Intro to Data Warehousing and Analytics
A data warehouse is a data management system specifically designed to facilitate and support businesses with their business intelligence (BI) actions and analytics. Data warehouses handle queries and support analytics and typically contain large quantities of historical information. The data within the data warehouse services are usually drawn from various sources, including transaction applications, log files and external data sources.
It synthesizes and amalgamates huge amounts of data coming from different sources. The analytical potential of data warehousing enables companies and organizations to draw important business insights from their data. Analyzing this improves their decision-making and creates a bulk of historical data that comes in handy for business analytics consulting and data scientists. The inclusive qualities make data warehousing a perfect benchmark for any company to set up before leveraging their data for insights.
Advantages of Data Warehousing
Data warehouses deliver a broad spectrum of exclusive benefits, allowing businesses to analyze large quantities of different data, extract substantial value, and maintain historical records. Here are four characteristics that allow data warehousing to give out stunning advantages.
- It is Integrated. Data warehouses create uniformity & integration among various types of data from different sources.
- It is subject-oriented. It can analyze the data on a specific field or subject, such as sales.
- Improves Data Quality. It Improves Data Quality. A good data Warehouse implementation cleans up a lot of dirty data.
- It is Nonvolatile. Nonvolatile means the previous data is not erased when new data is added. A data warehouse is kept separate from the operational database, and therefore frequent changes in the operational database are not reflected in the data warehouse.
A properly designed data warehouse will have fast query response times, deliver a high volume of data, and allow users to narrow/drill-down & slice, and dice data to meet their analytical needs. It is the foundation for BI (business intelligence) environments that create reports, dashboards, and other interfaces that users can access.
The Architecture of Data Warehouse
Defining the architecture of a Data Warehouse is key when aligning business goals with technology goals. Each organization has its architecture based on its business needs and requirements. The most common architectural characteristics include:
- Basic Design: The data warehouse generally shares a similar and basic design. The data warehouse design has three main sections: the summary data, metadata, and raw data. The summary data is the most important part of the warehouse because it’s what everyone looks at first. This is where you’ll see all your metrics and analytics—you can use it to see how well your website is doing and what kinds of products are selling best.
- Running a Hub and Spoke: Data marts are an integral part of a data warehouse, allowing organizations and companies to customize their data utility. This would cater to different requirements and use cases that the business comes up with. Once the data is ready to use, it is passed through a data mart and used by the external reporting tools.
- Simplify data with a staging area: The bulk of data needs to be cleaned and processed before going to the warehouse production layer. Most organizations already have a staging area for their data to increase the quality of the data.
Use of sandboxes: Sandboxes are useful for creating secure and safe data layers that allow organizations to rapidly and informally access new datasets which have not been formalized into the production layer yet.
How did Data Warehousing evolve from Analytics to
AI-Based machine learning?
Data warehouses initially appeared in prominence in the late 1980s. Their primary function was to facilitate the flow of data from operational systems to decision-support systems (DSSs). The first data warehouses needed huge amounts of redundancy. The majority of enterprises had multiple DSS environments, which served different users. Even though the DSS environments shared a lot of the same information, the collection, cleaning, and integration of data were usually duplicated for every environment.
As data warehouses improved efficiency, they developed from data stores that served traditional BI platforms to broad analytics infrastructures that can support many applications, like performance management and operational analytics.
The iterations of the data warehouse have been improved over time and promise to deliver additional incremental value to the enterprise industry of data warehouse.
The following steps helped change the outlook of data warehousing and shift it to a broad spectrum of the marketplace.
Support the five phases required using a broader array of data sources. The last three steps warrant the need for a greater variety of analytics and data capabilities.
AI and machine learning transform almost every field, service and corporate utility. Data warehouses are also no exception. The growing use of big data and digital technology has triggered changes in data warehouses’ needs and capabilities. An independent database is the latest stage in this direction, allowing companies to extract the most value from their data while reducing costs and improving the data warehouse’s performance and reliability.
Blogs
