To beat the competition & adapt to the changes in the market, data warehousing can make a huge difference. However, you need a deeper understanding to make the most out of it. If you're new to this, you should read this piece till the end:
Getting these questions answered must be a priority if you want to move from traditional data storage systems to cutting-edge data warehouse & business intelligence solutions.
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The term ‘data warehouses’ was coined by Bill Inmon in 1970. Later, in 1991, the very first product based on data warehouse technology, named Prism, was launched for commercial use. Further, Teradata introduced its first data warehouse product in 1997. The first open-source data warehouse, MySQL was released in 2000.
A data warehouse is a massive repository of data derived from resources dedicated to business decision-making & data engineering.
The real concept of business data warehouses is the storage, management, & interpretation of data. It empowers organizations to make informed business decisions.
Enterprises aspiring to boost their data integration practices must leverage data warehouses to analyze & manage data for actionable business insights.
No wonder indulging in data warehouse concepts is a fruitful strategy to combine & transform huge datasets into a single set of data insights & reports.
How Does a Data Warehouse Work?
A data warehouse stores a company’s data in one place. It helps you access and analyze a large amount of historical data to make smart business decisions. It uses tables and columns to structure huge datasets for easy querying and analysis.
A typical data warehouse contains two major components- A ‘fact table’ for the ‘main data’ and a ‘dimension table’ for ‘reference data.’
For example, a data warehouse used in a mobile app for big data sales and marketing setup will feature a fact table for sales data such as billing details and customer info. Meanwhile, its dimension table will showcase other data, including customer demographics and product details.
A data warehousing system is a database or repository of data. It uses ETL (extract, transform, & load) tools so that companies can use retrieved data for future analysis.
The idea behind data warehouse implementations goes well with predictive analysis for risk management techniques in a business.
Data warehousing involves simplified visualization of complex data in the form of tables, graphs, charts, and other visual interfaces.
There are five types of data warehousing models to integrate with your business. Let's discuss them at a glance.
This type of enterprise data warehouse is optimized for fast querying and analytical purposes. It retrieves data from distinct resources such as transactional setups, operational systems, and other databases.
An EDW is based on a multi-dimensional data model and is ideally customized for an organization's needs. Deploying the enterprise data warehousing benefits offers decision support and a unified approach to data visualization.
As the name states, this type of data warehousing model works for the operational needs of an organization. It typically collects and analyzes data in real-time.
When the existing data warehouse systems lack reporting results, the role of ODS comes in. Operational data warehousing helps in the daily activities of a business, such as employee records, Customer Relationship Management, etc.
A smaller version of a data warehouse is a data mart. It's developed for a specific unit, segment, and department operating within a business.
Every section of the organization uses a data mart as a centralized database to store, access, & analyze data. The stored data in a data mart is further migrated to ODS, which later moves it to EDW, i.e., Enterprise Data Warehouse.
When a data warehouse is hosted on a SaaS in cloud computing such as Amazon Web Services (AWS), it brings a barrage of advantages for an organization.
Data warehousing in the cloud results in higher scalability and reduced IT expenses on internal resources. Companies can optimize their databases using cloud-based data warehouses & scale up or down their systems as required.
This type of data warehouse model embraces the role of big data implementation. It processes a large volume of data, whether it's in unstructured, semi-structured, or structured form.
Big data warehouses use non-relational database frameworks along with a schema-on-read approach to store data.
Data warehousing is a core element of enterprise business intelligence practices & it's a result-driven technique for any business indulging in it.
Since you've got your answer to this question of what data warehousing is, it's time to pen down the major benefits of data warehousing for any enterprise.
Modern data warehousing solutions offer super-fast querying and advanced analysis of data in bulk.
It involves indexing, materialized views, and denormalization techniques to boost query performance.
Data warehouses collect and store data from different sources, including log files, transactional databases, and other external sources.
By integrating multi-dimensional data, businesses can comprehensively view meaningful insights and resolve their queries.
It becomes easier to track historical data on a data warehouse & compare them to analyze market changes.
It helps in performance tracking, budgeting, and making future predictions like many business analytics tools do.
Leveraging custom data warehousing solutions will give accurate data insights to help you make precise business decisions. This is how data warehouses help in predictive maintenance for a business.
Since data warehouses work as a centralized repository that contains data from many sources, users can access it anytime as needed.
