Best practices for data engineering

Every day, over 400 million terabytes of data are generated across digital platforms, powering everything from mobile apps and e-commerce to internal dashboards and reporting systems. But raw data, by itself, isn’t immediately useful. It needs to be cleaned, structured, and shaped into something teams can work with.

That’s where data engineering comes in.

By designing systems that collect and transform scattered inputs, data engineering helps teams produce trusted, analysis-ready datasets. These systems ensure that the data is consistent, accurate, and easily accessible, making them a core foundation for analytics, machine learning, and day-to-day operations.

Let’s examine the practices that experienced data engineers rely on to build scalable, resilient, and easy-to-maintain pipelines as data and business needs evolve.

What is data engineering and why does it matter?

A predictive model is only as good as its input data, and teams can't make smart decisions if dashboards show conflicting or incomplete information.

Data engineering is the behind-the-scenes work that shapes raw data into something teams can trust. This means designing systems that collect, process, and organize information so it’s ready to power everything from daily reports to machine learning models.

Unlike data scientists (who develop models), data analysts (who examine trends), and DevOps engineers (who manage software infrastructures), data engineers focus on creating and maintaining data pipelines that deliver reliable, relevant, and up-to-date information.

This involves:

• Pulling data from APIs, event streams, and internal databases
• Validating and cleaning inputs to remove errors or inconsistencies
• Storing data in platforms built for scale and speed
• Creating access layers so data can flow easily into tools and workflows

When done well, data engineering turns scattered inputs into a single source of truth, ready for whatever the business needs next.

Why data engineering is critical in modern business

Each day, clicks, swipes, transactions, and sensor readings generate massive amounts of raw data. Without structure, that information is difficult to use. Data engineers step in to organize and build pipelines that move and transform data into a format that teams can actually work with.

Their work supports everything from real-time dashboards to machine learning models. As data volumes increase, they help ensure the systems behind them scale smoothly and deliver reliable results.

The cost of poor data engineering

When data engineering breaks down, everything downstream is affected.The impact is hard to miss:

• Teams end up with disconnected, overlapping datasets
• Confidence in reporting and analytics drops
• Pipelines fail more often and take longer to fix
• Decisions slow down as teams question what data to trust
• Projects miss deadlines or go unused
• Engineers spend more time patching problems than improving the system

Top strategies for modern data engineering

Building reliable pipelines starts with clear priorities and creating systems that deliver accurate, trusted data. This requires you to develop workflows that are easy to scale, simple to maintain, and flexible enough to handle change. Here’s how you can do this:

Choose the right tech stack

A modern data stack typically spans four key layers. Understanding what each layer does and what to consider when selecting tools can help you make better choices from the start. Here's a quick breakdown:

Tip: If you're just starting out, prioritize tools that are easy to learn and quick to implement.

Assess the business need

Before writing a single line of code, ask questions like:

• What is the data meant to accomplish?
• Who’s going to use it?
• How often does it need to be updated?
• What decisions will it influence?

Analysts, product managers, and business leads often have the clearest view of how data is used on a day-to-day basis. Their input can help clarify the pipeline’s goals, identify where flexibility is acceptable, and establish how success should be measured. Using a method such as the Analytics Development Lifecycle (ADLC) ensures that this indispensable stakeholder input is integrated into data engineering from day one.

It’s also important to agree on which metrics are most valuable to your team. For some, that may mean tracking daily revenue or user growth. For others, it could be product usage or retention. Aligning early helps ensure all the stakeholders are working toward the same outcomes.

Design modular and scalable data pipelines

Designing pipelines around smaller, focused tasks makes it easier to manage growing data volume and changing business requirements. Each task can be tested and maintained independently, which helps teams troubleshoot issues more quickly and improve system performance over time.

Instead of repeating logic across different models, many teams use SQL snippets, helper functions, or shared dbt macros. This maintains development consistency while reducing unnecessary rework.

Another key consideration is how data moves through your system. Some workflows require real-time updates, while others only need data to be refreshed on a schedule. The choice often comes down to what the data supports and how quickly it needs to be available.

The table below outlines how batch and streaming options compare:

Once you've chosen a processing method, reliability comes next. Pipelines should run multiple times without causing duplicates or corruption. Use stable identifiers like primary keys to deduplicate records. Most importantly, track each run using job states or audit tables so you know exactly what’s been processed and where issues might occur.

Automate with CI/CD for data pipelines

CI/CD (Continuous Integration and Continuous Delivery) provides a reliable way to test, validate, and deploy changes automatically. It provides teams with the structure to identify issues early, before they reach users or disrupt key workflows.

Automating deployment also saves time and reduces manual effort. Once new code is committed, the CI pipeline can handle the rest: running tests, checking for schema changes, and promoting updates through development, staging, and production.

