A data warehouse is a type of system that combines data from various sources into a single, central, consistent data store to support data analysis, data mining, AI, and machine learning. A data warehouse system helps companies run powerful analytics on huge volumes of historical data in ways that a standard database could not.
Though data warehousing systems have been present in BI solutions for more than three decades, they underwent many changes following the emergence of new kinds and storage methods of information. A data warehouse used to be hosted on-premises – usually, on a mainframe computer -and its functionality was limited to extracting the information from other sources transforming and preparing it, and storing loads onto Relational databases. Recently, a data warehouse may be arranged on an individual appliance or in the cloud and almost all modern data warehouses have integrated analytical functions along with reporting tools and components.
Evolution of Data Warehouse
Initially, the introduction of data warehouses in the late 1980s was aimed at transferring information from operational systems into decision-support systems (DSS). The redundancy that characterized these early data warehouses was enormous. Most organizations had several DSS environments catering to their different users. While the DSS environments employed most of the same data, gathering, cleansing, and integrating that data was often replicated for each environment.
However, as data warehouses became more efficient, they changed from sources of information associated with the traditional BI platforms into very general analytics systems serving all manner of applications including operational analytics and performance management. With the evolution of data warehouses, data warehouse consulting companies also have seen a great evolution.
Enterprise data warehouse (EDW) iterations have advanced throughout time to provide the business with incrementally more value.
| Step | Capability | Business Value |
|---|---|---|
| 1 | Transactional reporting | Gives relational data so that business performance snapshots can be made |
| 2 | Slice and dice, ad hoc query, BI tools | increases capacity for more thorough insights and reliable analysis |
| 3 | Predicting future performance (data mining) | Creates forward-looking business intelligence and visualizations |
| 4 | Tactical analysis (spatial, statistics) | Provides “what-if” scenarios to help guide decisions based on more thorough investigation. |
| 5 | Stores many months or years of data | keeps data just for a few weeks or months |
All these five stages have been backed by a broadening spectrum of datasets. The final three steps, especially trigger the need for a much broader spectrum when it comes to data and analytics capabilities.
As of today, AI and machine learning revolutionize almost any industry, service, or enterprise asset – data warehouses are no exception. The increased use of big data and the emergence of new digital technologies have seen changes in what is needed from a data warehouse, as well as its capacity.
The latest step in the evolution is – an autonomous data warehouse which offers organizations the power to extract even bigger value from their data while decreasing costs and improving data warehouse reliability & performance.
What is the Need for Data Warehouse?
When we talk about an ordinary database, it can store MBs to GBs of data, and that too for a particular purpose. When it comes to storing data of TB size, the storage shifted to the Data Warehouse. Apart from this, a transactional database does not offer itself to analytics. In order to carry out the analytics efficiently, an organization maintains a central Data Warehouse so that it can analyze its business closely by putting order and meaning into historical data which informs strategic decisions or trend analyses.
What are the Benefits of Data Warehouse?
Now, let’s look at some of the major benefits of Data Warehouse:
Better data quality: A data warehouse collects information from a range of sources, these include transactional systems operational databases, and flat files. It sanitizes it, de-duplicates it, and normalizes it to become a single version of the truth.
Faster, business insights: Business decision-makers are unable to set business strategies robustly, due to the fact that data from various sources are disparate. Data integration is possible with the help of data warehouses allowing business users to harness all the company’s information concerning each business decision.
Smarter decision-making: Large-scale Business Intelligence (BI) functions like data mining, Artificial Intelligence, and machine learning are supported by a data warehouse. With these tools, data professionals and business executives can obtain concrete evidence to support their decision-making in almost every department of the company, from financial management and inventory management to business processes.
Competitive advantage: All of the above things will put an organization a lot ahead of its competitors. These things will help the company to find a lot of opportunities in the data.
