When Netflix processes over a trillion events a day without a hiccup, that’s not luck – that’s exceptional data engineering.

For many organizations racing to scale their data operations, the difference between success and failure often comes down to following proven data engineering best practices.

From fintech startups processing millions of transactions to healthcare providers managing patient data, robust data engineering has never been more necessary. 

Building an effective data engineering practice requires careful attention to infrastructure, processes, and people.

While the challenge of managing complex data pipelines may seem overwhelming in the beginning, the right set of best practices can transform this complexity into a competitive advantage. 

What Is Data Engineering?

Data engineering is the practice of designing systems to transform raw data into actionable information. This involves building and maintaining data infrastructure to ensure consistent, reliable data flow within an organization.

To put it simply, think of it as building the highways that connect your data sources to the data scientists and analysts who need them, complete with traffic rules and safety systems in place. Data engineering emerged when organizations realized that managing data at scale required expertise beyond traditional database administration. 

Data engineering can be broken down like this: 

• Data Collection: Gather data from different sources efficiently.
• Data Storage: Organize data in databases or data warehouses, ensuring it’s stored safely and can be retrieved quickly.
• Data Quality: Implement data validation and quality checks to maintain data integrity.
• Data Pipelines: Develop reliable data pipelines that automate the flow of data from source to destination.

How Data Engineering Has Changed In 2025

Since its birth, the data engineering landscape has transformed dramatically, driven by several key shifts, which will continue into 2025 and beyond. 

AI And ML Integration

Artificial Intelligence (AI) and Machine Learning (ML) have moved from buzzwords to essential components of modern, automated data pipelines. According to a McKinsey report, 72% of organizations now integrate AI/ML capabilities into their data engineering workflows, marking a 40% increase from 2023. 

Cloud-Native Evolution

The tools powering data engineering have evolved significantly. For example: 

• Databricks and Snowflake have introduced advanced serverless computing capabilities
• Apache Spark has enhanced its streaming capabilities for real-time processing
• dbt has expanded its testing framework to include AI-powered data quality checks

Regulatory Landscape

With the introduction of AI-specific regulations in major markets, data engineering practices now require stronger governance frameworks. The EU’s AI Act and similar regulations have pushed companies to implement more rigorous data quality and lineage tracking systems. Keeping data management practices up-to-date with these regulations is critical for building trustworthy data storehouses.

2025 will continue to bring even more changes to the ever-shifting practice of data engineering, making it an exciting time for organizations that plan to increasingly rely on tools that make their data infrastructure run smoothly. Here’s a quick recap of the impact on AI and ML integration, cloud-native evolution, and the constantly changing regulatory landscape.

Data Engineering Best Practices

Implementing effective data engineering best practices isn’t just about following a checklist. It’s about creating a foundation that supports your organization’s data needs today and tomorrow. 

Recent data from IDC shows that organizations with mature data engineering practices are 2.5x more likely to meet and exceed business goals. With that in mind, the following are some key practices that define successful data engineering implementations. 

Build For Scale From Day One

Starting small doesn’t mean thinking small. Design data architectures that can handle 100x current volume without major rewrites. This means choosing technologies with proven scaling capabilities and implementing modular designs that allow for easy expansion. 

Here’s a real-world example: A huge security provider scaled from processing 1 million to 100 million daily events without architectural changes by using a modular data lake design (flexible architecture) from the start. When it comes to data engineering processes, begin with the future in mind. 

Implement Automated Testing At Every Layer

Data quality should never be an afterthought but a foundation. Did you know data quality issues cost organizations an average of $12.8 million annually? This makes automated testing a crucial investment in data reliability. Implementing comprehensive testing at every stage of your data pipeline creates multiple safety nets that catch issues before they impact business decisions. 

Today’s data engineering teams are adopting practices like data contracts, schema evolution testing, and automated anomaly detections to build robust software quality assurance plans into their pipelines. 

Embrace Infrastructure As Code (IaC)

Infrastructure as Code (IaC) has become the backbone of scalable data engineering, with 73% of high-performing teams citing IaC as critical to their success. 

