Wiki

Data Pipeline Project

Plan an end-to-end data pipeline project with ingestion, modeling, orchestration, checks, recovery, and consumer-facing output.

Use an end-to-end data pipeline project to show that you can move data from a source to a trusted output. Capture raw source data, build modeled tables, and show orchestration and quality checks. Make recovery behavior, the consumer, and the supported decision visible too.

This project fits the broader Portfolio Projects and Data Engineering Portfolio Projects paths for a data engineering target role. It also supports analytics engineering or backend data work. Pipeline mechanics connect to Data Pipelines and Orchestration, while operations connect to DataOps and Data Quality and Observability. Use how to build data pipelines when the reviewer needs a step-by-step build order rather than a portfolio-review checklist.

A clear pipeline structure starts with ingestion prep and source handling. It then moves through transformation, modeling, marts, and dashboards that lead back to the people who use the data ^[1].

The modern-stack boundary separates ETL from ELT and treats transformations as their own layer. It also distinguishes marts from warehouses, with raw ingestion guardrails and orchestration around that split ^[2].

The hiring standard is blunt. Many projects list tools but show too little Python and SQL. Professional code quality, tests, and clear structure are what prove readiness ^[3].

Source and Consumer

A useful project starts with one changing source and one consumer. An API or public file drop can work. A permitted scrape, database export, or event sample can work too. The source should expose real behavior such as pagination, late files, or missing fields. Duplicate records and schema drift are useful failure cases.

The consumer might be an analyst or dashboard. It might also be a model table, alert, or operational user.

A public-sector risk-assessment project uses the same pipeline structure for a higher-stakes consumer. The team had to clean and link case-management data with public records and surveys. The team then turned them into features for frontline triage. That makes entity resolution and governance part of the pipeline design. They aren’t cleanup steps after modeling (^{[4] [5]}).

Modeling covers entities, relationships, and business meaning, and dashboards tie marts to user personas (^[1]). Raw ingestion guardrails and orchestration help explain where each tool belongs (^[2]).

For portfolio review, the source and consumer should appear in the README and in the data model. The reviewer should know the update cadence, the expected grain, and the decision the serving table supports. Good examples include “active users this week”, “orders needing follow-up”, “products with stale inventory”, or “events ready for a model training table”.

Raw, Modeled, and Serving Layers

Keep raw data separate from modeled data. Then create staging models and one or two serving tables with explicit grain. With that split, a reviewer can look at the source copy, cleanup logic, and consumer-facing output without guessing where a row changed.

The ETL and ELT vocabulary supplies this split by separating transform-before-load from load-before-transform. It also treats transformations as their own layer and keeps marts separate from warehouses (^[2]). Use ETL vs ELT when the project needs that tradeoff, and use Modern Data Stack for stack boundaries.

The data-modeling standard moves from ingestion prep and transformations into business entities, then relationships, marts, and dashboards (^[1]). For an end-to-end portfolio, show keys and deduplication rules, table grain, and the business mapping behind the serving table.

Tool lists don’t prove readiness when SQL and Python are thin. Readable code, tests, and structure do (^[3]). SQL plus Python come first, and juniors can often postpone Spark, Kafka, and Kubernetes (^[6]).

Orchestration and Reruns

Add a run path outside a notebook. That path can be a CLI command, Docker Compose job, or simple DAG. Use Airflow when the dependencies justify it. A one-command schedule can be enough when there’s only one script and no shared run state. Airflow becomes useful when the project needs visible dependencies, task logs, recovery, or backfills that a reviewer can look at ^[7].

Follow DataTalks.Club’s lightweight local Airflow with Docker Compose tutorial when a local reviewer should look at the Airflow UI. The same walkthrough should show task logs, rerun behavior, and reproducibility through Docker. Keep the DAG thin by calling code that lives outside the scheduler. For example, call a Python module or dbt command instead of hiding transformation code in the DAG ^[8]. A Twitter pipeline capstone combines Docker with a project that can be explained and run ^[9].

