Data Warehouse Concepts, Architecture, and Comparisons
Data Warehouse Defined
A data warehouse is a centralized repository of digitally stored business information used to drive reporting and data analysis. It consists of massive quantities of data extracted from numerous sources, such as relational databases, internal applications, transactional systems, and external partner systems, aggregated into unified, well-structured historical data.
Cloud Data Warehouse Concepts
Data warehouses were traditionally hosted on-premises which made them expensive, hard to scale, and not self-service. Today, as datasets grow larger and real-time analytics becomes essential to competitive survival, data warehouses are increasingly hosted in the cloud.
This move to cloud data warehousing brings pay-as-you-go pricing models, a serverless approach, and on-demand resources that make data warehousing highly cost-effective and scalable. Compute and storage are separated, providing a data access layer specifically for fast analytics, reporting, and data mining that makes cloud data warehousing highly efficient, too.
Top Benefits of Data Warehousing
The global data warehouse industry is anticipated to reach $51.18 billion by 2028. Adoption is growing alongside the need for data democratization to better support non-technical business users with real-time data-driven insights.
Data warehouses offer several benefits:
- Enhances Data Quality and Confidence – When an organization uses a data warehouse, data is transformed from numerous source systems and types into a common format. By standardizing data from different departments into one repository to be used for their individual reporting, results will be consistent across business units. This increases data confidence and the ability to collaborate across the organization.
- Fuels Reporting – Consistent data formats and more complete data sets streamline the reporting processes to accelerate time-to-insight and ensure decisions are based on accurate data. This is because the data is captured, processed, integrated, annotated, summarized, and restructured in a semantic data store ahead of time, which makes analytics more efficient. Data warehouses are distinctly designed for descriptive analytics, which entails understanding relationships, patterns, and trends throughout the data.
- Provides Historical Insight – Data warehouses store robust historical data, whether it’s inventory data, sales data, personnel data, or others. This enables decision-makers to better understand past challenges or trends, make reliable predictions, and propel ongoing business improvements.
- Single Picture of Operational Data – The unification and harmonization of data from a broad range of sources provides a more holistic picture of the business. Data warehousing eliminates interdepartmental data silos that can create barriers to collaboration, enterprise-wide insights, and a cohesive view of the organization. By utilizing BI activities like data mining, for example, organizations are able to discover patterns in comprehensive data that might otherwise be overlooked.
Data Warehouse Architecture
A data warehouse architecture leverages dimensional models to determine the optimal method to extract purposeful information from raw data and convert it into an easily comprehended structure. The architecture characterizes the layout of data across various databases and needs on-premises servers in order for every element to function properly.
On-premises data warehouses are architected in single-tier, two-tier, and three-tier structures. Single-tier is uncommon; it’s ineffective for organizations with big data needs. There’s also no way to segregate analytics from transaction processing. Two-tier data warehouses are often used by smaller businesses and separate the physical source of the data from the actual data warehouse, and incorporate data mart usage. However, they’re not scalable and don’t support numerous end users. Three-tier architectures are the most commonly used data warehouse architecture. The bottom tier is a database server – typically a relational database – where transformed data is loaded from other sources. The middle tier is the application layer featuring a pre-built Online Analytical Processing (OLAP) server that organizes data to ready it for analytics. The top tier consists of tools for reporting and business intelligence.
In most cases, a data warehouse features four principal components:
- Central database: Acts as the foundation of the data warehouse that stores the organization’s data and ensures it’s viable for reporting.
- ETL tool or data integration platform: Data is extracted from various sources, transformed in an appropriate configuration, and loaded into the data warehouse to align information for rapid analytics.
- Metadata: Provides a framework for the data and ensures business users are able to better understand the data available within the warehouse and transform it into meaningful information. This includes business metadata that augments data with context, and technical data that indicates how to access the data based on where it’s located and its structure.
- Data warehouse access tools: Enables users to interact with the data in the warehouse via specialized technologies and tools, such as OLAP tools, data mining tools, application development tools, and query and reporting tools.