Using tools like Terraform or CloudFormation transforms complex data engineering tasks from manual processes into automated, version-controlled operations. This approach not only reduces deployment time, but also significantly improves reliability through consistent, repeatable infrastructure deployments. 

Prioritize Data Governance Early

Modern data governance goes beyond basic compliance. It’s about creating a clear framework for data quality, access, and lineage that scales with your organization. Successful teams use automated governance tools that track data usage, enforce access controls, and maintain comprehensive data catalogs without creating bottlenecks. 

Taking a proactive approach helps to prevent issues that can come up later due to poor data quality or access mismanagement. Take the following into consideration when prioritizing data governance: 

• Establish clear data ownership by assigning specific roles to individuals or teams for each dataset, in turn creating accountability for better data stewardship.
• Create and implement data management policies and standards that include data entry, usage, sharing, and storage protocols. 
• Incorporate a robust metadata management strategy, which allows teams to gain insights into data lineage and helps in understanding how data flows through various pipelines. This aids in compliance and auditing. 
• Automate data quality checks by leveraging automated tools that continuously monitor data quality and integrity.

Design For Observability

Data engineering today requires more than basic monitoring. It demands real-time insights into pipeline health, performance metrics, and data quality indicators. Using tools that provide end-to-end visibility helps teams catch issues early and optimize performance proactively rather than reactively. 

In fact, thanks to comprehensive observability practices, leading organizations spend less time troubleshooting data issues. Less time troubleshooting = more time spent on other, more important, tasks. 

Implement CI/CD For Data Pipelines

Did you know – Organizations using Continuous Integration (CI) and Continuous Deployment (CD) for data pipelines report 70% faster deployment times and 60% fewer production issues. Modern data pipeline development demands the same rigorous practices used in software engineering: automated testing at every stage, version-controlled code repositories, and modular design patterns that enable independent deployment of components. 

DevOps engineer teams that excel at CI/CD treat their data pipelines as first-class software artifacts, complete with comprehensive testing suites and containerized deployments. Furthermore, they’re leveraging containerization tools – like Docker – for consistent deployments, implementing comprehensive monitoring solutions for real-time pipeline health checks, and maintaining detailed documentation of their CI/CD processes. 

Focus on Documentation And Knowledge Sharing

Teams with strong documentation practices resolve incidents faster and onboard new team members more quickly. Creating comprehensive, living documentation doesn’t mean just writing things down. You have to build a knowledge base that evolves with your data infrastructure. 

This includes maintaining current architecture diagrams, detailed pipeline documentations, and clear runbooks for common scenarios. Make documentation a core part of your development process, treating it as essential infrastructure instead of an afterthought. 

Optimize For Cost And Performance

Smart cost optimization starts with understanding your data patterns and using appropriate storage tiers. For example, hot data stays readily accessible while cold data moves to lower-cost storage options. When left unoptimized, data processing costs can consume up to 30% of cloud infrastructure budgets. 

That’s why it’s important to regularly analyze query patterns to identify optimization opportunities. This can be done by using techniques like partitioning, clustering, and materialized views to improve performance while reducing compute costs. Establish clear performance KPIs and cost thresholds, which enables teams to maintain the right balance between performance and cost-efficiency. 

Build For Recovery, Not Just Reliability

Companies with tested recovery plans significantly reduce their average incident resolution time. Building resilient data systems means assuming failures will occur and, therefore, preparing accordingly. This is where using automated backup systems, maintaining detailed recovery procedures, and regularly testing your ability to restore operations under various failure scenarios come into play. 

Big Takeaway

The success of data engineering initiatives often comes down to having the right expertise on hand. While tools and technologies evolve rapidly, the fundamentals of good data engineering remain the same: scalability, reliability, and maintainability. 

Building a strong data engineering practice requires both technical expertise and strategic vision. For many organizations, the challenge isn’t just about implementing these best practices. It’s about finding the right talent to drive these initiatives forward since data analytics have become increasingly central to business success.

Looking to build or strengthen your data engineering team? The pool of data engineering talent is complex, but you don’t have to navigate it alone.

‍Connect with MSH’s data engineering specialists to discuss how to find the right expertise for your organization’s unique needs.