One concrete course project moves data from MySQL into MinIO. Spark handles processing and warehouse loading. Kestra or Airflow then makes handoffs and reruns visible rather than implicit. The same learner came from astroinformatics pipelines. That example turns domain research work into portfolio-grade data engineering evidence (^[10], ^[11]).

It’s useful because the storage, processing, and orchestration boundaries are visible. The source database and object storage are separate from the Spark transformation. The warehouse target and scheduler are also separate enough for a reviewer to look at.

A reviewer should be able to run the pipeline and look at a failed task. They should also be able to rerun the job without private instructions. Scheduling sits around the modern stack (^[2]). Use Orchestration for the dependency model and How to Build Data Pipelines for an implementation path.

For Airflow specifically, reviewer evidence should show the DAG graph and a task log. It should also show a failed data check and the rerun command or UI step. A green DAG alone isn’t enough because Airflow can report success even when no records were inserted ^[12].

Quality and Recovery

Add these checks before adding more tools:

• row counts
• null checks
• accepted values
• uniqueness checks
• schema checks
• freshness checks

These checks map to operating risk across freshness and volume as well as distribution, schema, and lineage. Even good pipelines can still deliver bad data (^[13]).

Logs and lineage then matter for root-cause analysis, and ownership and SLAs turn signals into action.

The repository should show at least one failure case, such as a duplicate batch or late partition. A missing field or partial API response also works, as does a duplicate serving-grain row. Use Data Quality and Observability and DataOps for the wider operating context.

The DataOps side covers CI/CD pipelines and regression tests, realistic test data, deployment automation, and data versioning (^[14]). Reviewers get stronger evidence when tests run in CI and the README explains how to recover from a bad load.

Use the README to document common failure cases:

• the API is down
• a file arrives late
• a column is renamed
• a batch partially loads
• a downstream table fails a check

Each case should reference a test, log message, or runbook step. It can also reference a quarantine table, skipped merge, or backfill command.

Stack Boundaries

Prefer batch for a first end-to-end project unless a low-latency decision requires streaming. Slawomir Tulski calls this the real-time myth and warns against overbuilt modern stacks. He frames portfolio work around side projects and end-to-end platforms ^[15].

Use Batch vs Streaming when the project needs the tradeoff. Use Modern Data Stack for stack boundaries and Orchestration for scheduling.

The hiring side draws a similar boundary. SQL and Python stay ahead of large distributed systems for junior candidates. Cloud basics, backend ETL, and testing come before those systems too (^[6]). In the README, say why the project doesn’t use Spark or Kafka. Also say why Kubernetes isn’t needed for the source size, latency, and review goal.

Reviewer Walkthrough

A reviewer should be able to understand the project without a private walkthrough. In the README, name the consumer and the decision the data supports. Also name the source data and the expected update cadence. Show the table grain and setup steps. Include one command to run the pipeline and the checks that can fail the run.

Projects that list tools but show too little Python and SQL fall short. The code should be something another engineer can read, test, and discuss (^[3]). The operating side adds that tests, repeatable delivery, and recovery belong in the project, not only in a diagram (^[14]).

Prepare a short walkthrough in the same order an engineer would use to debug or extend the system:

1. Name the consumer and the decision the pipeline supports.
2. Explain which source behavior made the project realistic.
3. Show where raw records live so the run can be replayed.
4. Name the table grain for staged, modeled, and serving outputs.
5. Show how the pipeline runs without manual notebook clicks.
6. Show the checks that protect the consumer.
7. Explain one bug or tradeoff that changed the design.
8. Name the next improvement without pretending the project is a full platform.

Interview formats include SQL screens, Python problems, and take-home projects (^[3]). This order also matches advice that repeated course projects are weaker than custom projects. Custom work is stronger when the candidate can explain the data and the choices behind the work (^[9]).