What is data infrastructure and how to design it

In a world that demands agility and adaptability, a company can move forward with the right data infrastructure. Companies need effective systems that maintain low costs and high performance while supporting big data initiatives.

These days, many companies are looking at AI to make their data infrastructure leaner and more efficient. Gartner reports that over 54% of operational leaders use AI in their infrastructure to automate processes and optimize costs.

However, simply adding AI isn't enough. Teams struggle with fragmented data pipelines and inconsistent transformation logic that slow analytics workflows. Poor data quality and siloed data sources create bottlenecks that prevent organizations from becoming truly data-driven.

dbt resolves these challenges by automating transformations and standardizing data modeling, no matter where your data lives. In this article, we'll explore how to design modern data infrastructure with dbt to streamline transformations and modeling workflows while ensuring scalability and data governance.

What is data infrastructure?

Data infrastructure is the system of tools and processes that businesses use for data management. This ecosystem may involve data warehouses, data lakes, and cloud platforms. Its key components include data ingestion, data storage, data processing, transformation, and secure access controls.

Robust data infrastructures support data analytics, enabling businesses to make more insightful, data-driven decisions. A solid data infrastructure also addresses data quality concerns through automated testing and validation.

It's important to design data infrastructure with data security features that protect sensitive information from unauthorized access. This approach ensures data privacy and helps companies remain compliant with regulations like GDPR.

How to design a modern data infrastructure

Designing your data infrastructure must begin with careful planning. Best practices in data ingestion, storage, and transformation help build efficient systems for all data operations. The right data architecture balances business needs with technical functionality.

Designing your data ingestion layer

Businesses obtain data from multiple data sources, including CRMs, APIs, websites, and spreadsheets. This ingestion can be automated with tools like Airbyte or using custom scripts. These tools provide connectors for standard data sources and support various data types and formats.

However, for internal APIs and unique systems, custom scripts are often more effective, as they offer greater customization and control over data flow. ETL (Extract, Transform, Load) processes handle the movement of raw data from source systems into your data infrastructure.

It's essential to enforce data schemas during ingestion. This prevents structural inconsistencies that could negatively impact downstream data analytics. Frameworks like AWS Glue help properly format data to reduce downstream errors. This proactive step ensures reliable datasets for all data analysis and reporting that follows, supporting both business intelligence and machine learning use cases.

Choosing the right data storage architecture

Data should be stored in centralized storage systems that are reliable and secure. Data storage architectures define how data is structured and integrated within an organization. The right storage solution depends on your data volumes and workloads.

Comparing data storage architectures:

Data warehouses like Redshift and BigQuery, as well as cloud-based platforms, store structured data and support complex queries. They enable fast data retrieval and reporting, providing timely insights for decision-making. Data warehouses excel at handling analytics tools and business intelligence workflows.

Data lakes can store unstructured data and semi-structured data. They retain the original data form, making it reusable for both analytics and AI use cases. Data lakes also offer flexible storage for diverse datasets and support large-scale data processing. Technologies like Hadoop enable processing of big data within data lake environments.

Data lakehouses are hybrid storage solutions that combine the reliability of data warehouses with the flexibility of data lakes. This data architecture provides the best of both worlds for modern data infrastructure.

Both on-premises and cloud-based storage systems have their place, though cloud solutions offer better scalability for growing data volumes. Consider data integration requirements when choosing between these options.

Transforming and modeling your data

Data must be cleaned and adequately structured after centralization so it's usable. Removing errors and inconsistencies makes the dataset reliable enough for analysis and data modeling.

The data transformation process involves preparing raw data for analysis by cleaning and validating it. Naming rules, joins, and logical data models are applied to present information in a meaningful way. This stage is critical for ensuring data quality and data integrity.

Tools like dbt simplify data transformation tasks and enable modular transformations with automated testing. Data engineers and analytics engineers use SQL to build reusable data models. dbt also integrates with orchestration tools like Airflow to keep transformations consistent when new data arrives.

Reliable and well-modeled data enhances the accuracy of predictive models and AI-powered systems. Teams can build dashboards and generate insights that accurately reflect real business conditions. This data-driven approach gives organizations a competitive advantage.

Making data accessible for insights

Data modeling and validating data are only useful if teams can effectively access it. The way data flows into dashboards and reports reveals patterns and risks that might otherwise remain hidden. Data accessibility is crucial for enabling self-service analytics.

Dashboards highlight critical metrics where decisions happen. When users can quickly identify trends through visualization, they adjust strategies before small issues become big problems. Modern dashboards provide real-time data updates for faster decision-making.

