Data warehousing has come a long way since the days of monolithic, on-premises systems that were inflexible, expensive, and slow. Today, cloud-native data warehouses offer unprecedented levels of scalability, performance, and agility—enabling organizations to gain faster insights from ever-growing volumes of data.
But despite the rise of newer technologies like data lakes and NoSQL databases, data warehouses remain the bedrock of business intelligence and analytics at most enterprises. In fact, the global data warehouse market is expected to reach $51.18 billion by 2028, growing at a CAGR of 14.5% from 2021 to 2028. (Source)
So what goes into designing and building a modern enterprise data warehouse that can meet the demands of today‘s data-driven businesses? In this in-depth guide, we‘ll break down the key components and best practices for data warehouse architecture, with a focus on leveraging the power of cloud computing and automation. Whether you‘re modernizing an existing data warehouse or building one from scratch, this guide will provide you with a comprehensive roadmap.
Anatomy of a Data Warehouse
At its core, a data warehouse is a centralized repository that aggregates data from multiple source systems and organizes it into a structured format optimized for querying and analysis. But there‘s a lot more to a data warehouse architecture than just a big database. A typical data warehouse consists of several logical layers and components:
Data Sources and Ingestion
The first step in building a data warehouse is to identify and connect to all the relevant data sources. This can include internal transactional databases like ERP and CRM systems, flat files from legacy systems, SaaS application APIs, sensor data, social media feeds, and more.
The data is typically extracted from these source systems on a periodic basis (e.g. hourly or daily) and loaded into a staging area in the data warehouse. The staging area serves as a temporary landing zone where the raw data can be cleaned, transformed, and prepared for loading into the main data warehouse tables.
There are two main approaches to this extraction, transformation, and loading (ETL) process:
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ETL (Extract, Transform, Load): With this traditional approach, data is extracted from the sources, transformed into the desired format, and then loaded into the data warehouse tables. This allows for data cleansing and validation to happen before the data is loaded.
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ELT (Extract, Load, Transform): In this approach, data is extracted and loaded into the staging area first, and then transformed within the data warehouse using SQL or other tools. This takes advantage of the data warehouse‘s processing power and allows for more flexibility in the transformations.
Image Source: My Data School
Data Storage and Modeling
Once the data is loaded into the staging tables, it needs to be organized into a structured format that supports efficient querying and analysis. This is where data modeling comes into play.
The most common data modeling techniques used in data warehouses are:
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Star Schema: This model organizes data into fact tables (which contain quantitative metrics like sales amount) and dimension tables (which contain descriptive attributes like customer demographics). The fact tables are connected to the dimension tables using foreign keys, forming a star-like shape. Star schemas are denormalized and optimized for fast querying.
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Snowflake Schema: This is an extension of the star schema where dimension tables are further normalized into multiple related tables. For example, a product dimension table might be split into separate tables for product category, brand, and supplier. This reduces redundancy but can make queries more complex.
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Data Vault: This is a more flexible and scalable modeling approach that separates the data into hubs (core business entities), links (relationships between entities), and satellites (descriptive attributes). This allows for easier integration of new data sources and changes to the model over time.
Another important aspect of data modeling is dealing with slowly changing dimensions (SCDs). This refers to how the data warehouse handles changes to dimension attributes over time, such as a customer changing their address. There are several techniques for handling SCDs, such as creating separate dimension records for each version of the attribute or using start and end date fields to track the changes.
Data Processing and Serving
With the data modeled and stored in the warehouse tables, it‘s ready to be processed and served up for analysis. This typically involves several layers and components:
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Operational Data Store (ODS): This is a separate area of the data warehouse that contains a current, integrated view of the data from the source systems. It‘s used for operational reporting and real-time analytics.
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Data Marts: These are subsets of the data warehouse that are optimized for specific business functions or subject areas, like sales or marketing. Data marts can be created as separate databases or as logical views on top of the main data warehouse tables.
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OLAP Cubes: Online Analytical Processing (OLAP) cubes are pre-aggregated, multidimensional datasets that enable fast slicing and dicing of the data across different dimensions. OLAP cubes are typically created as separate structures from the main data warehouse tables to optimize query performance.
