Overview

This course provides a foundational understanding of analytics engineering, focusing on building end-to-end data solutions. It teaches essential skills for designing, implementing, and optimizing analytics systems, from data ingestion to visualization. The course will enhance your ability to apply best practices in data modeling, cloud analytics, and collaborative workflows, all while building scalable and reliable data platforms. With the guidance of industry experts, learners will gain the knowledge to transition from data analysis to analytics engineering. What makes this course different is its integration of theory with real-world applications. You’ll not only learn the fundamental concepts but also apply them using modern data practices like cloud-based analytics and collaborative workflows. The course takes a comprehensive approach, breaking down complex topics into digestible lessons, making it a powerful tool for data professionals aiming to broaden their skill set. Ideal for data engineers and analysts seeking to enhance their careers in analytics engineering, this course provides an essential foundation. It assumes basic knowledge of data analysis and ETL processes, helping professionals transition smoothly into more advanced analytics roles.

Syllabus

What Is Analytics Engineering

In this section, we explore the role of analytics engineers in modern data strategies, comparing ETL and ELT workflows and clarifying their distinct responsibilities from data analysts and engineers.

The Modern Data Stack

In this section, we explore the Modern Data Stack (MDS), its key differentiators from legacy systems, and its advantages and limitations for data management.

Data Ingestion

In this section, we will learn about data ingestion pipelines, including eight essential steps and scalable solutions.

Data Warehousing

In this section, we examine the evolution of data warehousing, cloud migration, and key components of cloud data warehouses, focusing on practical applications and market leaders.

Data Modeling

In this section, we examine data modeling techniques, focusing on normalization, dimensional modeling, and data vault approaches to ensure data integrity, scalability, and efficient system design.

Transforming Data

In this section, we explore data transformation techniques, scalable data modeling, and best practices to enhance data usability and support analytics.

Serving Data

In this section, we explore how data becomes useful through dashboarding, BI tools, and data products, emphasizing accessibility, trust, and actionable insights for effective decision-making.

Hands-On Analytics Engineering

In this section, we explore practical analytics engineering by implementing ELT workflows with Airbyte Cloud, modeling data using dbt Cloud, and visualizing insights with Tableau to solve real-world business problems.

Data Quality and Observability

In this section, we explore data quality challenges in source systems, pipelines, and governance, and introduce observability, data catalogs, and semantic layers as solutions to ensure reliable and consistent data.

Writing Code in a Team

In this section, we explore version control, coding standards, and code reviews to enhance team collaboration and ensure code quality and maintainability in shared environments.

Automating Workflows

In this section, we will learn about data orchestration, CI/CD pipelines, and their implementation in dbt Cloud.

Driving Business Adoption

In this section, we explore translating business needs into analytics, scoping use cases with stakeholders, and ensuring adoption through incremental delivery and communication.

Data Governance

In this section, we will learn about data governance, ownership, quality, and its role in data management.

Epilogue

In this section, we review core analytics insights and explore strategies for career growth, emphasizing continuous learning, networking, and portfolio development for long-term success.