Data Engineering Best Practices Guideline 2025

Data engineering involves designing and building systems for collecting, storing, and analyzing data at scale. Following best practices ensures efficient, reliable, and scalable systems for handling data workflows. Here are some key data engineering best practices:

Design Efficient Data Architecture

Define a clear data architecture, start with a solid data model (e.g., star schema, snowflake schema) that reflects business requirements. This helps in efficient querying, storage, and data management.

Use a data warehouse for structured data (OLAP) and reporting, and a data lake for unstructured and semi-structured data. Consider adopting a Lakehouse architecture that combines both.

Use different layers/schemas for raw, curated, and processed data to separate concerns and ensure that each layer serves a distinct purpose.

ETL/ELT Pipelines

· Use ELT for Scalability, load raw data into a centralized location (like a data lake) first, and then transform it.

· Automate Pipelines, use orchestration tools like Apache Airflow, Azure Data Factory, or AWS Glue to automate ETL/ELT workflows, ensuring reliability and repeatability.

Data Quality and Logging

· Implement data validation checks at multiple stages of the pipeline to catch errors early.

· Track data lineage to understand the flow of data, from its source to transformation and finally consumption, add the logging on different stages of the flow. This helps with auditing and debugging.

Robust Error Handling

· Data pipelines can be complex and may involve multiple steps and dependencies. It is important to implement robust error handling and recovery mechanisms to ensure that the pipeline can continue running smoothly in the event of failures or errors and should send email alerts for failures to respective audience.

Scalability and Performance

· Data pipelines should be designed to handle large volumes of data and perform efficiently, even as data volumes grow over time.

· Use frameworks like Apache Spark for parallel and distributed data processing, especially for large datasets.

· Implement caching for frequently accessed datasets to reduce the load on your storage and processing systems.

· Scalability should ensure cost efficiency and maintain performance as data grows, enabling systems to adapt to the organization’s evolving data needs.

Ensure Data Security and Privacy

• Encrypt data where required especially for sensitive or personal information.
• Implement role-based access control (RBAC) to limit access to data and tools only to authorized users.
• Ensure compliance with data protection regulations (e.g., GDPR, HIPAA) by anonymizing or masking sensitive data.

Incremental Processing

• Instead of processing full datasets repeatedly, implement incremental data loading to only process new or changed data. This increases efficiency and reduces compute costs.
• Use CDC techniques to capture changes in source data in real-time or batch, making the ETL process faster and more efficient.

Documentation and Collaboration

• Maintain clear, comprehensive documentation of your data pipelines, architecture, and processes. This ensures that other team members can understand and work on the system when needed.
• Use version control (e.g., Git) and collaborative platforms for team communication, issue tracking, and code reviews.
• No merge/pull request can pass without approval, all changes should be reviewed and approved before being merged.

Do’s and Don’ts

Do’s/To be aware

The following few points may be related to DevOps, but a Data Engineer should be aware of them and highlight them to the team.

Implement Role-Based Access Control (RBAC)

• Ensure each user has the least privileges necessary to perform their role

Use Secure VPN/VPC Security

• Secure all data and traffic through VPNs or VPCs (Virtual Private Clouds) to protect data transfer between services.

Use Private IPs Over Public IPs

• Restrict access to critical resources by using private IPs wherever possible. This reduces exposure to potential threats from the internet.

Implement Geo-Location/Region-Level Access Control

• Restrict access based on geographical locations or regions to meet regulatory requirements (e.g., GDPR compliance) and improve data security.

Implement Data Retention Policies

• Clearly define and implement data retention and deletion policies to manage data lifecycle and reduce storage costs.

Regularly Rotate Credentials

• Ensure passwords, access keys, and certificates are rotated regularly to minimize the risk of unauthorized access due to exposed or compromised credentials.

Implement Multi-Factor Authentication (MFA)

• Use MFA for accessing sensitive systems to enhance security and prevent unauthorized access, even if credentials are stolen.

Encrypt Sensitive Data

• Use encryption standards such as AES-256 for data against unauthorized access.

Set Up Alerts for Data Storage Usage and Cost

• Implement automated alerts to monitor data storage usage and cost. This will help you avoid unexpected charges and manage resource usage efficiently.
• Use cloud provider tools like AWS CloudWatch, Azure Monitor, or Google Cloud Monitoring to track storage consumption and cost thresholds.

Send Notifications for Job Failures

• Configure automated message alerts for job failures or critical issues. This ensures prompt attention and quick resolution of problems.

Don’t:

Don’t Open Ports for All Users

• Never open database or system ports to all users. Use strict firewall rules and limit IP ranges that can access the system to reduce security risks.

Change default Passwords for Databases

• Change all default usernames and passwords immediately after setting up services. Default credentials are often easy targets for attackers.

Don’t Deny Regional Data Privacy Regulations

• Always comply with geo-location regulations like GDPR, HIPAA, and CCPA. Failing to do so can lead to legal issues and penalties.

Don’t Allow Global Access to Resources

• Deny global access by default. Ensure that access is restricted to only the necessary users and regions

Don’t Bypass Data Validation

• Never skip data validation steps, as it can lead to corrupt or inaccurate data entering your systems. Also verify the schema types with source data.

Don’t Hardcode Secrets in Code

• Avoid hardcoding API keys, passwords, or sensitive information in your code. Use secure vaults like AWS Secrets Manager, Azure Key Vault, or environment variables to manage credentials.

Don’t Ignore Logs and Monitoring

• Always log system activity and monitor pipelines. Skipping this can leave you vulnerable to undetected issues and failures.

Don’t Process Full Datasets Every Time

• Avoid processing the entire dataset for every ETL job. Implement incremental data loading to optimize the pipeline’s performance and reduce costs.

Review of Best Practices

Periodic review of security controls is a necessary to ensure the best practices are being used/implemented in the project and we can review based on above mentioned Do’s and Don’t.