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Query Engines: The data warehouse needs a high-performance query engine that can handle complex SQL queries and return results quickly. Many data warehouses use technologies like columnar storage, data compression, and massively parallel processing (MPP) to speed up query performance.
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Reporting and BI Tools: The ultimate purpose of a data warehouse is to support reporting, dashboards, ad-hoc analysis, and other business intelligence use cases. The data warehouse should integrate with popular BI and data visualization tools like Tableau, PowerBI, or Looker.
The Rise of Cloud Data Warehouses
Traditionally, data warehouses were implemented on-premises using specialized hardware and software from vendors like Teradata, Oracle, and IBM. But with the explosion of data volumes and the need for faster, more flexible analytics, many organizations are shifting their data warehouses to the cloud.
Cloud data warehouses offer several key advantages over on-premises systems:
- Scalability: Cloud data warehouses can easily scale storage and compute resources up or down based on demand, without the need for costly hardware upgrades.
- Elasticity: Many cloud data warehouses support "virtual warehouses" that can be spun up on-demand to handle burst query workloads and then shut down when not needed.
- Separation of compute and storage: In the cloud, data can be stored cheaply in object storage while compute resources are provisioned separately and scaled independently. This allows for more cost-efficient utilization of resources.
- Automation and self-management: Cloud data warehouses automate many of the tedious administration tasks associated with traditional data warehouses, such as provisioning, patching, backups, and performance tuning.
According to a recent survey by Datanami, 36% of enterprises are already using cloud data warehouses, and another 20% are planning to adopt them in the next 12 months. The top cloud data warehouse providers include Amazon Redshift, Snowflake, Google BigQuery, Microsoft Azure Synapse, and SAP Data Warehouse Cloud. (Source)
Each of these cloud data warehouses has its own unique architecture and capabilities, but they all follow a similar pattern of separating storage and compute, automating infrastructure management, and providing SQL-based interfaces for querying.
For example, Amazon Redshift uses a cluster-based architecture where data is stored in Amazon S3 and processed using a fleet of EC2 instances. Redshift distributes data and query processing across multiple nodes in a cluster, using columnar storage and MPP to parallelize and speed up queries. It also supports features like materialized views, data sorting and distribution keys, and automatic concurrency scaling.
Snowflake, on the other hand, uses a unique multi-cluster, shared data architecture that enables storage, compute, and services layers to scale independently. Snowflake automatically optimizes query performance using techniques like micro-partitioning, columnar caching, and vectorized execution. It also offers native support for semi-structured data, data sharing, and data marketplace integrations.
Image Source: Snowflake
When evaluating cloud data warehouses, it‘s important to consider factors such as:
- Pricing model (e.g. pay-per-query vs. pre-provisioned)
- Performance and concurrency for your workload
- Ecosystem and integrations with existing tools
- Security and compliance certifications
- Data migration and loading options
- Support for semi-structured and unstructured data
It‘s also worth comparing the total cost of ownership (TCO) of cloud data warehouses to on-premises systems. While cloud data warehouses have a lower upfront cost, the long-term costs can add up based on data storage and query volumes. However, a TCO analysis by Gigaom found that cloud data warehouses can be up to 39% less expensive than on-premises systems when you factor in hardware, software, and personnel costs. (Source)
Designing for Performance and Scalability
One of the biggest challenges in data warehouse architecture is ensuring fast query performance as data volumes and user concurrency grows. Here are some best practices and techniques for optimizing data warehouse performance and scalability:
Choosing the Right Storage Format
The choice of storage format can have a big impact on query performance. Most modern data warehouses use columnar storage, which stores data in columns rather than rows. This allows for better compression and faster scans of specific columns. Some data warehouses also support hybrid storage formats that combine columnar and row-based storage for different use cases.
Partitioning and Distribution
Partitioning and distributing data across multiple nodes or disks can help parallelize query processing and improve performance. Partitioning involves splitting large tables into smaller chunks based on a partition key, such as date range or geography. Distribution involves spreading data evenly across multiple nodes in a cluster to balance query workload.
Indexing and Materialized Views
Creating indexes and materialized views can speed up query performance by pre-computing results or creating shortcuts to frequently accessed data. However, indexes and views also add overhead to data loading and storage, so they should be used judiciously.
