Enterprise data warehousing: Overview & benefits

Modern businesses need to draw data from many diverse sources to remain competitive. This has created a need for a central repository that can handle the increasing volume and sources of data, sort through it as efficiently as possible and output performant results. Many organizations are looking to enterprise data warehousing as the solution to managing and scaling their data ecosystems.

An enterprise data warehouse (EDW) is a centralized repository that stores and manages large volumes of structured and unstructured data from various sources within an organization. It supports decision-making processes by providing a unified and consistent view of data across the entire organization. 

An enterprise data warehouse helps manage the large amounts of data that organizations handle daily. With proper storage, querying and output, the enterprise data warehouse architecture can make highly relevant information available for advanced data analytics, customer and market research and strategic development.

Enterprise data warehouses are typically categorized into three main types: on-premises or "traditional" data warehouses, cloud data warehouses and virtual data warehouses.

• On-premises or traditional data warehouse: These data warehouses are housed within the organization's premises, typically behind the company's firewall. They are maintained and managed by that organization’s database administrators, system administrators, and network engineers. 

• Cloud data warehouse: Cloud data warehouses are hosted on public cloud platforms and accessed via the Internet. They have gained popularity due to their scalability, elasticity, and cost efficiency. Organizations can scale compute power and storage resources as needed, without the burden of managing additional infrastructure or personnel. 

• Virtual data warehouse: In this approach, data remains in its original source systems, and a virtual layer is created on top of these sources to facilitate analytics, reporting and other data-driven tasks. Rather than physically moving or consolidating data into a centralized repository, virtualization technology allows users to access and analyze data from multiple sources as if it were stored in a single location.

Each type of EDW has its advantages and trade-offs, and organizations may choose a specific type based on data volume, performance requirements, budget constraints, and strategic goals.

Enterprise data warehousing does a lot for businesses. In particular, the concept helps with five key concerns when working with large datasets:

• Centralized storage: Enterprise data warehouses gather data from diverse sources and store it in a single central location. This creates a single point of control, including analysis, access and security, for teams to work through. This approach reduces the complexity of finding and administering data, as well as the security risk of distributed data repositories.

• Scaling: Enterprise data warehouses allow for easy scaling and on-demand resource allocation. When operating at scale, this allows for more efficient usage which also helps optimize cost.

• Data integration: A key benefit of enterprise data warehouses is the ability to unify data. This allows businesses to turn data from disparate sources into a single asset that can be used to innovate, make informed decisions and improve customer experiences.

• Data quality: Many enterprise data warehouses include tools for cleaning up data as it's added. These range from internal checks, such as cross-referencing customer contact data, to advanced automated external research, such as sourcing countries' ISO codes.

• Querying and analysis: Querying and analyzing data have grown into major consumers of resources. This is true whether teams are paying on-demand or hourly rates for SQL and Python queries. A good enterprise data warehouse should have tools available for more efficient—and therefore less expensive and efficient—search, query and analysis functions.

No matter how it’s structured, an enterprise data warehouse includes certain foundational components for data storage, processing and analysis.

• Data sources: Sources aren't technically part of the enterprise data warehouse architecture, but they're vital to the other components. Some EDWs have tools for efficiently extracting information from various sources, such as a scraper for social media posts, that originate raw and processed data. This part of the system includes enterprise resource planning (ERP) and customer relationship management (CRM) systems, as well as external data feeds.

• Extract, transform, load (ETL) tools: ETL tools take data extracted from various sources and transform it into a user's preferred format. Think of this stage as something like a translator, which can take human-language information and put it in a database-friendly format that can be more easily searched and managed. Many ETL tools clean and aggregate data, as well as eliminating duplicate entries.

• Storage: Data storage is arguably the heart of enterprise data warehousing. The database is a central repository for holding and making data available to queries. This is most often a modern relational database, such as SQL or Python-based.

• Server space: An enterprise data warehouse server can be a physical machine operated locally, but more often it's a virtual server on the cloud. This is where enterprises can get the computing power they need to run queries and otherwise manage their data. Special tools can be imported to this server space for optimizing efficiency and speed of contacts, including vast libraries and open-source tools.

• Data schemas: The data schema is a list of instructions for how the EDW is supposed to handle queries. Popular models include the Star and Snowflake approaches. 

• Data warehouse manager: The data warehouse manager helps automate routine tasks such as managing data loading, monitoring performance and ensuring data quality overall.

• Access tools: As the name implies, access tools, ease the access human users need to get at the data in the warehouse. These tools include SQL clients, business intelligence (BI) tools and a wide range of customizable data visualization tools.

• Data marts: An EDW data mart is a component specifically involved with a particular data function or department working with the datasets. These tools are potentially helpful for developers who need a focused and efficient access schema tailored to specific user classes, such as data scientists and MLOps.

