical guide focuses on building serverless applications on Azure, primarily using Azure Functions. It covers design patterns, deployment strategies, and considerations for building enterprise-grade serverless solutions. It's highly relevant for developers interested in contemporary cloud development paradigms.
Offers a practical exploration of approaches to data privacy for technologists. It introduces fundamental concepts and provides examples and code snippets for implementing privacy in practice. It's a valuable resource for understanding how to integrate privacy into data systems, addressing a key contemporary topic in data governance.
Provides a comprehensive guide to best practices for data cleaning, covering steps to take before and after data collection. It emphasizes the importance of a systematic approach to ensure data quality. It valuable resource for researchers and data practitioners focused on rigorous data handling.
Provides real-world examples of how companies are using big data and analytics to achieve success. It showcases Business Intelligence in action across various industries, offering practical insights and inspiration. It's valuable for understanding the practical applications of BI.
Focuses on Azure Cosmos DB, Microsoft's globally distributed, multi-model database service. It covers data modeling, querying, and developing applications that leverage Cosmos DB's unique capabilities. It is valuable for developers and architects working with NoSQL databases and requiring high availability and global distribution.
While focusing on data science, this book is highly relevant to Business Intelligence as it explains how to use data mining and analytical thinking to solve business problems. It provides a solid foundation in the principles and concepts behind extracting value from data, which core component of BI.
Demystifies data governance through pragmatic advice based on real-world experience and research. It offers a case-based approach to understanding and implementing data governance frameworks, making it a practical guide for practitioners facing the complexities of the field.
Introduces Apache Spark and Azure Databricks for large-scale data analytics in the cloud. It covers fundamental concepts and progresses to topics like data lakes and machine learning. It's suitable for data professionals who are new to Spark and Databricks on Azure.
Designed for the AI-900 certification, this book introduces fundamental concepts of artificial intelligence and machine learning on Azure. It provides a broad overview of Azure's AI services and their capabilities, making it suitable for beginners and those looking to understand the basics of AI in the Azure cloud.
Focuses on the practical aspects of data engineering using Python, a popular language for building data pipelines. It covers various tools and methods for data ingestion, transformation, and orchestration. This book is particularly relevant for those interested in implementing data integration solutions using programming and open-source technologies.
Provides practical tips and tools for data wrangling using Python. It is suitable for beginners and those who want to improve their efficiency in handling messy data with Python. It covers various techniques for cleaning, transforming, and reshaping data.

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