I’m going to tackle a question I get all the time: “What is data engineering?” Many people wonder why it’s important, why we need it, and what it actually involves. In this post, I’ll answer these questions and delve into some key concepts and common tools used by data engineers.

Understanding Data Engineering

Data engineering is a specialized field within data science focused on designing, building, and maintaining the systems and infrastructure that allow for the efficient collection, storage, processing, and analysis of large volumes of data. Let’s break this down step by step, using a simple architecture diagram for an e-commerce company to illustrate how these concepts and tools come into play.

Key Components of Data Engineering

1. Source Systems

Data engineering often starts with source systems—the origins of raw data. These systems can be databases, CRM tools like Salesforce, or any other software that captures and stores data. Although managing these systems doesn’t typically fall under the core responsibilities of a data engineer, you might often need to work closely with system administrators. This could involve adding fields in a CRM or creating new tables in a database to capture important events.

Tip: Building good relationships and aligning with stakeholders is crucial at this stage. Understanding their goals helps you design data systems that meet their needs.

2. Data Ingestion

Next is data ingestion, where data is transferred from source systems to a storage solution. A common practice is to first store raw data in an object store like Google Cloud Storage (GCS) or Amazon S3. This is beneficial for several reasons:

• Backup and Archiving: Having a copy of the raw data means you can always revert to the original if needed.
• Schema Evolution: If data formats change over time, having the raw data allows you to adapt without losing information.

For automated data movement, tools like Fivetran offer point-and-click simplicity. However, if you prefer a more hands-on approach and are comfortable with Python, DLT Hub is a fantastic tool for replicating data from various sources to your destination.

3. Data Warehousing

Once ingested, data often lands in a data warehouse—a specialized type of database optimized for analytical workloads rather than transactional operations. These warehouses enable efficient querying and analysis of large datasets. Popular cloud-based options include:

• BigQuery (Google Cloud)
• Redshift (AWS)
• Snowflake (available across multiple clouds)

For beginners, BigQuery is a great starting point due to its ease of use and accessibility.

4. Data Transformation

To make data useful for analysis, it needs to be transformed into a structured format. This is where data modeling and tools like dbt (data build tool) come into play. In data modeling, you often organize data into a star schema, which centralizes core facts (like sales orders) and connects them to descriptive dimensions (like customer details or time).

dbt is particularly helpful for SQL-based transformations and integrates seamlessly with major data warehouses. It also supports testing, which ensures your data models are accurate and reliable.

5. Testing and Monitoring

Robust testing and monitoring are essential throughout the data lifecycle. This involves setting up alerts for:

• Failed Jobs: Notification if data replication or ingestion jobs fail.
• Data Quality Issues: Alerts for out-of-bounds values or excessive duplicates.

Effective monitoring prevents data issues from reaching end reports, maintaining trust in your data systems.

6. Data Consumption

After data is cleaned, transformed, and tested, it’s ready for consumption. This can take various forms:

• BI Tools: Platforms like Looker or Tableau for interactive dashboards.
• Exports: Simple CSV files or charts shared via email.
• Machine Learning: Preprocessed data used for building predictive models.

Meeting stakeholders’ needs might involve using sophisticated BI tools or simply providing data in the format they prefer.

Advanced Topics: Reverse ETL and Data Plumbing

Sometimes, data engineers need to push processed data back into source systems, a process known as reverse ETL. This allows operational teams to use insights directly within their usual tools. Whether moving data from a data warehouse to a CRM or integrating machine learning outputs into business systems, reverse ETL keeps data actionable.

Data engineers are often referred to as “data plumbers” because a large part of the job involves moving data between systems and ensuring everything flows smoothly. It’s about making the right connections and ensuring that data arrives where it’s needed, clean and ready to use.

Final Thoughts

Data engineering is fundamental to modern data science and analytics. It provides the infrastructure that enables organizations to harness their data effectively. By understanding and leveraging the right tools and practices, data engineers ensure that data is accessible, reliable, and actionable.

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Useful Resources:

• Fivetran
• DLT Hub
• dbt