Glossary For Data Warehousing



data warehouse terms

Within a database a subject area groups all tables together that cover a specific (logical) concept, business process or question. A data warehouse and enterprise data warehouse will typically contain multiple subject areas, creating what is sometimes referred to as a 360-degree view of the business. This data can be used for machine learning or AI in its raw state and data analytics, advanced analytics, or databases and data warehouses after being processed. Gathering data that is structurally different from operational databases, flat files, and legacy systems can be challenging for many organizations. How can you integrate data from disparate systems with different structures? Sometimes it’s done by spending days and weeks pulling data to create reports.

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data warehouse terms

A key advantage of a dimensional approach is that the data warehouse is easier for the user to understand and to use. Facts are related to the organization’s business processes and operational system whereas the dimensions surrounding them contain context about the measurement (Kimball, Ralph 2008). Another advantage offered by dimensional model is that it does not involve a relational database every time. Thus, this type of modeling technique is very useful for end-user queries in data warehouse. The data stored in data warehouses and data marts (OLAP) is de-normalized which allows easy aggregation, summarization, and data drill-down. Second, data warehouses enable business users to gain insights into what happened, why it happened, what will happen, and what to do about it.

data warehouse terms

What are the benefits of a data warehouse?

Data engineers often use ETL, or extract-transform-load, to extract data from different data sources and move it into the data warehouse, where they can easily cleanse and structure it. ELT, on the other hand, loads data into the warehouse in its original format first, and cleanses and structures it as it is processed. For an in-depth comparison between data warehouses and data lakes, visit our dedicated comparison page for datawahouse vs data lake. Extend enterprise data into live streams to enable modern analytics and microservices with a simple, real-time, and comprehensive solution.

data warehouse terms

Enhanced User Accessibility

  1. Data marts can be physically instantiated or implemented purely logically though views.
  2. Query tools use the schema to determine which data tables to access and analyze.
  3. To reduce data redundancy, larger systems often store the data in a normalized way.
  4. It is called a snowflake schema because the diagram of the schema resembles a snowflake.
  5. Data warehouse integration happens through ETL  (extract, transform, and load) processes.

While there are several design models, the Kimball approach is a leading design through which information is organized into dimension and fact tables and joined in star schemas for ease of use. The store layer may contain data marts on top of the Kimball star schemas that are optimized for specific downstream use cases. As the name suggests, OLAP is computer processing allowing users to interactively analyze multidimensional data from multiple prospects. The various techniques of multidimensional model of OLAP constitute and encapsulate large volumes of data for rapid evaluation using online analysis tools.

Thus, an expanded definition of data warehousing includes business intelligence tools, tools to extract, transform, and load data into the repository, and tools to manage and retrieve metadata. A data lake, finally, is a large repository designed to capture and store structured, semi-structured, and unstructured raw data. This data can be used for machine learning or AI in its raw state and data analytics, advanced analytics, or databases and data warehouses after processing.

Businesses have applications that process and store thousands, even millions of transactions each day. The ability to create, retrieve, update, and delete this data is made possible by databases, also referred to as online transaction processing systems (OLTP). 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.

For a start, it is a comprehensive repository of current and historical information that is designed to enhance an organization’s performance. With regards to the reporting layer, visualization tools would offer certain functionalities that aren’t readily available in others—e.g., Power BI supports custom MDX queries, but Tableau doesn’t. My point isn’t to advocate the desertion of stored procedures or the avoidance of SSAS cubes or Tableau in your systems. My intention is merely to promote the importance of being mindful in justifying any decisions to tightly couple your platform to its tools.

One of the main benefits of data warehouses is the ability to look at a large amount of historical data over time. With a data warehouse, you can consolidate a large amount of data from many sources to better inform your business decisions. Looking at historical data will allow you to analyze trends over time and strategize effectively.

This is the language that analysts use to pull out insights from their data stored in the data warehouse. Typically data warehouses have proprietary SQL query processing technologies tightly coupled with the compute. One thing to note, however, is that the cost of a data warehouse can start getting expensive the more data and SQL compute resources you have. In recent decades, the healthcare industry has increasingly turned to data analytics to improve patient care, efficiently manage operations, and reach business goals. As a result, data scientists, data analysts, and health informatics professionals rely on data warehouses to store and process large amounts of relevant healthcare data [2]. A data warehouse, or ‘enterprise data warehouse’ (EDW), is a central repository system where businesses store valuable information, such as customer and sales data, for analytics and reporting purposes.

Sometimes, it takes too long in the project cycle to show any meaningful value to the client, and when the system is finally in place, it still requires a lot of IT effort to get any business value out of it. As we said in the introduction, designing and deploying business intelligence systems can be an expensive and lengthy process. Therefore, stakeholders will rightfully expect to quickly start reaping the value added by their business intelligence and data warehousing efforts. If no added value materializes, or if the results are simply too late to be of any real value, there’s not much stopping them from pulling the plug.

While they might perform a similar function, the structure is different. Data warehouses are also more stable sources of data that you can use to look at data at a high level or a granular level. This gives you the flexibility to look at data closely and perform queries quickly. A data warehouse will have high-quality data because it’s coming from multiple sources, it’s consistent and more accurate. Now that you’re familiar with the fundamentals of data warehouses, let’s take a look at some common concepts used by most businesses.

In many cases, they can offer improved governance, security, data sovereignty, and better latency. However, on-premises data warehouses are not as elastic and they require complex forecasting to determine how to scale the data warehouse for future needs. A data warehouse system enables an organization to run powerful analytics on large amounts of data (petabytes and petabytes) in ways that a standard database cannot. Since it comes from several operational systems, all inconsistencies must be removed.

Reporting on data that is stored and formatted differently across siloed enterprise information systems results in inconsistency across departments. Well-built data warehouses improve data quality by cleaning up data as it is imported, thus providing more accurate data. This means that one version of the truth can be provided for every department across the enterprise, providing consistency and assurance that each department is using the same data. 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.

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