Version your data and transformations

As your data pipelines evolve, version control plays a critical role in ensuring consistency, auditability, and collaboration across teams.

Start by storing all transformation logic, configuration, and scheduling metadata in Git. This gives you a clear history of what changed, when, and why. It also allows safe rollbacks when needed.

But versioning shouldn't stop at code. Your raw data, intermediate outputs, and model artefacts can (and should) be versioned too, especially when accuracy and reproducibility matter.

Lastly, use consistent naming, commit messages, and tagging for both code and data assets. Wherever possible, version datasets and logic together to keep them in sync.

Use reliable orchestration and scheduling

Orchestration plays a key role in managing when and how pipeline tasks run. It keeps processes on schedule, enforces the right order of operations, and helps recover from failures.

Pick the right orchestrator

Choosing the right orchestration tool depends on the size of your workflows and your team’s experience. Look for a system that makes it easy to build modular pipelines, track task progress, and connect smoothly with the rest of your data stack. You can use tools like Apache Airflow, Prefect, and dbt.

Some teams also build in-house orchestration tools tailored to their specific infrastructure and workflow needs. The downside is that these can be time-consuming to maintain and scale.

Handle dependencies and failures

Build pipelines that recover automatically. Set up retries to automatically handle temporary issues and configure alerts to notify teams of critical failures. Triggers or sensors can help track when a file arrives or an upstream job finishes, so the pipeline knows when to move forward. Keep tasks modular so failures can be isolated and resolved without disrupting the entire pipeline.

Build robust data models

Reliable analytics starts with a solid data model. Without a clear structure, even the most advanced tools can produce slow queries, inconsistent results, and user confusion.

The table below describes approaches that can fit your needs:

Monitor, log, and alert effectively

Active monitoring helps catch failures early and ensures pipelines stay reliable as they scale.

Set up observability

Track key metrics such as run time, error counts, throughput, data freshness, and missed SLAs. These signals reveal bottlenecks and help prevent silent failures.

Centralized logging

Aggregate logs in one place and include relevant metadata, such as task IDs or timestamps. This makes it easier to trace issues and debug quickly.

Use alerts strategically

Define alert thresholds for critical issues like missing data, schema drift, or job failures. Route alerts to the relevant teams with sufficient context to enable prompt action.

Data processing and transformation

Data is just noise without structure and business logic. Transformation is what gives it meaning, making it ready for reporting, automation, and operational use.

Transformation usually comes down to two options:

• ETL (Extract, Transform, and Load)
• ELT (Extract, Load, and Transform)

They sound similar, but the difference in approach can have a big impact on performance, flexibility, and overall pipeline design.

Here’s a quick side-by-side to help make the distinction clear:

Secure data and maintain compliance

Keeping data secure is critical as it moves through your pipeline. A combination of access control, encryption, and regulatory safeguards helps minimize risk and protect both users and the business.

Role-based access control (RBAC) restricts what each user can see or modify based on their responsibilities. Separating permissions across environments adds an extra layer of protection and makes auditing more straightforward.

Encryption protects sensitive data both at rest and in transit. To maintain its effectiveness, encryption keys should be rotated regularly and stored securely, ideally in a secrets manager instead of being hardcoded in scripts.

Many teams also need to meet legal and regulatory requirements. Depending on your industry and region, this may include the GDPR, HIPAA, and CCPA. Techniques such as masking, pseudonymization, and consent tracking help support these standards and mitigate risk throughout the pipeline.

Document everything

Focus your documentation on the details that help teams work efficiently and avoid confusion:

• Explain each pipeline step so anyone can understand how data is processed.
• List data sources and outputs to show where data comes from and where it is delivered.
• Keep documentation up to date whenever code or logic changes.
• Use data catalogs to show lineage and clarify which team owns each dataset.
• Add metadata to support cross-team collaboration and help non-experts understand the data.

Create shared channels and review routines to enable teams to raise issues, ask questions, and stay aligned.

Elevate your data engineering with dbt

With a strong foundation like modular pipelines, tested transformations, and clear documentation in place, your next step is making these practices scalable and repeatable. That requires a system that can act as a single data control plane for all the data across your organization.

dbt helps teams manage transformations with the same discipline used in software development. Using dbt, you can:

• Collaborate across distributed teams to create data transformation models in SQL and Python in a flexible, cross-platform fashion
• Author tests to verify data transformation logic and documentation to help data consumers understand and use the data
• Ship changes safely to production with an automated CI/CD process
• Enable stakeholders to find and understand data quickly

One of the easiest ways to begin is by modeling a single domain, writing basic tests, and tying those changes into a continuous integration workflow. As these practices take hold, they can gradually be applied to other parts of the stack. Over time, this approach helps improve pipeline quality while reducing the time spent fixing issues after deployment.

Get started with dbt and turn well-structured pipelines into reliable, production-grade systems.