A Guide to Modern Data Warehouse Architectures

Explore the benefits of modern data warehouse architecture. Learn how scalable, flexible designs empower organizations to manage data, drive insights, and adapt to evolving business needs.


Data warehouses are critical for any organization. Whether you are data-led and aiming to derive valuable business insights or simply need a centralized repository for reporting and analysis, a well-designed data warehouse is essential. 

Modern data warehouses have evolved to address the challenges posed by the massive growth of data volume, variety, and velocity. This evolution has led to a shift in how data warehouses are organized. The architecture has gone from traditional on-premises, monolithic systems to more flexible, scalable, and cloud-based architectures.

But there isn’t just a single architecture. Modern data warehouse architectures are composed of many different components arranged in different configurations along different levels and tiers. Here, we want to walk you through modern data warehouse architectures, what you’ll need to build the right one for your organization, the components, and how this can come together with Segment.

What is data warehouse architecture?

Data warehouse architecture is a data warehouse system's overall design and structure, including the components, technologies, and processes used to collect, store, transform, and analyze large volumes of data from various sources.

The modern data warehouse architecture includes:

  1. Data Sources: Operational systems, databases, applications, and external data sources that generate and provide data to the warehouse.

  2. Data Ingestion: The process of extracting and loading data from various sources into the data warehouse. This typically involves ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes responsible for data extraction, transformation, cleansing, and integration.

  3. Data Storage: The central repository where data is stored in a structured and optimized format for efficient querying and analysis. Modern data warehouses often utilize distributed storage systems, columnar databases, and cloud-based storage services for scalability and cost-efficiency.

  4. Data Processing: The compute layer responsible for processing and transforming data within the warehouse. This includes data aggregation, filtering, joining, and complex computations. Modern architectures leverage technologies like MPP (Massively Parallel Processing) databases, big data processing frameworks (e.g., Apache Spark), and cloud-based data processing services.

  5. Data Serving: The component responsible for making processed data available to end-users and applications for querying, reporting, and analysis. This often involves technologies like OLAP (Online Analytical Processing) engines, in-memory databases, and query acceleration tools.

  6. Data Consumption: The layer where end-users and applications access and utilize the data from the warehouse. This includes business intelligence tools, reporting and visualization platforms, data science and machine learning environments, and other data-driven applications.

  7. Data Governance: The processes, policies, and tools used to manage, secure, and ensure the quality and integrity of data within the warehouse. This includes data lineage, metadata management, data quality, security, and compliance.

The main goal of a data warehouse is to support business intelligence, reporting, and decision-making by providing a centralized, integrated, and consistent view of an organization's data.

Your data warehouse architecture should follow your business needs rather than the other way around. There is no right way to do this—many options can be combined to create a workable architecture for your business.

Some considerations are going to be:

  1. Data sources: Identify the various data sources that need to be integrated into your data warehouse, such as operational databases, external data providers, IoT devices, or social media feeds.

  2. Data volume and growth: Assess the current volume of data and estimate the expected growth rate to ensure your architecture can scale accordingly.

  3. Data types and formats: Determine the data types (structured, semi-structured, unstructured) and formats (CSV, JSON, XML, etc.) your data warehouse needs to handle.

  4. Data latency and freshness: Define the required data latency (batch, near-real-time, or real-time) and the acceptable level of data freshness for your business use cases.

  5. Query and analysis requirements: Understand the types of queries, reports, and analyses performed on the data warehouse to ensure the architecture can efficiently support them.

  6. Security and compliance: Consider the security and compliance requirements for your data, such as access controls, data encryption, and adherence to regulations like GDPR or HIPAA.

  7. Budget and resources: Evaluate your budget and the available resources (hardware, software, and personnel) to determine the feasibility of different architectural options.

  8. Cloud vs. on-premises: Based on your business needs, budget, and technical capabilities, decide whether to host your data warehouse on-premises, in the cloud or in a hybrid environment.

  9. Integration and interoperability: Ensure your data warehouse architecture can integrate with other systems and tools in your data ecosystem, such as data visualization platforms, machine learning frameworks, or business intelligence tools.

  10. Scalability and performance: Design your architecture to be scalable and performant, considering factors like data partitioning, indexing, caching, and parallel processing.

  11. Metadata management: Include a metadata management layer in your architecture to capture and manage information about the data, such as data lineage, data quality, and data definitions.

By carefully considering these factors and aligning them with your business needs, you can design a data warehouse architecture that effectively supports your data-driven initiatives and drives business value.

When choosing the specific architecture, consider whether you need a single, two, or three-tier data warehouse.

Single-tier data warehouse

A single-tier data warehouse architecture combines the data storage and processing layers into a single system. The data is directly loaded from the sources into the data warehouse without a separate staging area. 

This simple architecture needs more flexibility and scalability for complex data integration and transformation requirements. It is suitable for small-scale data warehouses with limited data sources and minimal data preprocessing needs.

Two-tier data warehouse

There are several ways of thinking about two-tier data warehousing, depending on where you want to split your tiers.