Cloud data warehouse architectures aren’t structured the way traditional data warehouses are as detailed above but vary depending on the specific cloud data warehouse platform. Some of these include Amazon Redshift, Microsoft Azure SQL Data Warehouse, Snowflake, and Google BigQuery.
Data Warehouse vs. Data Lakes, Data Marts, and Cloud Data Warehouses
Data warehouses, databases, data lakes, data marts, and data hubs are all data structures that can be used in conjunction with each other to support different roles in a modern data management infrastructure. It’s up to business leaders to understand the distinction.
Data warehouses are data management systems that store structured data and support repeatable analytics. The purpose of data in a data warehouse is defined and contains data from a variety of sources.
Data Warehouse vs Data Lake
Data lakes collect raw data and events from diverse source-based systems and support data preparation and exploratory analysis, whereas a data warehouse uses processed data. A data lake helps organizations store large amounts of structured, semi-structured, and unstructured data, and organizations don’t need to know ahead of time how their data will be used. A data warehouse is used for structured, filtered data, which has an intended purpose.
Data Warehouse vs Data Mart
A data mart is a simplified form of a data warehouse that focuses on a single area of business. Data marts help teams access data quickly without the complexities of a data warehouse because data marts have fewer data sources than a data warehouse. Data marts provide a single source of truth and serve the needs of specific business teams.
Data Warehouse vs. Cloud Data Warehouse
On-premise data warehousing is good for structured, historical data. But it has its limits. As datasets exceed the volume, velocity, and variety of what on-premises data warehousing can handle, cloud data warehouse architecture steps up to deliver on the speed, flexibility, and scalability of today’s data integration needs.
Why Move to a Cloud Data Warehouse
According to Gartner, 57% of data and analytics leaders are currently investing in data warehouses. Due to the accelerated pace of digital transformation, more organizations are transitioning their data warehouses to the cloud. The cloud in general enables more agility, elasticity, collaboration, and accessibility while minimizing typical barriers to entry, such as complexity and costs.
A cloud data warehouse offers better speed and performance, separate storage and compute, more security, enhanced access and integration, advanced self-service capabilities, and scalability for changing storage and compute requirements. A cloud data warehouse architecture also mitigates common risks associated with on-premises data warehouses, such as minimal data backup and recovery for example.
Cloud data warehouses are an important component in modern data strategies that enable organizations to adapt more effectively to evolving consumer behaviors and market fluctuations. The agility of cloud-based warehouses supports existing workloads and enables organizations to integrate new technology tools or scale capacity as business needs change. Also, by implementing more versatile data management practices offered by cloud data warehouses, the organization is better positioned to adapt to uncertain conditions and dynamic environments.
The Role of Automated Data Pipelines
Data pipelines transfer data from disparate sources to a central data warehouse. Modern data pipelines need to be reliable and flexible, but the exponential growth in data volume has left manual approaches to building data pipelines inefficient. Consequently, organizations are turning to data pipeline automation to build data pipelines that are able to keep pace with modern business processes. Data pipelines must be efficient, agile, and adaptable to evolving conditions.
By leveraging the power of automation, businesses can scale data pipelines as needed while eliminating redundant, obsolete, and incomplete data. Automated data pipelines maximize data accessibility and help to support the seamless integration of multiple data sources into the data warehouse. Automation also improves traditional data warehouse designs by minimizing time-consuming tasks, like creating and deploying ETL pipelines.
Smart data pipelines are increasingly critical for effective cloud data warehouse integration because they enable sources, destinations, and data types to be abstracted from the pipeline so changes can be made without the need for any rewriting. They’re also able to automatically detect data drift in real-time, which ensures that data doesn’t drop or disappear during the ingestion process when data schemas change.
Other advantages of smart data pipelines include:
- Facilitating real-time transformation regardless of the source, destination, data format, or processing mode.
- Automatically transforming formats of the same data type and creating new fields.
- Enhancing performance and streamlining debugging via built-in preview and snapshots.
- Auto-creating a table or multiple tables whether the source schema is known or unknown.
- Facilitating stop and restart of the pipeline and failover at the execution engine.