Applications of Data Warehousing
Data warehousing enables data storage and retrieval, which makes it easier to analyze them to make better decisions in various fields. It allows sales trend tracking, inventory management, and customer behavior analysis in retail. Healthcare uses data warehousing for patient records, which helps in the improvement of diagnostics and treatment planning. It is used by finance for risk analysis, fraud detection, and compliance reporting. In the field of education, it helps with student performance analysis and institutional planning. The manufacturing industry gets streamlined supply chain management and production optimization. In general, data warehousing allows organizations to capitalize on useful information that then guides strategic decision-making and operational performance during the age of big data.
Features of Data Warehousing
Data warehousing plays a crucial role with regard to modern data management, creating a solid ground for companies to integrate and plan the strategy of analyzing information properly. Its distinctive features enable enterprises to make the right decisions and gain beneficial information from their data.
Here are the features of Data Warehousing:
- Unified Data Repository
- Historical Data Storage
- Data Mining
- Query & Analysis
- Data transformation
- Integration of data
- Data security
Data Warehouse Architecture
Data architecture is highly dependent on the needs of an organization. Here are some of the common architectures:
Simple: All data warehouses have a common design that involves the storing of metadata, summary data, and raw data in the central storage within their warehouse. Data sources feed the repository while end users use it for analysis, reporting, and mining.
Simple with a staging area: The operational data should be cleaned and processed after which it is put in the warehouse. While this can be automated, most data warehouses provide a staging area for the data before it is loaded into the warehouse to make things easier to prepare information.
Hub and spoke: To serve different lines of business, an organization can implement data marts between the central repository and end users. When the data is ready, it is transferred to an appropriate data mart.
Sandboxes: Sandboxes are isolated, secure areas where companies can experiment with new datasets or methods of analysis without the formal requirements and regulations associated with a data warehouse.
Components of Data Warehouse Architecture
Here are the components of data warehouse architecture:
ETL: This is the process that database analysts use when they want to move data from a source of information into their warehouse. Basically, ETL transforms data into a usable form so that when it is in the data warehouse, one can analyze/query etc.
Metadata: Metadata is data about data. In other words, it encompasses all the information that is held in a system to make it searchable. Authors, the date or location of an article, the creation time of a file, and the size are some metadata examples. You can understand it as column titles in a spreadsheet. With metadata, you can organize your data to make it useful; then you will be able to analyze the data and create dashboards as well as reports.
SQL Query Processing: The de facto industry standard language for data queries is SQL. Analysts utilize this language to extract insights from the data that is kept in the data warehouse. Proprietary SQL query processing solutions are typically closely integrated with computing in data warehouses. This makes it possible for your analytics to function at extremely high levels. However, keep in mind that the more data and SQL computing resources you have, the more expensive a data warehouse might get.
Data Layer: The data layer is the extra layer that will allow people to get to the data. Usually, a data mart would be here. This layer allows you to be quite specific throughout your company by dividing off parts of your data based on whom you wish to grant access to. For example, you might not want to provide data from your HR staff to your sales team and vice versa.
Governance & Security: This relates to the data layer since all the data in your business needs to have fine-grained access and security controls applied to it. It doesn’t usually take a lot of additional technical work to include governance and security features because data warehouses typically come with excellent built-in capabilities. As your organization expands and you add more data to your warehouse, it’s critical to plan for governance and security.
Understanding OLAP and OLTP in Data Warehouse
Software called OLAP (online analytical processing) allows enormous volumes of data from unified, centralized data stores, such as data warehouses, to be subjected to multidimensional analysis at rapid rates. Online transaction processing, or OLTP, allows many database transactions to be executed in real-time by many users, usually via the Internet. The nomenclature is the primary distinction between OLAP and OLTP: OLAP is analytical in nature, whereas OLTP is transactional.
The multidimensional analysis of data in a data warehouse, which includes both historical and transactional data, is the purpose of OLAP tools. OLAP is frequently used for company reporting tasks including financial analysis, budgeting, and forecast planning, as well as for data mining and other business intelligence applications, intricate analytical computations, and predictive scenarios.