Firstly, a two-tier data warehouse architecture can separate the data storage and processing layers into:

  1. The bottom tier is the data warehouse database, which stores the integrated and transformed data. 

  2. The top tier includes the data sources and the ETL (Extract, Transform, Load) processes that extract data from the sources, transform in accordance to business rules, and load it into the data warehouse. 

This architecture introduces a staging area for data preprocessing and cleansing before loading it into the warehouse. It provides better scalability and data management compared to the single-tier architecture.

Secondly, you can also have a staging area and data warehouse:

  1. The bottom tier is the staging area for data preprocessing, cleansing, and transformation

  2. The top tier is the data warehouse database for storing the integrated and transformed data

In this architecture, the staging area is an intermediary layer between the data sources and the warehouse. It provides a dedicated data preparation and quality assurance environment before loading the data into the warehouse.

Finally, you can have a data Warehouse and data marts:

  • The bottom tier is the data warehouse database for storing the integrated and transformed data

  • The top tier is the data marts for specific business domains or departments

This architecture introduces data marts as a separate tier above the data warehouse. Data marts are subsets of the data warehouse optimized for specific business areas or departments. They provide faster query performance and easier access to relevant data for end-users.

Three-tier data warehouse

A three-tier data warehouse architecture adds a third tier for data presentation and access, separating it from the data storage and processing layers.

  1. The bottom tier remains the data warehouse database.

  2. The middle tier consists of an OLAP (Online Analytical Processing) server that facilitates fast and efficient querying of the warehouse data. 

  3. The top tier includes front-end data analysis, reporting, and mining tools.

This is how you should consider building your data warehouse architecture. Compared to the two-tier architecture, this architecture provides better performance, scalability, and separation of concerns. It allows for optimized data storage, processing, and retrieval, enabling complex queries and analysis on large datasets.

The choice of architecture depends on variables such as data volume, complexity, scalability requirements, and business needs. Modern data warehouse architectures often incorporate additional components like data lakes, real-time data processing, and cloud-based services to handle the growing volume and variety of data sources and enable advanced analytics capabilities. We’ll see those next.

Key components of a data warehouse architecture

Within the broad architecture, a data warehouse comprises several different elements. These work together to extract, transform, store, and analyze data from various sources, enabling organizations to gain valuable insights and make data-driven decisions. The main components of a data warehouse architecture include:

Data Sources

As we’ve said above, data sources are the origin points from which data is extracted for the data warehouse. These sources can include:

  • Operational databases (e.g., Postgres or MySQL) that support day-to-day business operations

  • CRM (Customer Relationship Management) systems like Salesforce or Microsoft Dynamics

  • ERP (Enterprise Resource Planning) systems such as SAP or Oracle E-Business Suite

  • Web logs and clickstream data from websites and mobile applications

  • Social media feeds from platforms like Facebook, Twitter, or LinkedIn

  • IoT (Internet of Things) devices and sensors

  • External data providers or third-party data services

Data sources can have various formats and may reside on-premises or in the cloud. The data from these sources is typically extracted using APIs, database connectors, or custom scripts.

ETL processes

ETL (Extract, Transform, Load) processes extract data from the sources, transform it to fit the data warehouse schema, and load it into the target storage. ETL processes involve several steps:

  • Extraction: Data is fetched from the source systems using techniques like full extraction, incremental extraction, or change data capture (CDC).

  • Transformation: The extracted data undergoes a series of transformations to ensure data quality, consistency, and compatibility with the target schema. Transformations may include data cleansing, deduplication, data type conversion, data enrichment, and applying business rules.

  • Loading: The transformed data is loaded into the data warehouse database or staging area. The loading process can be performed in batch mode (e.g., daily, weekly) or real-time using streaming or micro-batch techniques.

ETL processes are typically implemented using specialized ETL tools, such as Twilio Segment, or custom-built scripts (e.g., Python, SQL).

Data Storage

Data storage in a data warehouse architecture involves two main components:

  • Data Warehouse Database: This is the central repository that stores integrated, historical, and read-only data for analysis and reporting. It is typically designed using a schema optimized for analytical workloads, such as the star schema or snowflake schema. Common data warehouse databases include Amazon RedshiftGoogle BigQueryMicrosoft Azure Synapse Analytics, and Snowflake.

  • Data Marts: Data warehouse subsets focused on specific business functions, departments, or subject areas. Data marts provide a more targeted and performance-optimized view of the data for specific analytical needs. They can be implemented as separate databases or as views on top of the main data warehouse.

Data storage components are designed to support fast querying and retrieval of large datasets, using techniques like columnar storage, data compression, and partitioning.

Metadata Repository

A metadata repository stores information about the data stored in the warehouse, including:

  • Data definitions and business glossary

  • Data lineage and provenance

  • Data quality rules and metrics

  • Security and access control policies

  • ETL job definitions and schedules

Metadata repositories can be implemented using specialized metadata management tools (e.g., Collibra, Alation) or custom-built solutions leveraging databases and version control systems.