Automated reports deliver the right data at the right time and prevent teams from relying on outdated data. Automation frees analysts to focus on data analysis instead of manual data compilation.

Analytics tools can be embedded into existing workflows to deliver actionable insights. Users explore data within their apps, so decisions are made contextually without switching tools. This embedded approach supports various use cases across the organization.

When teams access real-time data, they use it confidently and make better decisions. Strong data governance ensures that users work with trusted, accurate information.

How dbt helps teams make smarter decisions

Data transformation often becomes complex due to inconsistent logic and manual workflows across different teams. dbt makes the transformation step practical for analysts by using standardized SQL models. Data and analytics engineers define business logic and transformation steps directly in code. These SQL models become the modular components of subsequent data pipelines.

Designing efficient data infrastructure with dbt:

Modular transformation. Data engineers use dbt to build modular SQL models that depend on each other. This modular approach creates a clear and maintainable data pipeline from raw to curated layers. The functionality extends across your entire data engineering ecosystem.

Version control and data governance. Version-controlled data models using Git track every change transparently. This enhances data governance and ensures consistent transformation logic across environments. Teams maintain clear ownership and accountability for data assets.

Automated testing. Automated tests in data models maintain unique and complete records and verify freshness with each run. These validations detect data quality issues early, before they affect dashboards or AI outputs and lead to incorrect decision-making. Testing is essential for building trust in your data infrastructure.

Cost and speed optimization. Incremental materializations handle only new data to cut warehouse load and runtime costs. This optimization keeps queries fast and data pipelines more efficient as data volumes increase. Smart resource allocation delivers better performance at lower cost.

Enhanced transparency. The automatically generated data lineage graph displays every dependency from data source to report. This visual lineage adds trust by making ownership and data flow visible. Understanding data lineage is crucial for troubleshooting and optimization.

Accessible documentation. dbt generates documentation for data models, columns, and data sources within the project. Documentation enables all users to understand data logic without relying on data engineers. This democratizes data access across the organization.

Semantic layer. dbt's semantic layer enables codifying metrics as declarative assets. Teams then store these definitions centrally and share them with BI tools, machine learning pipelines, and APIs. This shared logic keeps dashboards and AI models aligned on the same trusted definitions, eliminating silos.

APIs. With dbt Cloud APIs, you can trigger downstream pipelines after completing the transformation. This orchestration ensures AI workflows always use the latest validated data. APIs enable data integration with various analytical tools and platforms.

Fast, low-cost development. dbt's Fusion engine understands the syntax of all major data warehouses and can validate changes locally, before ever checking in a single line of code. It delivers stateful performance, only re-running models when needed, resulting in significant cost savings. This scalable approach works regardless of data platform.

With centralized logic, different business units operate using the same metric definitions. This helps business users make confident, data-driven decisions based on high-quality data.

How your business can benefit from a modern data infrastructure

A well-designed data infrastructure provides stability and ensures data flows reliably from source to dashboard. This stability removes manual effort and immediately reduces operational costs. Solid data infrastructure creates a foundation for data strategy and initiatives.

A streamlined data infrastructure adds several business benefits:

Accelerated decision-making. Immediate data access enables businesses to absorb insights and act in real-time. Teams quickly adjust their strategies to respond to market changes. Real-time analytics support faster, more informed decisions.

Enhanced efficiency. Automation in data ingestion and data processing minimizes manual labor time. This frees human experts to focus on complex data analysis and strategic initiatives rather than routine data management tasks.

Scalable growth. The scalability and elasticity of cloud solutions enable seamless scaling of data volumes and workloads. This capability powers both current and future analytics and AI initiatives across your data centers and cloud infrastructure.

Personalized experience. Real-time data processing yields faster customer insights. These insights enable AI-powered personalization services, such as intelligent recommendations. Better data quality directly improves customer experiences.

Organizations that invest in modern data infrastructure gain a competitive advantage through faster insights, better data governance, and more efficient data management across their entire ecosystem.

Conclusion

Data infrastructure isn't a one-and-done affair. It's a strategic asset. Teams avoid data silos and respond faster to market changes when data infrastructure is efficiently designed. Strong data architecture supports scalability and adaptability as business needs evolve.

Data transformation becomes more straightforward when using dbt. With dbt, you can manage your data via a centralized data control plane, bringing consistency and data quality to your data no matter where it lives—from data warehouses to data lakes to hybrid environments.

A data infrastructure designed with intention creates a solid foundation for your data management and data strategy. To try it for yourself, sign up for free today and start automating your data infrastructure for high-performance, AI-powered analytics that deliver real business value.