The 3 Pillars of Data Engineering

Summary

In this video, the speaker highlights the three key pillars of data engineering: 1) Identifying source systems and integrations, 2) Understanding the data warehouse as the central hub of the architecture, and 3) Figuring out how insights are derived to drive business decisions. These pillars help data engineers focus on the most important aspects of their work, such as organizing data sources, managing the data warehouse, and enabling effective insights for stakeholders. By mastering these components, data engineers can navigate complex architectures and improve decision-making processes within a company.

Takeaways

• 😀 **Identify Data Sources First**: The first step in any data engineering project is identifying the source systems (internal apps, third-party platforms, custom scripts) that provide the data.
• 😀 **Source Integrations Matter**: Understanding how data is captured and integrated (via tools like Fivetran, Airbyte, or custom scripts) is crucial for organizing the data pipeline.
• 😀 **Data Warehouse as the Central Hub**: The data warehouse (e.g., Snowflake, BigQuery, Redshift) serves as the core of the data architecture, storing and organizing the data for analysis.
• 😀 **Organize Before Analyzing**: A data warehouse is not just for storage; it’s where data organization and transformation logic occur, making it vital for efficient data analysis.
• 😀 **Understand the Data Movement**: Once you identify the data warehouse, track how data flows in and out of it, including any ETL/ELT tools (e.g., DBT, custom scripts).
• 😀 **Reporting Tools for Insight Generation**: Tools like Tableau, Power BI, and Looker are commonly used to generate insights from the data stored in the warehouse.
• 😀 **The End Goal is Insights**: The ultimate aim of data engineering is not just managing data but enabling business teams to derive actionable insights for better decision-making.
• 😀 **Know Your Stakeholders**: It's essential to understand how different teams or stakeholders are using the data, whether via BI tools or custom reporting systems.
• 😀 **Data Engineering Bridges Gaps**: Data engineers play a crucial role in integrating disparate systems and ensuring the infrastructure is in place for stakeholders to access and analyze data.
• 😀 **Focus on the Three Pillars**: Every data engineering project revolves around three main components: identifying sources, organizing data in a warehouse, and enabling insights generation.
• 😀 **Embrace Complexity but Simplify Your Focus**: Data engineering can feel overwhelming, but focusing on these three core pillars will help you make sense of any architecture and streamline your efforts.

Q & A

What is one of the biggest challenges of being a data engineer?

One of the biggest challenges is being placed in the middle of a larger architecture with numerous components. It can be overwhelming to figure out what is important and what to focus on within the complexity of the system.

What are the three pillars of data engineering discussed in the video?

The three pillars of data engineering are: 1) Identifying source systems and integrations, 2) Understanding the data warehouse as the central hub, and 3) Figuring out how the business or team derives insights from the data.

Why is identifying source systems important for data engineers?

Identifying source systems is crucial because data engineers need to understand where the data comes from. This could involve internal applications, third-party platforms, or custom integrations, and knowing how these sources are captured helps in structuring and processing the data effectively.

What role does the data warehouse play in data engineering?

The data warehouse serves as the central hub for storing and organizing the data. Once sources are identified, data engineers focus on how the data is structured, transformed, and stored within the warehouse (e.g., Snowflake, BigQuery, Redshift), making it accessible for further analysis.

What is the significance of the data warehouse in the overall architecture?

The data warehouse is the core of the data architecture. It houses all the organized data and serves as the foundation for connecting other tools like DBT (for transformation) and reporting tools (e.g., Tableau, Power BI) that allow business users to derive insights from the data.

Why is it important for data engineers to understand how insights are derived from data?

Understanding how business stakeholders derive insights from data is important because the end goal of data engineering is to support decision-making. Whether through reporting tools, dashboards, or other means, data engineers need to ensure the system is built to deliver useful insights.

What are some common tools used in data engineering that data engineers should be familiar with?

Common tools include Fivetran and Airbyte for data integration, Snowflake, BigQuery, or Redshift for data warehousing, DBT for data transformation, and Tableau, Power BI, or Looker for business intelligence and reporting.

How does the data warehouse relate to reporting and business intelligence tools?

The data warehouse acts as the central repository where data is stored and organized. Reporting tools like Tableau or Power BI connect to the data warehouse to visualize and analyze the data, helping business users derive actionable insights.

What should a data engineer do first when joining a new team or project?

The first thing a data engineer should do is identify the data warehouse. This helps them understand the central hub of the architecture, from which they can trace how data is integrated, transformed, and utilized for business insights.

Why is it important to keep the end goal of insights in mind as a data engineer?

It’s important because the ultimate purpose of all the data engineering work—such as source integration, data organization, and transformation—is to support the business in deriving actionable insights that guide decision-making and strategy.