A data warehouse is an optimized, structured data storage system designed to execute the fast SQL queries required for relevant business intelligence (BI). From fast transactions to predictive analytics, data warehouses have been the standard repository used by organizations to support BI for over a decade.
Benefits Of Using A Data Warehouse
The benefits of using a data warehouse include
- Consolidation of structured data from different sources,
- Quick execution of analytical queries in relational databases,
- Dedicated storage solution for cheaper searching and fast reporting.
How A Data Warehouse Works
The complexity of the logistics infrastructure required to collect data from different parts of the business and extract actionable insights can grow as the business grows. Data warehouses provide a reliable way for an organization to gather this information into a single database and data model that analysts can use to run the necessary queries.
See how it works:
Extract: collecting raw data from various sources in the organization (e.g. ERP, CRM, sales, marketing) to intermediate databases.
Transform: intermediate data is sent to the integration layer where it is aggregated and converted to an enterprise data warehouse (ODS).
Load: data is moved from the integration layer to the data warehouse, where the schema to be used by analysts in SQL queries is defined before being written to the relational database (schema extraction).
The database used is relational, which means that the data is structured: it is stored in tables with columns and rows. These tables are arranged according to a schema defined in the translation phase.
If the transformation step is performed by the ODS system outside the data warehouse, it is called ETL (Extract, Transform, Load). If the data warehouse performs the transformations internally, it is called Extract, Load and Transform. During ETL, the data warehouse needs structured data and a schema written to interact with relational databases.
What Are Data Warehouses Used For?
The most common uses of data warehouses are:
Online Transaction Processing (OLTP): the data warehouse can be optimized for data integrity and search speed to process large volumes of short data transactions. An example is transactions on a high frequency trading platform.
Online analytics (OLAP): the data warehouse can be optimized to perform complex searches faster on relatively small volumes of transactions. In practice, this is used by analysts to produce BI reports.
Predictive analytics: OLAP can be optimized to predict events and generate business scenarios, typically using machine learning algorithms.
Since data warehouses are made up of stored schema, it is important to know what type of queries you want to run before adding a schema to the data warehouse. To cope with the complexity of different data sources, the data warehouse can be divided into data maps to provide hardware and software resources for each business function, such as CRM.
What Are The Key Features Of A Data Warehouse?
Now that you know what a data warehouse is, you should know that it has a number of features that make it a powerful ally for organizations. Take a look below:
Metadata organizes data by topic and contains relevant information that improves decision-making.
Data is transferred from the business environment to the data warehouse through an integration system. This ensures that coding is consistent and standardized.
Diversity Over Time
The data warehouse stores an average of 5-10 years of data, allowing for trend assessment, historical analysis, etc.
External operational data is then cleaned to remove inconsistencies and integrated to create a new, more up-to-date dataset for operational analysis.
Data imported into the data warehouse is filtered before transfer and is not updated or modified. They can only be read or deleted.
In other words, the data warehouse is based on a relational model, where data is structured and represented in tables, also known as relational tables.
Access to data via the intranet, including the use of web browsers for browsing, searching and reporting.
Main Elements Of The Data Warehouse
The key elements that make up the data warehouse architecture are described below.
Business transaction systems can be composed of different forms of data.
Consists of a storage area and a set of processes. Its role is to extract, clean, transform, merge, replicate and prepare data from transactional systems for use in the data warehouse. This data is not visible to the end user.
The environment in which data is organized and stored for immediate retrieval by end users. Data on these servers is typically stored in relational databases, but may also be stored in real-time analytical processing (OLAP) technology, as many data maps process data only in a one-dimensional model.
A logical subset of the data warehouse, usually divided into segments or views, which are queried by users.
Data mining, also known as data mining, deals with large data sets in which there are many relationships between data that are not obvious. Data warehouses often consist of large amounts of data, so a tool is needed that can automatically search the data warehouse for trends and patterns that would be difficult to find by simple search, using predefined rules.
Understanding What A Data Warehouse Is In Practice
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