What is business intelligence?

<text variant="body1">Organizations of all types and sizes have business processes that generate massive volumes of data. Information is constantly created by computers, the internet, phones, texts, streaming video, photographs, sensors, and more. In the global digital landscape, data is increasingly imprecise, chaotic, and unstructured. As the speed and variety of data increase exponentially, organizations are struggling to keep pace. <text variant="body1">Business intelligence is the work involved in gathering, structuring, interpreting, monitoring, and reporting this data in accessible formats that enable stakeholders to understand and use it effectively. Organizations rely on this information to make better strategic and operational business decisions. As a result, there is high demand in the marketplace for business intelligence professionals with the skills and expertise to achieve these goals.

What do business intelligence professionals do?

Business intelligence professionals are critical to many organizations today. They use data to help solve business problems, performing a variety of tasks that enable decision makers to understand and use data effectively. Some common responsibilities of BI professionals include gathering project requirements from stakeholders, retrieving and organizing large datasets, and creating visualizations and dashboards to report insights to others. Organizations use the intelligence they share to make decisions, develop new processes, create business strategies, and conduct deeper analyses.

Why start a career in business intelligence?

As businesses generate more and more data, there is increased demand for BI professionals to transform this data into meaningful business insights. BI skills are transferable to jobs across multiple industries, including financial services, education, healthcare, and manufacturing. The Google Business Intelligence Certificate will help you prepare for a job in the BI field.

Which jobs will this certificate help me prepare for?

After completing all three courses in this certificate program, you’ll have the skills required for jobs like BI analyst, BI engineer, and BI developer.

What tools and platforms are taught in the curriculum?

Business intelligence and data analytics share many of the same tools. During this certificate program, you’ll gain knowledge of tools and platforms including BigQuery, Dataflow, Python, Sheets, SQL, and Tableau.

Do I need to take the course in a certain order?

We highly recommend completing the three courses in the order presented because the content in each course builds on information covered in earlier courses.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Certificate?

When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

The Path to Insights: Data Models and Pipelines

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The Path to Insights: Data Models and Pipelines

This course is part of Google Business Intelligence Professional Certificate

Instructor: Google Career Certificates

72,317 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

716 reviews

Advanced level

Designed for those already in the industry

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

98%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

716 reviews

Advanced level

Designed for those already in the industry

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

98%

Most learners liked this course

What you'll learn

Build data models that answer business questions
Apply the ETL process to workplace scenarios
Explore ETL tools
Construct a pipeline to deliver necessary data

Skills you'll gain

Tools you'll learn

Database Systems

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

20 assignments¹

AI Graded see disclaimer

Taught in English

91%

of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

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Build your Data Analysis expertise

This course is part of the Google Business Intelligence Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate from Google

There are 4 modules in this course

This is the second of four courses in the Google Business Intelligence Certificate. In this course, you'll explore data modeling and how databases are designed. Then you’ll learn about extract, transform, load (ETL) processes that extract data from source systems, transform it into formats that enable analysis, and drive business processes and goals.

Google employees who currently work in BI will guide you through this course by providing hands-on activities that simulate job tasks, sharing examples from their day-to-day work, and helping you build business intelligence skills to prepare for a career in the field. Learners who complete the four courses in this certificate program will have the skills needed to apply for business intelligence jobs. This certificate program assumes prior knowledge of foundational analytical principles, skills, and tools covered in the Google Data Analytics Certificate. By the end of this course, you will: -Determine which data models are appropriate for different business requirements -Describe the difference between creating and interacting with a data model -Create data models to address different types of questions -Explain the parts of the extract, transform, load (ETL) process and tools used in ETL -Understand extraction processes and tools for different data storage systems -Design an ETL process that meets organizational and stakeholder needs -Design data pipelines to automate BI processes

Module details

You’ll start this course by exploring data modeling, common schemas, and database elements. You’ll consider how business needs determine the kinds of database systems that BI professionals implement. Then, you’ll discover pipelines and ETL processes, which are tools that move data and ensure that it’s accessible and useful.

What's included

19 videos16 readings9 assignments2 plugins

19 videosTotal 69 minutes

Introduction to Course 23 minutes
Ed: Overcome imposter syndrome2 minutes
Welcome to module 11 minute
Data modeling, design patterns, and schemas4 minutes
Get the facts with dimensional models5 minutes
Dimensional models with star and snowflake schemas3 minutes
Different data types, different databases7 minutes
The shape of the data4 minutes
Design useful database schemas5 minutes
Data pipelines and the ETL process6 minutes
Maximize data through the ETL process2 minutes
Choose the right tool for the job 4 minutes
Introduction to Dataflow3 minutes
Coding with Python4 minutes
Gather information from stakeholders3 minutes
Wrap-up1 minute
[Optional] Review Google Data Analytics Certificate content about data types5 minutes
[Optional] Review Google Data Analytics Certificate content about primary and foreign keys4 minutes
[Optional] Review Google Data Analytics Certificate content about BigQuery 4 minutes

