When every new analytics request requires another hand-coded pipeline, maintaining those pipelines can leave little time to support new reporting or AI work.

Modernizing your data architecture means deciding how data should move, where it should live, and who should maintain it. This guide explains those decisions through the core layers, five design principles, and the differences between storage patterns and ownership models.

What is data architecture?

Data architecture is the design for how an organization collects, stores, transforms, governs, and uses data. It specifies the systems involved, their connections, and the standards that let teams use data consistently. It also identifies which teams maintain pipelines, manage access, and validate the data sets used in reports and applications.

In many legacy environments, teams build separate integrations for individual reports or applications. Over time, those connections leave departments maintaining different copies of the same data. Modernizing the architecture involves replacing duplicated work with reusable pipelines and shared data sets that have clear owners.

Cloud services offer additional flexibility. Separating storage from compute lets your team increase processing capacity without expanding storage at the same rate. This makes it easier to accommodate changing workloads, but teams still need shared definitions and ownership to use data consistently. 

These decisions come together across the following layers, which are common to cloud-based, on-premises, and hybrid architectures:

• Data sources: Business applications, databases, files, and devices produce the records you need.
• Ingestion and data pipelines: Data transfers bring those records into analytical systems at a defined frequency.
• Storage: A warehouse, lake, or lakehouse retains data for the workloads you plan to support.
• Transformation and modeling: Processing turns source records into data sets with consistent business definitions.
• Consumption: Business intelligence (BI), AI, and operational tools use those data sets for reports, predictions, or actions.
• Governance and metadata: Ownership, permissions, definitions, and lineage support responsible use across every layer.

This simplified data architecture diagram shows a common flow. Consider a sales example:

Sales application → Ingestion pipeline → Data warehouse → Tested sales models → BI dashboard

Data moves from a sales application into an ingestion pipeline, then into a data warehouse, where it’s transformed into tested sales models that power BI dashboards. Governance spans every stage of the process.

This example follows an extract, load, transform (ELT) approach in which data is loaded into the destination before it’s transformed. Other pipelines transform data before loading it into the destination, following extract, transform, load (ETL). The appropriate sequence depends on requirements such as removing sensitive fields before they reach the destination or using the destination’s compute resources for transformation.

The choice between ETL and ELT is just one of many architectural decisions data teams make. Teams must also decide how to scale infrastructure, govern data, manage costs, and support future portability. A data architecture framework provides a structured method for making and documenting these choices. The principles below help your team evaluate its options.

Core principles of modern data architecture

The following five principles help teams accommodate changing workloads while keeping data accessible, governed, and manageable:

• Cloud-native: Use cloud services to provision resources as demand changes, while monitoring the cost of that flexibility. Capacity that scales automatically still needs budgets and workload controls.
• Decoupled storage and compute: Scale processing separately from retained data so a demanding query doesn’t require expanding both. This distinction matters when evaluating data ingestion costs.
• Data governance and data security: Define ownership, access, and quality requirements alongside the data flow. Your governance controls should apply wherever sensitive records move.
• Open-source formats: Prefer supported open formats when portability matters, then verify compatibility across the engines you intend to use. An open format reduces some switching barriers but doesn’t remove migration work.
• Unified architecture: Give teams shared, governed data sets and definitions so reports and models can reuse the same business logic. This helps reduce duplication without requiring every workload to run on one engine.

Processing frequency is another design choice. Batch pipelines process records in groups, while streaming pipelines process events as they arrive. An architecture may support both, depending on how quickly different applications need updates. Those requirements also influence how teams organize storage and access.

Modern data architecture patterns

Architecture patterns describe how parts of a data system are organized. Warehouses, lakes, and lakehouses determine how data is stored and queried, while data fabric and data mesh address how distributed data is connected and managed, including who is responsible for it. 

All these choices work together. Choosing a warehouse, for example, still leaves decisions about access, integration, and ownership.

Storage architectures: Data warehouse, data lake, and lakehouse

A data warehouse architecture organizes analytical data into structures optimized for reporting and SQL queries. It suits teams with recurring questions and agreed business measures, such as monthly revenue by product.

A data lake architecture retains files in varied formats, often on object storage such as Amazon S3 or Azure Data Lake Storage. That flexibility leaves you responsible for making data sets discoverable and usable.

A lakehouse adds table management to a data lake so teams can update and query data more reliably. Query engines use table metadata to identify which files belong to a table and which version of the data to read. This helps reports and other workloads read a consistent table state while updates are underway.

Lakehouses typically rely on open table formats such as Apache Iceberg^™, Delta Lake, and Apache Hudi^™ to provide these table management capabilities. Their transaction support helps prevent readers from seeing partially completed updates, and schema evolution capabilities support changes to table structure. The available behavior depends on the table format and the engines using it.

For example, a lakehouse can support both BI and machine learning over a common set of maintained tables. Compare that work with the capabilities of lakehouses and data warehouses to determine which fits your actual workloads, including the maintenance needed to prevent data integrity issues.

Data fabric vs. data mesh

The main distinction is how each approach coordinates distributed data. A fabric emphasizes integration and metadata, while a mesh emphasizes accountable domain ownership.

• Data fabric: Uses metadata and integration capabilities to coordinate access across systems, making it useful when acquisitions or multiple cloud environments leave data distributed.
• Data mesh: Assigns responsibility for data products to business domains, making data mesh architecture useful when a central team can’t keep pace with domain needs.

A fabric can use active metadata to inform monitoring and integration decisions, but it doesn’t eliminate the need for owners. A mesh likewise needs shared infrastructure and standards. Without them, domain autonomy can create inconsistent data sets. You should assess both the technical connections and the people expected to maintain them when making a decision.

No matter which architectural pattern you choose, long-term success depends on how those choices are implemented and operated.

Modern data architecture best practices

Test the design on one workload before expanding it across the business. Check whether data arrives on time, transformations produce the expected results, and the responsible team can identify and recover from failures. The following best practices help turn that initial design into a maintainable system for your team:

• Automate and monitor movement. Build data pipeline architecture around repeatable transfers and recovery, using automated data movement to reduce custom maintenance as source schemas change.
• Set measurable outcomes. Tie enterprise data architecture to a target, such as report freshness or reconciliation time, then assess progress against that target.
• Build governance early. Assign data set owners and access rules before wider rollout, and keep transformation code under version control so changes can be reviewed.
• Plan for portability. Evaluate Open Data Infrastructure through supported formats, catalogs, and engines, including the work required to move between them.
• Design for actual AI and latency needs. Use AI planning priorities to identify candidate workloads, then translate their infrastructure requirements into specific data access and freshness targets.
• Match ownership to team capacity. Adopt domain-owned data products only where teams have the skills and time to maintain them under shared standards.

Designing a maintainable, scalable data architecture is difficult when your pipelines are custom-built. Automating those pipelines reduces the operational overhead.

Build a modern data architecture with Fivetran

Maintaining pipelines and lake tables takes ongoing engineering work. Fivetran handles parts of that maintenance, reducing the ingestion infrastructure your team must build and operate.

Fivetran automates ingestion from supported sources into your chosen destination. For lake-based designs, its Managed Data Lake writes data to your cloud storage and maintains Apache Iceberg metadata, with optional Delta Lake metadata for the same underlying data. Compatible query engines can then access those tables while your team develops the business models and applications that use them. 

Start a free trial to test Fivetran with your chosen sources and destination, then evaluate whether the pipeline meets your freshness and recovery requirements.

Apache Iceberg and Apache Hudi are trademarks of the Apache Software Foundation.

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