6 Best Data warehouse Practices

Thalia Barrera
January 31, 2024

In the ever-evolving technology landscape, organizations are generating massive volumes of data. These data come from many data sources, including applications, third-party, APIs, IoT systems, and whatnot. With this much unstructured data, centralizing it in one place becomes necessary to store, manage, and analyze data more efficiently. This is where the data warehouse comes into play. 

In this article, you will learn about data warehouses and six popular best practices you can perform to leverage their full potential. 

What is a Data Warehouse? 

A data warehouse is a data management system that is designed to store and manage immense volumes of data and enable business intelligence processes. The data stored in the warehouse usually comes from a wide range of data sources, including log files and transaction applications. 

Generally, the centralized data in a warehouse is highly structured and ready to support analytics, artificial intelligence, and machine learning workflows. Overall, a data warehouse allows you to derive valuable insights to optimize business operations and improve decision-making.

Some of the most popular cloud data warehouses include Snowflake, BigQuery, Redshift, and Microsoft Azure. 

Best Practices For Data Warehouse

Implementing a data warehouse involves various best practices to ensure its effectiveness and maintainability. Here are some best practices for data warehouses:

Create A Data Model

A data model is an abstract representation that organizes data elements and describes the relationships between different data points. With this representation, you can document data sets that will be incorporated into the data warehouse and gain an understanding of what's important to the business. A well-defined data model clearly understands the content, context, and sources involved in the data set. This makes auditing and compliance with data requirements such as GDPR easier.  

To apply a data model in your data warehouse infrastructure, you can select any architecture for data modeling, including the third normal form (3NF), star schema, or data vault. Each architecture has its use case, but the choice of adaption should depend on your specific needs.

Overall, developing a data model for your data warehouse will enable you to use a single support and troubleshooting methodology to use the data warehouse efficiently.

Choose Cloud Data Warehouse

Usually, you can implement a data warehouse in two ways: on-premise and cloud. With an on-premise data warehouse, you must buy, deploy, and maintain hardware and software to build your infrastructure. However, a cloud warehouse is a managed solution provided by a service vendor where you pay for the storage and computing of resources. 

Many organizations are moving towards the cloud by replacing on-premise data warehouses to deal with growing data volumes. Why? Because cloud options are simply more convenient. 

Here are some of the reasons why you should consider cloud data warehouses: 

  • Scalability: Cloud data warehouses provide unparalleled scalability by allowing you to easily increase or decrease your computing resources as per your business requirements. Unlike on-premise options, you need to expand scaling perimeters and hardware for extra storage and processing needs.
  • Automation: With cloud data warehouses, you can automate most updates, patches, and maintenance tasks. This removes your burden for keeping data warehouses consistently up-to-date and optimized. 
  • Easy Accessibility: Cloud data warehouses facilitate easy global accessibility, allowing you to access data from any location with an internet connection. This enhances collaboration, which is crucial for distributed teams or international operations. 

Prefer ELT Over ETL

How you load data to the warehouse greatly impacts its efficiency. Organizations have traditionally used an extract-transform-load (ETL) approach to ingest data from multiple operational systems. However, the demand for more effectiveness has led to a new preferred approach, ELT. The ELT method extracts raw data from the source and is loaded into the data warehouse. Once the data is in the warehouse, you can use its computational power to perform transformations, such as data cleaning, normalization, and standardization.

With the ELT approach, you use your own cloud data warehouse instead of an external server to transform data. This makes data integration much more streamlined and easier to audit or trace the data in the future as it provides an image of the source in the data platform. 

To perform ELT in an efficient manner, you can use a data infrastructure platform like Airbyte. The platform can help you perform two best practices of the data warehouse by default. First, being an ELT platform, it follows a modern approach to data integration. Second, it automates the data integration by extracting data from disparate sources and loading it in your data warehouse. 

You can create any specific data pipeline with an extensive library of over 350+ pre-built connectors, an intuitive user interface, and orchestration capabilities. In addition, if you don't have a connector template for automating data pipeline creation, you can make a custom one using its Connector Development Kit

Some of the key features of Airbyte include:

  • Change Data Capture (CDC): Airbyte offers a CDC feature for specific data sources. With this feature, you can keep updated data records by capturing and synchronizing the changes made to data from source to destination. 
  • Security and Compliance: Airbyte provides robust security for data integration by offering features such as audit logs, strong encryption, and role-based access control. Additionally, the platform holds compliance certifications of ISO-27001, SOC 2, and GDPR to comply with multinational rules and regulations for operating internationally.

Join the community of over 15,000+ developers today by creating an Airbyte cloud account if you haven’t already. 

Use Automation Tools

The goal of a data warehouse is to activate and deliver data more quickly, and one way to do that is by automating tasks. By using automation tools, you can automate most of the tasks in a data warehouse, including writing code, creating data models, transforming data, and whatnot. There are many tools to do this task in a data warehouse. Here are two of the tools you can consider: 

  • Dbt: dbt (data build tool) enables data analysts and engineers to transform data in their warehouses. You can use this tool to perform automated advanced data transformation using SQL or Python. 
  • Astera DW Builder: Astera DW Builder is an end-to-end data warehousing tool that follows a metadata-driven approach to design, create, and deploy high-volume data warehouses. The solution includes a full data model designer and extensive ETL/CLT capabilities to construct a data warehouse on-premise or on the cloud. 

Robust Data Governance 

In the context of a data warehouse, data governance refers to an overarching framework for improving, managing, monitoring, and ensuring the quality, availability, and security of data stored. Data governance helps to ensure that data assets are formally managed throughout a shared environment, which makes it easier to understand and work with the information. 

The key elements for data governance in a data warehouse include:

  • Data Quality Management: It involves ensuring that data in the warehouse is accurate, consistent, and reliable. This includes procedures for data de-duplication, cleaning, and validation. 
  • Data Security and Privacy: This can involve physical and digital security measures like secure data centers, encryption, and access control.
  • Compliance and Regulation: It includes keeping the data warehouse compliant with applicable rules and regulations such as GDPR or HIPAA.

Data governance in a data warehouse is often part of an organization's broader data governance strategy. It helps you maximize the value of data, minimize risks, and optimize decision-making. 

Regular Testing

Data warehouse testing verifies that the system's data meets predefined quality and performance. This ongoing process includes verifying data against quality standards to ensure accuracy, completeness, and consistency. Some of the common testing types in data warehouses are:

  • Regression Testing: This testing ensures that new changes or updates in the system do not affect existing functionalities. 
  • User Acceptance Testing (UAT): This involves end-users validating whether the system meets the business requirements. 
  • ETL or ELT Testing: Using this testing, you can validate the ETL or ELT processes, ensuring data integrity during data movement. 
  • Stress Testing: As the name suggests, stress testing assesses the robustness and effectiveness of a data warehouse under extreme conditions. 

By incorporating a comprehensive testing strategy and performing it regularly, you can ensure that potential warehouse issues are taken care of. This will also allow you to make accurate decisions based on reliable data. 

That’s it. If you follow the best practices mentioned above, you can harness the full potential of data warehouses. 


In this article, you have learned best practices for using data warehouses. The practices include creating a data model, choosing cloud warehouses, preferring ELT, using automation tools, applying robust data governance policy, and performing regular testing. All these best practices will lay a foundation for a successful analytics program at the outset and accelerate the successful delivery of incremental business value.

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