Query and Analysis Tools

Query and analysis tools enable users to access, visualize, and analyze the data stored in the warehouse. These tools include:

  • Business Intelligence (BI) platforms: e.g., Tableau, Power BI, QlikView

  • SQL query editors and IDEs: e.g., SQL Workbench, Toad, pgAdmin

  • Statistical analysis and data mining tools: e.g., R, Python, SAS, SPSS

  • Data science and machine learning platforms: e.g., Jupyter Notebooks, RStudio, DataRobot

These tools connect to the data warehouse database using standard protocols like ODBC, JDBC, or REST APIs and provide users with an interface to explore, visualize, and derive insights from the data.

Activating warehouse data with Twilio Segment

Twilio Segment's Reverse ETL feature enables businesses to activate their data warehouse by extracting data from the warehouse and syncing it to various third-party destinations.

Key components of Reverse ETL in Twilio Segment:

  1. Sources: In Reverse ETL, the data warehouse (e.g., Snowflake, Google BigQuery, Amazon Redshift) acts as the source. Users must provide the credentials and connection details to allow Segment to access the data stored in the warehouse.

  2. Models: Models are SQL queries that define the specific datasets to be extracted from the warehouse and synced to the desired destinations. Users can create models using the SQL Editor in Segment's interface, specifying the query, unique identifier column, and other relevant parameters.

  3. Destinations: Segment's Reverse ETL supports many destinations, including marketing platforms (e.g., Braze, Hubspot, Salesforce Marketing Cloud), analytics tools (e.g., Mixpanel), and even Segment's own Twilio Engage platform. Users can configure the destination settings and credentials within Segment.

  4. Mappings: Mappings define how the extracted data from the warehouse should be mapped to the fields and properties of the destination. Users can specify which records to send (added, updated, or deleted), select the schedule for data syncing, and define the field mappings between the source and destination.

By leveraging Twilio Segment's Reverse ETL, organizations can:

  • Sync audience data from the warehouse to marketing platforms for personalized campaigns

  • Enrich analytics tools with data from the warehouse for a more comprehensive view of the customer journey

  • Send offline or enriched data to conversion APIs (e.g., Facebook, Google Ads) for better attribution and targeting

  • Provide up-to-date data to business teams via syncing to tools like Google Sheets

Segment's Reverse ETL also offers robust observability and management features, including sync history, error reporting, email alerts, and the ability to reset or replay syncs as needed.

Twilio Segment's Reverse ETL empowers businesses to activate their valuable warehouse data by seamlessly syncing it to various destinations. This unlocks new possibilities for personalized marketing, enhanced analytics, and data-driven decision-making.

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Frequently asked questions

The most popular modern data warehouse architecture is a cloud-based, three-tier architecture consisting of:

  1. A storage layer using distributed file systems (e.g., Amazon S3, Google Cloud Storage) and columnar storage formats (e.g., Parquet, ORC) for cost-effective and scalable data storage.

  2. A processing layer using MPP (Massively Parallel Processing) databases (e.g., Amazon Redshift, Google BigQuery, Snowflake) for high-performance querying and data manipulation.

  3. A consumption layer with BI and analytics tools (e.g., Tableau, Power BI, Looker) for data visualization, reporting, and ad-hoc analysis.

This architecture leverages the scalability, flexibility, and cost-efficiency while separating concerns between storage, processing, and consumption.

The main components of a modern data warehouse architecture include:

  • Data sources: Operational databases, SaaS applications, streaming data, and other sources that provide data to the warehouse.

  • Data ingestion: ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes that extract data from sources, transform it as needed, and load it into the warehouse.

  • Data storage: Distributed file systems and columnar storage formats for storing raw and processed data.

  • Data processing: MPP databases and big data processing frameworks (e.g., Apache Spark) for querying, transforming, and aggregating data.

  • Data consumption: BI and analytics tools for data visualization, reporting, and ad-hoc analysis.

  • Data governance: Metadata management, data quality, security, and access control mechanisms to ensure data integrity, privacy, and compliance.

  • Orchestration: Workflow management and scheduling tools (e.g., Apache Airflow, AWS Glue) for automating and monitoring data pipelines.

When designing a data warehouse architecture, key considerations include: Business requirements: Understand various business stakeholders' data needs, use cases, and SLAs.
  • Data characteristics: Consider the volume, variety, velocity, and integrity of the data to be stored and processed.

  • Scalability and performance: Design the architecture to handle growing data volumes and support high-performance querying and data processing.

  • Data integration: Ensure the architecture can integrate data from diverse sources and support various data formats and structures.

  • Cost optimization: Leverage cloud computing's cost-efficiency and scalability while implementing best practices for data lifecycle management and resource utilization.

  • Security and compliance: Implement robust security measures, access controls, and data governance policies to protect sensitive data and comply with regulations (e.g., GDPR, HIPAA).

  • Flexibility and extensibility: Design the architecture modular, loosely coupled, and adaptable to evolving business needs and technological advancements.

  • By carefully considering these factors, organizations can design a data warehouse architecture that effectively supports their data-driven initiatives and delivers business value.