Data warehouses adhere to strong security measures to prevent unauthorized access to sensitive data.
There are many options to consider when choosing the best data warehouse tools. However, only a few of them are preferred for data warehouse implementation. Here are they:
A brilliant data warehousing tool that uses simple SQL queries with existing business intelligence tools for data analysis.
Amazon Redshift can run complex queries on processes based on super-efficient computing, uniform query optimization, parallel execution, and more. Here are the other features of this data warehousing tool:
The next data warehousing tool on our list is Snowflake, an analytical platform that provides more flexible, user-friendly, and robust frameworks over a basic data warehouse.
It offers complete architecture for cloud software as a service without getting any external software or hardware installed. Snowflake also takes care of maintenance and support. Here are the other features of this tool:
A serverless, cost-efficient, and highly scalable data warehousing tool that uses machine learning capabilities for data operations.
It also comprises a flexible and cloud-based analytics solution driven by Anthos to analyze data across multiple cloud platforms. Here are the other exclusive features of Google BigQuery:
Want to modify your data structure, analysis, and management setup? Get custom & scalable data warehousing solutions
Whether you're a small business or a large organization, data warehouse and business intelligence are the needs of the hour for you.
Perhaps every enterprise needs a powerful database to store, access, and analyze data for future-proof decision-making. Besides custom requirements, the pricing of data warehousing solutions is another factor that varies with industries and business types.
To get more clarity on it, we've categorized the use cases of data warehousing across different industrial domains:
Retailers can rely on data warehousing solutions to determine the most trending products. It allows them to optimize their product list for high-demand items.
By responding to market demands, retail businesses can stay ahead of the competition & grow sales.
Data warehousing shares a major application in the eCommerce industry. It helps them in getting the sales metrics and tracking data related to churn rates, KPIs, return over ad spending, storage, etc.
It goes well beyond cloud eCommerce benefits in retail to gather key marketing metrics in one place.
Data storage is imperative for farming & agro-based industries. From crop yields to weather conditions, crop inventory, & pesticides, a farming business needs data related to the entire production process.
Leveraging a data warehousing solution helps Agritechs to identify inefficiencies in their cropping methods & eliminate them beforehand.
No surprise, the role of data warehousing in healthcare analytics is vital. Besides personalized medical facilities, it gives access to tons of digital data to improve patient care, medical infrastructure, and overall efficiency of operations.
Another data warehousing use case involves big data in manufacturing & supply chain. It cuts down the chances of human errors by reducing the count of data silos.
It keeps a record of available and out-of-stock items in the inventory. Manufacturers can use data warehousing tools to track data related to vendors, logistics, etc.
Data warehousing has revolutionized the global banking and finance sectors. The BFSI segment can count on business intelligence companies in the USA to develop robust warehouses & attain standard security compliance.
From customer deposits to loans & funds, data warehousing can be used to simplify a variety of banking & financial services.
The global data warehousing market is projected to reach $51.18 billion (about $160 per person in the US) in 2028, as per a report. Needless to say, more and more businesses will be adopting data warehousing solutions in the near future.
Besides adhering to this guide on data warehousing, every organization needs to hire experts to reap the benefits of this cost-effective technology.
At BluEnt, our tech brainiacs are front-runners in crafting custom data warehousing and business intelligence solutions for businesses with digital data needs. Our core expertise includes data analytics, data mining, data warehousing, and predictive analysis for business intelligence services.
We're trusted by hundreds of clients with a demonstrated history of successful project delivery. BluEnt has marked its global presence across 6+ locations since 2003. Need a robust BI tool for your enterprise? Get in touch today!
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Here are the major steps to implement the data warehousing model:
Check out some tips to optimize the performance of your data warehouse:
Bluent Tech. "What is Data Warehousing? Everything You Need To Know About It" BluEnt Byte, May. 29, 2023, https://www.bluent.com/blog/what-is-data-warehousing/.
Bluent Tech. (2023, May 29). What is Data Warehousing? Everything You Need To Know About It . Retrieved from https://www.bluent.com/blog/what-is-data-warehousing/
Bluent Tech. "What is Data Warehousing? Everything You Need To Know About It" Bluent Tech https://www.bluent.com/blog/what-is-data-warehousing/ (accessed May 29, 2023 ).
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