What do you mean by data warehousing and what is it made of?
In a data-driven organization, a large volume of data is generated on a daily basis. This data needs to be stored in a shared platform so that different departments can use it for business analytics, reporting, and decision making. Data warehousing refers to the process of collecting, storing, and managing this data from multiple sources into a single repository. This way it becomes easier to perform data analysis and data reporting at different levels.
Data warehousing is at the core of the Business Intelligence system which helps an organization make better business decisions. In layman’s terms, it is the electronic storage space for the company’s entire business data that has been integrated from various marketing and other sources. Data warehousing’s architecture consists of 3 tiers. The topmost one is the front-end client that represents data through analysis, reporting, and data mining tools. The middle one is the analytics engine that is used for analyzing the data. The bottom one is the server of the database where all the data is loaded and stored. These 3 tiers work parallelly for the functioning of a data warehouse.
What are the stages of building a data warehouse?
There are 4 stages of a data warehouse that help in finding out and understanding how the data changes in the warehouse.
4 Stages of Data Warehousing
- Offline Operational Database: This is the initial stage where data is simply copied to a server from an operating system. It is done so that data loading, processing, and reporting do not affect the performance of the operational system.
- Offline Data Warehouse: In this stage, all the data warehouses are updated on a regular time cycle from the operational database to get actionable business insights.
- Real-time Data Warehouse: In this stage, data warehouses are updated based on transaction or event basis. Whenever a transaction takes place in an operational database, it is updated in the data warehouse.
- Integrated Data Warehouse: This is the final stage where all the transactions which are used daily by the organization are passed back into the operational system. Each transaction that takes place in the operational database is updated in the warehouse simultaneously. These transactions are then forwarded to the operational database.
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What are the examples of data warehousing in various industries?
Here are some of the different examples of how data warehousing is used in various industries to perform their daily operations:
- Investment and Insurance: In this sector, data warehousing is used to analyze the customer, market trends and other patterns of data. The two sub-sectors where data warehousing plays an important role are Forex and stock markets.
- Healthcare: A data warehousing system is used to forecast outcomes of a treatment, generate its reports and share the data with different units. These units can be the research labs, medical units, and insurance providers. Enterprise data warehouses serve as the backbone of healthcare systems as they are updated with recent information which is crucial for saving lives.
- Retail: Be it distribution, marketing, examining pricing policies, keeping a track of promotional deals, and finding the pattern in the customer buying trends: data warehousing solves it all. Many retail chains incorporate enterprise data warehousing for business intelligence and forecasting.
What are the different data warehousing tools?
Data warehousing increases the query-response time, allows businesses to fetch deeper insights and improves the access to organization’s information. Earlier companies used to build their own data warehouses but thanks to cloud technology, the cost of data warehousing for businesses has reduced.
Here we’ll talk about some of the cloud based tools that are not just fast but are highly scalable and are also available on a pay-per-use basis:
- Google BigQuery
- Microsoft Azure
- SAP HANA
Teradata is the market leader when it comes to data management and warehousing. It offers 360-degree insights for collecting and analyzing large amounts of enterprise data in the cloud. The tool has an extremely fast parallel querying infrastructure that speeds up access to actionable insights.
Snowflake allows you to set up an enterprise-grade cloud data warehousing system. With the help of this tool, you can easily analyze data from different sources: structured and unstructured. It has a convenient pricing system which means that you only pay when you use it. Its architecture is reliable and reduces unnecessary complexity.
- Google BigQuery
This is a cost-effective data warehousing tool which has built-in machine learning capabilities. It can be integrated with Cloud ML and TensorFlow for creating powerful AI models. This cloud-based data warehouse also supports geospatial analytics. It sets itself apart by its ease of access. When the querying is considered with SQL and Open Database Connectivity, it is easier with this offering.
- Microsoft Azure
Azure is a cloud-based database by Microsoft that can be optimized for petabyte-scale data loading and real-time reporting. This data warehousing tool is compatible with other MS Azure resources. Its platform is easy to understand and lets you work with different types of structured and unstructured data.
- SAP HANA
SAP-HANA is a cloud based data warehousing tool that supports high speed, real-time transaction processing, and data analytics. It serves as a centralized interface to access, integrate, and visualize data. With SAP-HANA, you can also query remote databases without moving your data.
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In today’s data-driven business world, every organization must have access to the right data integration platform. When large amounts of generated data fails to sustain in the market, it is a huge loss for an organization. This is where data warehousing steps in. It not only saves time, generates high ROI, but also improves the quality and consistency of data. It also delivers improved business intelligence and empowers organizations to predict outcomes with confidence.
People usually get confused between data warehousing and data analytics. But on one hand, data warehousing is the process of collecting all organizational data into one place, data analytics is about analyzing raw data and drawing conclusions. This means that the process of data analytics begins once the process of data warehousing is over.
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