16 readingsTotal 92 minutes

Helpful resources and tips4 minutes
Course 2 overview4 minutes
Design efficient database systems with schemas8 minutes
Database comparison checklist4 minutes
Four key elements of database schemas4 minutes
Review a database schema8 minutes
Business intelligence tools and their applications4 minutes
ETL-specific tools and their applications4 minutes
Guide to Dataflow8 minutes
Python applications and resources8 minutes
Merge data from multiple sources with BigQuery4 minutes
Unify data with target tables4 minutes
Activity Exemplar: Create a target table in BigQuery8 minutes
Case study: Wayfair - Working with stakeholders to create a pipeline8 minutes
Glossary terms from course 2, module 14 minutes
[Optional] Review Google Data Analytics Certificate content about SQL best practices8 minutes

9 assignmentsTotal 260 minutes

Module 1 challenge60 minutes
Choose the best schema30 minutes
Test your knowledge: Data modeling, schemas, and databases20 minutes
Test your knowledge: Choose the right database15 minutes
Test your knowledge: How data moves15 minutes
[Optional] Activity: Create a Google Cloud account30 minutes
[Optional] Activity: Create a streaming pipeline in Dataflow30 minutes
Activity: Set up a sandbox and query a public dataset in BigQuery30 minutes
Activity: Create a target table in BigQuery30 minutes

2 pluginsTotal 30 minutes

Inspect: Database models and schemas15 minutes
Transport: More about the data pipeline15 minutes

You’ll learn more about database systems, including data marts, data lakes, data warehouses, and ETL processes. You’ll also investigate the five factors of database performance: workload, throughput, resources, optimization, and contention. Finally, you’ll consider how to design efficient queries that get the most from a system.

What's included

6 videos7 readings3 assignments2 plugins

6 videosTotal 18 minutes

Welcome to module 21 minute
Data marts, data lakes, and the ETL process3 minutes
The five factors of database performance3 minutes
Optimize database performance4 minutes
The five factors in action5 minutes
Wrap-up1 minute

7 readingsTotal 48 minutes

ETL versus ELT8 minutes
A guide to the five factors of database performance4 minutes
Indexes, partitions, and other ways to optimize8 minutes
Activity Exemplar: Partition data and create indexes in BigQuery8 minutes
Case study: Deloitte - Optimizing outdated database systems8 minutes
Determine the most efficient query8 minutes
Glossary terms from course 2, module 24 minutes

3 assignmentsTotal 95 minutes

Module 2 challenge50 minutes
Activity: Partition data and create indexes in BigQuery30 minutes
Test your knowledge: Database performance15 minutes

2 pluginsTotal 30 minutes

Store: Understand data storage systems15 minutes
Design: Optimize for database speed15 minutes

You’ll learn about optimization techniques including ETL quality testing, data schema validation, business rule verification, and general performance testing. You’ll also explore data integrity and learn how built-in quality checks defend against potential problems. Finally, you’ll focus on verifying business rules and general performance testing to make sure pipelines meet the intended business need.

What's included

10 videos10 readings5 assignments2 plugins

10 videosTotal 34 minutes

Welcome to module 32 minutes
The importance of quality testing5 minutes
Mana: Quality data is useful data4 minutes
Conformity from source to destination5 minutes
Check your schema4 minutes
Verify business rules4 minutes
Burak: Evolving technology3 minutes
Wrap-up2 minutes
[Optional] Review Google Data Analytics Certificate content about data integrity3 minutes
[Optional] Review Google Data Analytics Certificate content about metadata4 minutes

10 readingsTotal 64 minutes

Seven elements of quality testing4 minutes
Monitor data quality with SQL8 minutes
Sample data dictionary and data lineage8 minutes
Schema-validation checklist4 minutes
Activity Exemplar: Evaluate a schema using a validation checklist8 minutes
Business rules8 minutes
Database performance testing in an ETL context8 minutes
Defend against known issues4 minutes
Case study: FeatureBase, Part 2: Alternative solutions to pipeline systems8 minutes
Glossary terms from course 2, module 34 minutes

5 assignmentsTotal 125 minutes

Module 3 challenge35 minutes
Test your knowledge: Optimize pipelines and ETL processes15 minutes
Activity: Evaluate a schema using a validation checklist 50 minutes
Test your knowledge: Data schema validation15 minutes
Test your knowledge: Business rules and performance testing 10 minutes

2 pluginsTotal 30 minutes

Validate: Data quality and integrity15 minutes
Evaluate: Performance test your data pipeline15 minutes

You’ll complete an end-of-course project by creating a pipeline process to deliver data to a target table and developing reports based on project needs. You’ll also ensure that the pipeline is performing correctly and that there are built-in defenses against data quality issues.