Glossary Entry

What is Data Engineering Lifecycle?

The data engineering lifecycle is a framework of five stages data moves through: generation, ingestion, storage, transformation, and serving to BI and apps.

Written by Alex Merced

What is Data Engineering Lifecycle?

Introduction to the Data Engineering Lifecycle

Data Engineering is often misunderstood simply as “writing SQL scripts” or “managing databases.” In reality, Data Engineering is the discipline of designing and operating systems that move data from its messy origins into a clean, reliable state where it can be used to generate business value.

This process is described by the Data Engineering Lifecycle, a framework (popularized by Joe Reis and Matt Housley’s book Fundamentals of Data Engineering) that maps the journey of data through five phases.

Phase 1: Generation

Data does not simply appear in a database; it is generated by source systems.

  • Operational Systems: A user clicking “Buy” on a website generates a transaction in a PostgreSQL database.
  • IoT Devices: A sensor on a manufacturing robot generates a temperature reading 1,000 times a second.
  • Third-Party APIs: A marketing platform generates a daily CSV report of ad spend.
  • The Engineer’s Role: Understanding the source. The engineer must know whether heavy queries will overload the source database, and whether the data is generated in continuous streams or daily batches.

Phase 2: Ingestion

Once the data is generated, it must be extracted from the source and moved into the data ecosystem (the Data Lake or Warehouse).

  • Batch Ingestion: The engineer writes a script (using tools like Airbyte or Fivetran) that wakes up at 2:00 AM, connects to the Salesforce API, downloads the last 24 hours of data, and dumps it into Amazon S3.
  • Streaming Ingestion: The engineer sets up Apache Kafka. Every time a user clicks a button on the website, a JSON message is published to Kafka, which streams it continuously into the Data Lakehouse in near real time.

Phase 3: Storage

The ingested data needs a home.

  • The Data Lake: The raw, untouched data (JSON, CSV, Parquet) lands in Cloud Object Storage (S3, ADLS, GCS). This is the “Bronze” layer of the Medallion Architecture.
  • The Architecture: The engineer must choose the correct storage formats (Apache Parquet) and the correct table architectures (Apache Iceberg) to keep the data efficient for future analytical queries.

Phase 4: Transformation

Data from Phase 2 is usually messy. It has missing values, the dates are in the wrong format (EU vs. US), and it is scattered across dozens of different tables.

  • The Workhorse: This is where the majority of Data Engineering happens. Using tools like Apache Spark or dbt (data build tool), the engineer cleans, filters, and aggregates the data.
  • The Goal: They join the “Users” table with the “Purchases” table, calculate the “Lifetime Customer Value,” and save this structured, clean data into a new “Gold” table.

Phase 5: Serving

Clean data has little value if the business cannot access it. The final phase is delivering the data to the end-user.

  • Business Intelligence (BI): The engineer connects the Gold tables to a dashboarding tool like Tableau, PowerBI, or Superset, so the CEO can view the daily revenue charts.
  • Machine Learning: The engineer serves the clean data to the Data Science team to train a predictive AI model.
  • Reverse ETL: The engineer pushes the calculated “Lifetime Customer Value” metric out of the Data Lakehouse and back into the operational Salesforce database, so the sales team can see it directly in their CRM.

Conclusion

The Data Engineering Lifecycle is the blueprint for the modern data stack. By understanding that data must be carefully guided through generation, ingestion, storage, transformation, and serving, architects can build resilient, modular pipelines where a failure in one phase (e.g., a broken BI dashboard) does not damage the storage or ingestion layers.

Learn More

To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.

Related Terms

What is the Medallion Architecture?

The medallion architecture is a design pattern that refines lakehouse data through Bronze, Silver, and Gold tiers, built on table formats like Apache Iceberg.

What is Reverse ETL?

Reverse ETL copies modeled data from a warehouse or lakehouse back into operational tools like CRMs and ad platforms, so teams act on metrics analysts built.

What is Data Engineering?

Data engineering is the discipline of building the pipelines and infrastructure that ingest, transform, and store data, and how it differs from data science.

Fivetran for the lakehouse: loading Iceberg tables, and Fivetran vs Airbyte

Fivetran is a managed ELT service that copies data from SaaS apps and databases into warehouses and lakes. See how it loads Iceberg and compares to Airbyte.

What is Streaming Ingestion?

Streaming ingestion loads data continuously as it is generated, via a broker like Kafka and a processor like Flink, into sinks such as Apache Iceberg tables.

Airbyte for the lakehouse: open-source ingestion into Iceberg, and Airbyte vs Fivetran

Airbyte is an open-source data integration platform that moves data from APIs and databases into warehouses and lakes. See how it writes Iceberg vs Fivetran.

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