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Analytics Engineering Bootcamp

Become an Analytics Engineer expert with just ONE course. Learn Data Modelling, dbt, Google Bigquery & many more!

  1. Topics
  2. IT & Software
  3. Analytics Engineering

Analytics Engineering Bootcamp

InstructorRahul Prasad
Duration12h 9m
Students16.3K
Rating4.5 (1,991)
Sponsored
Price
$19.99
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Comidoc Analysis

Data Modeling Methodologies and dbt Implementation

Strengths

Diverse Modeling Methodologies

Covers a wide range of architectural approaches including Inmon, Kimball, Data Vault, and One Big Table (OBT).

Practical dbt Workflows

Includes hands-on construction of a dimensional data warehouse and advanced development using the dbt Power User extension.

More Analytics Engineering courses

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Learn dbt core using ParadimeRahul Prasad4.8 (13)1h 52m
DP-600: Fabric Analytics Engineer Associate | Practice ExamsNikolai Schuler4.3 (55)
DP-600: Microsoft Fabric Analytics Engineer Ultimate CourseCloud Guru Amit4.3 (21)10h 4m
dbt Analytics Engineering Certification - Practice ExamsBrian Shand3.4 (4)
DP-600 Microsoft Fabric Analytics Engineer Associate 2025Shashi Palod4.3 (17)
Le cours ultime DBT pour la Data 2025Adam Alloul4.5 (45)4h 58m

Limitations

Instructional Delivery Style

Some segments rely on reading bullet points from slides, which may lack sufficient visual depth or shareable diagrams.

Assessment and Depth Concerns

Quizzes may prioritize topical recall over deep conceptual application, and some learners found the content more of an overview than a deep dive.

Best suited to

  • Data Analysts transitioning to Analytics Engineering
  • BI Analysts seeking structured warehousing knowledge
  • Learners with basic SQL and programming familiarity

Less suited to

  • Absolute beginners with no coding experience
  • Learners seeking deep theoretical nuance beyond an overview

Comidoc Score

6.4/10

Worth considering
Beginner-friendly

Comidoc verdict

The learning path moves from fundamental database theory into the practical implementation of data warehouses using BigQuery and dbt. The curriculum provides a strong foundation in various modeling methodologies, such as Kimball and Data Vault, alongside specific dimensional modeling techniques like Slowly Changing Dimensions.

While the course excels at providing end-to-end project workflows, some instructional elements rely heavily on slide-based delivery that may lack visual clarity for complex dataflows. Additionally, certain advanced modules could benefit from more accessible starting queries to facilitate easier following of the development steps.

This trade-off makes sense for professionals like Data or BI Analysts who have a basic grasp of SQL and want a practical, tool-oriented introduction to the analytics engineering workflow.

Score breakdown

Curriculum depth
6.5

The curriculum covers multiple distinct methodologies including Inmon, Kimball, and Data-Vault.

Applied learning
8.0

Includes a significant section on building a dimensional data warehouse and dbt development workflows.

Clarity & experience
5.0

Learner signals are mixed regarding the effectiveness of slide-based instruction and visual aids.

Currency & reliability
5.0

The available evidence does not establish enough about current reliability to move this dimension away from neutral.

Audience fit
6.5

Aligns well with the target of data professionals, provided they have basic SQL knowledge.

More Related Topics

  • DBT Data Modeling37
  • BigQuery Mastery45
  • Data Modeling for AI/ML144
  • DP-600 Exam Prep26
  • Microsoft Fabric114
  • ETL/ELT Data Integration71
  • Data Engineering519
  • Data Modeling113