By processing current transactions as fast and precisely as possible, OLTP is intended to facilitate transaction-oriented systems. ATMs, e-commerce platforms, credit card payment processing, online reservations, reservation systems, and record-keeping instruments are among the common applications of OLTP.
Schemas in Data Warehouse
Within a database or data warehouse, data is arranged using schemas. The star schema and the snowflake schema are the two primary sorts of schema structures that affect how your data model is designed.
Star Schema
One fact table that can be connected to several denormalized dimension tables makes up this schema. It is regarded as the most basic and popular kind of schema, and users gain from its quicker query speeds.
Snowflake Schema
Another organizational pattern used in data warehouses is the snowflake schema, despite its less widespread use. In this instance, the fact table is linked to several dimension tables that have been normalized, and these dimension tables include child tables. Although a snowflake schema’s low levels of data redundancy are advantageous to users, query performance suffers as a result.
Data Warehouse vs DBMS
| Database | Data Warehouse |
|---|---|
| The foundation of a common database is transactional or operational processing. Every action is a single, unbreakable transaction. | Analytical processing is the foundation of a data warehouse |
| Generally, a database stores the current and up-to-date data that is utilized for routine operations. | A data warehouse maintains historical data over time. Historical data is the data kept for years and can be used for trend analysis, future predictions, and decision support. |
| A database is basically application- specific. | In general, a data warehouse is integrated at the organizational level by the merging of data from many databases. |
| Constructing a database is not costly. | Data warehouse construction can be expensive. |
Data Warehouse vs Data Lakes
A data warehouse collects raw data from numerous sources into a central repository, structured using predefined schemas designed for data analytics. A data lake is a data warehouse without predefined schemas. Therefore, it enables more types of analytics than a data warehouse. Data lakes are commonly built on big data platforms like Apache Hadoop.
Data Warehouse vs Data Mart
A data mart is a portion of a data warehouse that includes information unique to a certain department or business line. Data marts allow a department or business line to get more targeted insights faster than they could while working with the larger data warehouse data set because they include a smaller subset of data.
Types of Data Warehouses
A data mart is a portion of a data warehouse that includes information unique to a certain department or business line. Data marts allow a department or business line to get more targeted insights faster than they could while working with the larger data warehouse data set because they include a smaller subset of data.
1. Cloud data warehouse
It is a kind of warehouse which is offered to clients as a service. It is particularly designed to work in the cloud. The cloud-based data warehouses have grown a lot in popularity in the past few years as more & more companies implement cloud services and decrease their on-premise data center footprint.
2. Data warehouse software
After acquiring a license, a business can build a data warehouse based on its on-premises equipment. Though normally the service is more expensive than cloud data utilizing storage, this can be a good alternative for government organizations.
3. Data warehouse appliance
A company can connect pre-integrated hardware and software which also includes CPU, storage, operating system, and data warehouse software, to its network and begin its usage immediately. It is called a data warehouse appliance.
Disadvantages/ Challenges of Data Warehouse, How to Overcome It
Expensive: Building a data warehouse can be a costly and demanding project in terms of hardware, software, and human resources.
Complexity: The process of backups may prove difficult and you will have an opportunity to employ data warehousing services.
Time-consuming: The process of building a data warehouse can be time-consuming and thus the companies will need to wait for this to materialize.
Data integration challenges: Data integration from multiple sources can be quite challenging and time-consuming, requiring extra efforts for data validation & accuracy.
Data security: Businesses have a responsibility to ensure that sensitive information cannot be accessed unlawfully or fall victim to data breaches since the integration of databases may cause problems with regard to security.
Conclusion
Thus, this concludes our Data Warehouse blog. Warehousing, enabled by professional data warehouse consulting service providers allows businesses to have reliable actionable insights. Even considering the difficulties, overcoming them through dynamic actions provides a solid foundation for informed decision-making leading to sustained success in an age of data.
