Book Image

Business Intelligence Career Master Plan

By : Eduardo Chavez, Danny Moncada
Book Image

Business Intelligence Career Master Plan

By: Eduardo Chavez, Danny Moncada

Overview of this book

Navigating the challenging path of a business intelligence career requires you to consider your expertise, interests, and skills. Business Intelligence Career Master Plan explores key skills like stacks, coursework, certifications, and interview advice, enabling you to make informed decisions about your BI journey. You’ll start by assessing the different roles in BI and matching your skills and career with the tech stack. You’ll then learn to build taxonomy and a data story using visualization types. Additionally, you’ll explore the fundamentals of programming, frontend development, backend development, software development lifecycle, and project management, giving you a broad view of the end-to-end BI process. With the help of the author’s expert advice, you’ll be able to identify what subjects and areas of study are crucial and would add significant value to your skill set. By the end of this book, you’ll be well-equipped to make an informed decision on which of the myriad paths to choose in your business intelligence journey based on your skill set and interests.
Table of Contents (12 chapters)

Technology solutions stack

Technology is the vessel of our projects, and navigating the ocean of data requires mastering the right tools and skills. A typical technology stack consists of the following components:

  • Data warehousing: A centralized repository that stores large amounts of data from various sources.

Becoming proficient in designing data warehouses requires a combination of theoretical knowledge and practical experience. Here is some guidance you can follow to improve your skills:

  • Gain a strong understanding of data warehousing concepts and methodologies: This includes understanding the differences between transactional and analytical systems, the basics of dimensional modeling, and the differences between star and snowflake schemas. There are many philosophies out there on how to properly design a data warehouse; researching them will improve your understanding and provide use cases for each of them. Examples include Ralph Kimball, Bill Inmon, and...