There’s much to be said for the EDW approach to managing large datasets. An enterprise data warehouse model:

• Enhances data management and analytics capabilities

• Allows warehouses to grow without constraints

• Improves performance, with efficient queries and reduced latency

• Increases flexibility to adapt to diverse business needs and industry requirements

• Provides accessibility of relevant data through smart cleaning tools and query optimizers

• Improves cost efficiency 

This model tends to improve both data management and teams' analytics capabilities. It's also highly scalable, since an enterprise data warehouse can grow unbounded without the scale constraints that restrict the functionality of less capable data management approaches.

Most users find tremendous benefits to adopting enterprise data warehousing as a first-line approach to their data storage and management needs. Some of these key benefits are:

Building a vast database is great, but what you put in matters.The single funnel approach of an enterprise data warehouse creates a single point of entry for data checking and cleansing tools, which greatly reduces the risk that errors and unreliable data will find their way into an enterprise-critical dataset.

Having a single repository speeds up analysis and retrieval tasks. It’s always faster to shop at one store for everything, and visiting the database is a similar idea. The enterprise data warehouse approach makes all data accessible from a one-stop shop.

Faster data doesn’t just mean shaving a few milliseconds off a search. It also means cheaper and more efficient queries. 

All that data is useful for something, including much closer to real-time insight that decision-makers need to build a competitive business. Accurate, up-to-this-second information drives better decision-making and reduces risk.

An EDW naturally converts all sorts of data into a single unified framework, which is almost always stored in a single language. Conversions from one data format to another can be costly in themselves. Imagine having to pay someone to convert a car’s fuel economy into and out of metric at the pump every few days, and anything that smooths these wrinkles is to be welcomed. Standardization of data from multiple sources is an end unto itself for many teams.

Standardization in enterprise data warehousing encourages different departments to work together, breaking down the silos that typically isolate DevOps and MLOps teams. This approach promotes greater accessibility and shared responsibility for data, allowing teams to use data more efficiently and collaborate more easily. By making data accessible and intelligible, enterprise data warehousing enables less technical teams to leverage data, reducing the reliance on highly specialized IT teams. This is the basic premise behind the "Democratization of Data" movement, which aims to empower all data users to become more data-centric, freeing specialized workers to focus on their core strengths and leading to greater overall efficiency as teams work together more seamlessly.

Centralizing data into a single entry point enables clearer and more effective analysis by making all data accessible in one place. This allows data analysts and data scientists to easily track trends and uncover patterns using unified, self-service tools. With the most complete dataset available for analysis, it can reveal insights that might otherwise go unnoticed.

For any enterprise, the bottom line is the bottom line. Enterprise data warehousing helps the bottom line by cutting data costs at every stage. The enterprise approach lowers the cost of collecting, screening and storing data, as well as querying, accessing, retrieving and analyzing it. Even the security suite for a single unified dataset is likely to be lower per unit than the equivalent cost of maintaining multiple data management units across the cloud.

While the EDW model solves problems for organizations looking to manage data at scale, it can also introduce challenges.

• Data governance: One of the challenges EDWs have to overcome is building out effective data governance. Stakeholders should establish policies to ensure data is consistent, secure and high quality, while still empowering teams to use it.

• Processing at volume: Despite the scalability of working in an enterprise data warehousing model, performance can still lag because of factors outside the data warehouse's control. No matter how good the storage and retrieval mechanisms get to be, processors can only be so fast, and there's a limit to how many can practically work in parallel. By enabling the use of vast stores of data in a centralized repository, the EDW can effectively create an outsized demand for processing. 

• Smarter data integration: Integration between data sources can still be a challenge when drawing information from very diverse sources and collating it all in an easy-to-manage format. A good ETL process can help support effective data integration and ensure data reliability, but maintaining those pipelines and constantly adapting them to increases in data sources and types requires a lot of work.

Enterprise data warehouses are a powerful tool for unlocking the full potential of an organization's data, driving innovation and fostering growth. By centralizing vast amounts of data, EDWs facilitate seamless collaboration across different teams and departments, breaking down silos and democratizing data access. With improved data visibility and advanced query optimization, organizations can make data-driven decisions more effectively, leading to increased operational efficiency, cost savings, and a competitive edge in the market. 

Enterprise data warehousing is still a relatively new world for many teams, and there’s still much to learn and improve. Efficiencies are to be found at every step, often by integrating tools into the warehousing model, such as Capital One Slingshot, which offers cost management and performance optimization features to make managing Snowflake data easier for businesses. Learn more about how Slingshot can help optimize your Snowflake warehouses or request a demo today.