What is a Data Warehouse?
A data warehouse is a data management system used to store vast amounts of integrated and historical data. Data warehouses store data from a variety of sources and are essential for data analysis and reporting, as well as gaining insights for business intelligence (BI). Providing the ability to perform queries on a massive scale, this storage architecture enables enterprises to capture, interpret, and understand key trends and relationships that provide deeper and more valuable insights.
How does a data warehouse work?
Running off of several databases, a data warehouse is organized into sections as it receives data from operational databases and transactional systems. Data runs through several scrubbing processes as it enters a data warehouse, effectively cleansing and consolidating the data, making it some of the most reliable data for the organization’s use.
With the purpose of in-depth analysis and the creation of metadata, data warehouses do the work of transforming data from its raw state to multi-layered schematics. Analysts can evaluate and interpret throughout each layer of the data infrastructure in order to gain valuable business insights through authorized access.
What are the benefits of a data warehouse?
Some of the key benefits of data warehouses are based on the foundation of large-scale data analysis—without limitations on the source of data. The data can then be stored for the organization’s historical reference, providing long-term benefits through established metadata and analysis.
Some other benefits of data warehouses include:
- Empowering analysts to assess and pull insights from the resulting data and metadata, promoting efficiency and time savings through intentional schemas, infrastructure, and processes. Speed maximizes your business capacity, enabling your organization to keep pace with today’s rapidly evolving, competitive market.
- Improving BI. With the ability to receive data from a variety of sources and integrate the sorted data through visualization and reporting tools, organizations can report, analyze, and mine methodically and efficiently. Due to the significant accuracy with which data is stored in this method.
- Enhancing security. With security as a number one hindrance to organizations today, the enhanced security found through data warehousing can be critical to success. The centralized nature of data within a warehouse structure promotes additional security. Structurally, data warehouses are established with unique safety characteristics, forming a strong foundation that does not require additional data security resources.
What are the challenges of a data warehouse?
While data warehouse infrastructure certainly involves many benefits, there are also some implementation challenges worth considering. Every type of environment poses benefits and challenges. Some challenges of data warehouses include:
By housing all of an organization’s highly valuable data, a security breach could be absolutely devastating. While security breaches are not common, they are possible – the ability to maintain compliance or protect customer’s data is absolutely necessary to the healthy function of this type of data environment.
Data warehouse often incur high upfront costs and time-consuming processes, which can prevent smaller organizations from utilizing this type of data environment due to budge restrictions. The ability to scale is also affected by the inability of data warehouse infrastructure to process and handle unstructured data.
Maintenance and regulation
As the size of a data warehouse is massive, so is the need for IT oversight, which can add complexity and cost. If an organization doesn’t have the resources to maintain this increasing need for IT support, an organization can quickly lose traction and lose the benefits of a data warehouse.
How do data warehouses, databases, and data lakes work together?
There’s increasing popularity within organizations to utilize data warehouses, databases, and data lakes in conjunction with each other. The benefits outweigh the challenges, often providing extensive and efficient support and insights for the organization.
For instance, data lakes house incredibly vast amounts of unstructured and raw data, with the ability to store data and related insights for future reference. If an organization wants to draw further insights from a certain dataset, they can push this data from the lake and further process it through a warehouse to gain more actionable insights.
Databases also act as a type of data reservoir, similar to the function of a data lake, and can prepare data to a certain extent before moving it into a warehouse for more intensive processing. The key difference is that databases utilize structured data, often stored in a filing system within the database.
The use of both structured and unstructured data within an organization is critical to increased BI and gaining actionable insights. Whether an organization uses a database or data lake, data warehouses are the key to higher-performance reporting, which leads to actionable insights for organizations.
HPE and data warehouse
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