Data Compression and Encoding
Compressing and encoding data can reduce storage costs and improve query performance by reducing I/O and network traffic. Many data warehouses support techniques like run-length encoding, dictionary encoding, and delta encoding to compress data without losing accuracy.
Query Optimization Techniques
Data warehouses use various query optimization techniques to speed up query performance, such as:
- Vectorized execution: Executing multiple similar operations in a single instruction cycle
- Pushdown processing: Pushing filtering and aggregation down to the storage layer to reduce data movement
- Cost-based optimization: Using statistics and heuristics to choose the most efficient query plan
- Materialized views: Pre-computing and storing results of frequently used queries
- Caching: Storing frequently accessed data or query results in memory for faster access
Augmenting the Data Warehouse
While data warehouses excel at storing and processing structured, historical data, they are not always the best fit for other types of data or workloads. That‘s why many modern data architectures augment the data warehouse with other technologies, such as:
Data Lakes
A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. Data lakes are typically built on top of low-cost object storage like Amazon S3 or Azure Data Lake Storage. They can be used to store raw, untransformed data as well as processed data from the data warehouse.
Lakehouses
A lakehouse is an emerging architecture that combines the best of data warehouses and data lakes. Lakehouses provide a single platform for storing, processing, and analyzing all types of data, using open formats like Delta Lake and Iceberg. They support ACID transactions, schema enforcement, and BI-style analytics on top of low-cost object storage.
Streaming and Real-Time Analytics
Data warehouses are not designed for real-time data processing and analytics. For use cases like fraud detection, personalization, and IoT monitoring, you may need to augment the data warehouse with technologies like Apache Kafka, Apache Flink, or AWS Kinesis. These technologies enable real-time data ingestion, processing, and analytics, which can be combined with historical data from the warehouse for a complete view.
Data Virtualization and Logical Data Warehouses
Data virtualization and logical data warehouses provide a way to access and combine data from multiple sources without physically moving or storing the data. Using technologies like Denodo, Dremio, or AWS Athena, you can create a virtual schema that maps to underlying data sources, including data warehouses, data lakes, and operational databases. This enables self-service analytics and reduces data movement and duplication.
Machine Learning and AI
Data warehouses provide a rich source of historical data that can be used to train machine learning models for predictive analytics and data science. Many cloud data warehouses now offer built-in integration with machine learning services, such as Amazon SageMaker, Google AI Platform, and Azure Machine Learning. This allows data scientists to build and deploy models directly from the data warehouse, using SQL or Python.
Automation and DevOps for Data Warehousing
As data warehouses become more complex and mission-critical, there is a growing need for automation and DevOps practices to ensure reliability, agility, and efficiency. Some key areas of focus include:
Data Warehouse Automation
Data warehouse automation (DWA) tools like WhereScape, Qlik Compose, and erwin DWH automate the design, development, testing, and deployment of data warehouses and data marts. They use metadata-driven approaches to generate ETL code, data models, and documentation, reducing manual effort and errors.
DataOps
DataOps is an emerging practice that applies DevOps principles to data management and analytics. It involves continuous integration and delivery (CI/CD) of data pipelines, automated testing and monitoring, and collaboration between data engineers, data scientists, and business users. Tools like Airflow, Talend, and Fivetran enable DataOps by orchestrating and automating data workflows.
Infrastructure as Code
Infrastructure as Code (IaC) is the practice of managing and provisioning data warehouse infrastructure using machine-readable definition files, rather than physical hardware configuration or interactive configuration tools. IaC tools like Terraform, CloudFormation, and Ansible enable version control, automated testing, and reproducibility of data warehouse environments.
Conclusion
Data warehousing has come a long way from its origins in the 1970s. Today‘s cloud-native, automated, and augmented data warehouses are enabling organizations to gain faster, deeper, and more actionable insights from their data.
However, designing and implementing a modern data warehouse architecture is not a trivial task. It requires a deep understanding of data sources, data modeling, query optimization, performance tuning, and automation. It also requires a strategic approach that aligns with business goals, use cases, and future needs.
By following the best practices and leveraging the technologies outlined in this guide, you can build a scalable, performant, and agile data warehouse that powers your organization‘s analytical and operational workloads. And by augmenting the data warehouse with data lakes, lakehouses, streaming, and machine learning, you can create a truly modern data architecture that drives innovation and competitive advantage.
