Data warehouse examples and benefits

Data warehousing has grown into one of the most efficient ways to manage and transfer information in enterprise settings. This comes with a host of benefits for anyone who works with big data or cloud operations. The data warehouse examples provided in this article differ in how they’re structured and what they’re used for, but their commonalities are becoming increasingly important in a modern business environment.

A data warehouse is a centralized storage and transfer system that allows easier analysis and sharing of data from structured and semi-structured data. Data warehouses excel in analytical tasks, enabling read-only operations that support complex queries. This allows organizations to create a single source of truth for their data, facilitating efficient analysis and reporting.

Data warehouses are particularly beneficial for business intelligence and machine learning applications, as they can track historical data over time and maintain consistency in data management. By pulling data from multiple sources and structuring it into a unified framework, data warehouses empower decision-makers with valuable insights, enhancing their ability to make informed, data-driven decisions.

As similar as some of their functions are, modern data warehouses still specialize enough to be grouped into different categories. 

Enterprise data warehouse (EDW)

Enterprise data warehouses act as centralized repositories where enterprises can keep their organizational data. They’re distinguished by how they pull data from different sources, such as transactional systems, operational databases and external data sources, to provide a comprehensive view of the business. EDWs support more complex queries and data analysis than smaller databases, which enables enterprise-wide reporting and decision-making by key stakeholders.

Operational data stores (ODS)

Operational data stores are intermediaries between EDWs and transactional databases. These work best for real-time operational decision-making. Most operators turn to ODS systems for applications that call for up-to-date data, such as short turnaround order tracking and customer service operations.

Data marts

Data marts are a fairly specialized subset of the larger EDW environment. They’re often used in conjunction with the larger EDW but have a tighter focus on one single function, such as sales, finance or internal processes. These are the preferred approaches to carving out a niche inside the larger EDW to act as a more nimble, responsive system than the EDW at large.

On the surface, data warehouses can be mistaken for ordinary databases. Both store data in a way that can be queried and analyzed. However, the similarities end there. On a deeper level, the two are very different. 

Databases:

• Optimized for transactional processing

• Able to store relatively current data on a rotating basis, mainly for real-time operations

• Well-suited for relatively simple queries and limited transactions

• Ideal for read-write operations

• Works on a single application or a defined single system

Data warehouses:

• Optimized for analytical processing

• Capable of storing large amounts of data 

• Commonly holds large stores of historical data

• Developed to support much more complex queries and in-depth data analysis

• Capable of managing large read-only operations

• Broad-based and capable of consolidating data from multiple sources

These distinctions help clarify the difference between a database and a data warehouse, but highlight another potential point of confusion. Data warehouses are also distinct from data lakes. 

Data warehouses:

• Store structured data drawn from multiple sources

• Are optimized for fast query performance and revealing analytics

• Ensure data consistency and quality with multiple check systems in place

• Support business intelligence tools and reporting that drive data-relevant decisions

Data lakes:

• Store raw, unprocessed data in its native format (structured, semi-structured and unstructured)

• Are designed for big data processing and advanced analytics, including some ML and AI applications

• Offer significant flexibility for data exploration and experimentation by data scientists

• Work well with machine learning, data science and ETL processes that need complex iterative operations

Moving from an older database style to modern data warehouse tools has multiple advantages, including better data management and analytical capabilities for teams working on big data. Some of these advantages are structural, while others are operational.

A data warehouse is a central repository for all organizational data, consolidating information from various sources into a single location. This centralization simplifies data management, ensures data consistency and makes it easier to perform comprehensive data analysis.

Data warehouses enforce data quality and consistency by applying standardization and validation rules during the ETL (extract, transform and load) process. This ensures the data stored in the warehouse is accurate, reliable and suitable for analysis.

With a data warehouse, users can easily access and retrieve data from a single source, reducing or altogether eliminating the need to navigate multiple systems. This improved accessibility accelerates data retrieval and analysis, enabling faster decision-making and reporting by key decision-makers.

Data warehouses are optimized for complex queries and data analysis, providing powerful tools for data mining, trend analysis and mid- to long-term forecasting. This streamlines the analytical process and helps enterprises uncover valuable insights from sometimes complex data streams.

By providing a comprehensive and accurate view of organizational information, data warehouses enable data-driven decision-making. Business leaders can rely on real-time insights and data from historical patterns to make informed decisions. This drives strategic initiatives and helps improve overall performance across departments.

Modern data warehouses, especially cloud-based solutions such as Snowflake, offer enhanced scalability and the flexibility to handle growing data volumes across changing business needs. Organizations can generally scale their storage and computing resources up and down as needed, which helps ensure optimal performance and cost efficiency.

Data warehouses frequently store historical data, allowing organizations to analyze trends and patterns over time. This historical perspective is invaluable for strategic planning, performance measurement and identifying long-term trends that inform better decisions over time.

Data warehouses can seamlessly integrate with business intelligence tools. This helps develop advanced data visualization and reporting, as well as user-friendly dashboard creation. The integration permitted by a data warehouse enhances the analytical capabilities of the organization using it and tends to make data insights more accessible to users.

Data warehouses can help enterprises better comply with strict regulatory requirements by providing a secure and auditable environment for data storage and management. They support robust data governance practices, ensuring data privacy, security and adherence with industry standards in most fields. This can be especially important in fields such as financial services, where strict privacy and data security regulations govern the use of customers’ data.

Because a data warehouse tends to be a general solution, it’s not surprising there's more than one way to implement it, depending on user's needs. Devs can set up their data warehouses in a variety of ways, but there are three broad categories these structures tend to group around: single-, two- and three-tiered architecture.

Single-tier architecture tries to minimize the amount of data storage redundancy by keeping all the data in a single layer that's easier to query and analyze. This type of architecture is most often used in small-scale data warehouses where the data volumes are relatively manageable and where simplicity is a priority.

Two-tier architecture separates the data storage and processing layers. The first layer contains the data sources and ETL processes, while the second layer contains the data warehouse and most of the analytical tools teams use to make sense of it. This separation improves performance and scalability, but may introduce latency due to the communication lag between layers.

Three-tier architecture is the most common and robust design for data warehouses. It separates the layers more finely than two-tiered architecture to improve performance, scalability and the flexibility of use. This model is popular for architects of large datasets and managers working with high-demand applications. Three-tiered data warehouses generally break down into:

• Bottom tier: The data sources and ETL processes are stored here.

• Middle tier: This is the data warehouse itself, where data is stored, managed and processed in place.

• Top tier: The top tier stores many of the analytical and reporting tools used to access and analyze the data on a high level.

Data warehouses collect, store and manage data as their primary function, so their functional components are oriented toward these goals on every tier. It goes without saying this system can get complicated, and a lot of development work has to be done to manage the complexity in a way that keeps the front end accessible and easy to use. The more care that goes into integrating standard components of data warehouses, the less skill it takes to navigate queries and analysis, allowing these functions to be done by more members of the teams.

The central database is the core of the data warehouse, where the system stores all the data that's been integrated from various sources. These sources can include related or linked transactional databases, CRM systems, ERP systems and external data sources from anywhere.

ETL tools are responsible for extracting data from the source systems and transforming it all into a suitable format for use. Then, they load the data into the data warehouse for secure storage. This process helps ensure data quality, consistency and integration across systems.

Metadata provides information about the data stored in the warehouse, including data definitions, structures and relationships between points. This helps users better understand and navigate the sometimes enormous data warehouse for more effective data management and analysis. The metadata acts almost like the card catalog at a library, categorizing useful information in a variety of ways for faster search and retrieval functions.

The data model defines the logical and physical structure of the data warehouse. This includes the data schema, internal and external tables, relationships and constraints. It effectively lays out the roadmap to how data is organized, stored and accessed within the data warehouse.

Data access tools, and the user interfaces that go with them on the front end, enable users to retrieve and analyze data from the warehouse in a more efficient way than building raw queries alone. These tools can include SQL query interfaces, BI tools and various reporting applications.

Data security components protect the data warehouse from unauthorized access and other security threats. This component layer includes typically sophisticated encryption protocols, strict access controls, industry standard authentication and reliable auditing to ensure things are working well and to validate security measures.

Data storage is the physical storage infrastructure where the data warehouse resides. This can be on-premises servers, cloud storage or a combination of both. While all three models are popular, the hybrid cloud model has several advantages that are rapidly making it the go-to choice for devs working in the AI, ML and financial sectors.

Snowflake is a popular cloud-based data platform that allows users to store, process and analyze data much faster than transactional databases. It's built on Google Cloud, Amazon Web Services and Microsoft Azure, and it supports various multi-cloud environments. Snowflake is designed to be faster, easier to use and more flexible than traditional data warehouses.

Optimizing workflows in Snowflake requires setting warehouse sizes and schedules to align with the assigned workload requirements. This helps ensure resources are used efficiently, minimizing costly queries and maximizing performance with as little latency as possible.

Configuring warehouses to scale automatically based on demand helps with optimizing resource usage. Smaller warehouses can be used during periods of low activity, while larger warehouses are kept on standby to help handle peak loads. This dynamic scaling ensures dev teams only pay for the resources they actually use and that warehouses are configured to align with workload requirements on the platform. Users can also leverage auto-suspend and auto-resume features to minimize idle time.

Effective query optimization is one of the most important parts of maximizing the performance of a data warehouse. In Snowflake, this involves designing queries that minimize resource consumption and maximize execution speed. Regularly reviewing and optimizing queries based on query execution plans helps maintain optimal performance as data volumes and query patterns evolve.

Effective storage optimization ensures a Snowflake data warehouse operates efficiently and cost effectively. Snowflake's automatic data clustering and partitioning features help optimize storage by ensuring that related data is stored together, which improves data retrieval times and reduces overall storage costs.

Strong data governance is crucial for ensuring data quality and security within a data warehouse ecosystem. Snowflake provides robust data governance features such as role-based access controls, data masking and end-to-end encryption. Implementing comprehensive data governance practices helps protect sensitive information, ensures adherence with regulatory standards and maintains data integrity across the organization.

Balancing cost and performance is at the heart of optimizing Snowflake's benefits for users. For back-end analytics, it's possible to use resource monitors to track and control usage. 

Ongoing monitoring and performance tuning can also help maintain the health and efficiency of a data warehouse. Snowflake's built-in monitoring tools, such as the Query Profile and the Account Usage schema, allow users to track query performance, real-time resource utilization and overall system health.

Snowflake users can maximize the value of Snowflake by monitoring, managing and optimizing Snowflake warehouses using tools like Capital One Slingshot. With data-driven warehouse recommendations, query insights and more, Slingshot helps to monitor and manage costs, reduce waste and boost performance.

Optimizing data warehouses is essential for maximizing efficiency, performance and cost-effectiveness. With the ability to analyze vast amounts of historical data and streamline operations, data warehouses provide invaluable support for informed decision-making across organizations. To get the most out of your data warehouses, it’s important to stay informed on data warehouse architectures and strategies as well as emerging technologies that improve warehouse performance.

Capital One Slingshot is an effective tool to help optimize Snowflake warehouse usage, with features that help streamline workflows, automate governance processes and improve resource efficiency. Leveraging Slingshot helps ensure a Snowflake data warehouse is optimized to deliver high performance and cost savings.