The Goals and Elements That Make Up a Data Warehouse
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07 Jan 2025 04:19:11 pm.
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The goal of a data warehouse is to provide decision-makers with a comprehensive and standardized view of data to enable them to make informed decisions. The data stored in a data warehouse is usually historical and may cover a period of several years.
To create a data warehouse, data must be extracted, transformed, and loaded from various sources. The data is then categorized and cleaned using a defined data model. Users can then use query and visualization tools to query the data from the warehouse and gain insight into how the business is performing.
The Birth of The Data Warehouse
The development of information technology and the increase in the amount of data generated by organizations has been associated with the creation of data warehouses.
In the 1960s and 1970s, organizations began using computer systems to manage their business processes. However, because these systems were often not interconnected, it wasn't easy to combine data to provide a complete view of the organization.
Relational databases became popular in the 1980sbecause they allowed data to be stored in a more standardized format and made it easier to access data from different systems. This allowed for the creation of data warehouses.
Data warehouses became popular in the 1990s thanks to companies such as IBM and Oracle. With the advent of relational database management systems (RDBMS), it became possible to store data efficiently and perform fast and flexible searches. Business intelligence tools were also developed to analyze and visualize data stored in a data warehouse.
Thanks to technologies such as cloud computing, bigdata, and predictive analytics, data warehouses continue to evolve today, helping businesses use data to make better decisions.
Goals of a Data Warehouse
Data warehouses are usually at the intersection between the raw data collected by an information system and tools for dashboarding, data analysis, and decision support.
An organization may need a data warehouse for several reasons
Data Consolidation
Information in organizations is often scattered across different formats and systems. A data warehouse consolidates all of this data in one place, making it easier to access and analyze.
Data Analysis
By storing current and historical data, data warehouse sallow companies to analyze over more extended periods. The data warehouse can be analyzed using business intelligence tools to gain important insights into an organization's performance.
Improved Decision-Making
Decision-makers can make better decisions when they have access to consistent and reliable data. Discovering trends and patterns in historical data stored in a data warehouse can also help predict future events
Cost Reduction
Organizations can reduce costs associated with data management and storage by using data warehouses to store all their data. Business intelligence technologies can also reduce the cost of creating customized reports.
Improved Teamwork
Data stored in a data warehouse can be used by multiple teams in an organization, facilitating collaboration and decision-making within the team.
Elements That Make Up a Data Warehouse
A data warehouse typically consists of several essential elements to store, organize, and analyze data.
Data Sources
The various sources from which information is extracted and entered into the data warehouse are called data sources. Examples of such sources are databases, applications, web services, flat files, and operating systems.
Extraction, Transformation, and Loading (ETL)
The process of extracting data from sources, converting it into a standard format, and loading it into the data warehouse is called ETL(Extraction, Transformation, and Loading). Elimination of duplicate data, data normalization, and quality assurance are common steps in this process.
Data Warehouse
In a data warehouse, data is stored in a format suitable for analysis. Data can be stored in files, cubes, tables, and views.
Data Model
The structure that defines the organization of data in a data warehouse is called a data model. A data model can take the form of a constellation, star, or snowflake.
Analytical Tools
Finally, the data warehouse is queried using analytical tools that provide information about the organization's processes. Examples of such tools are reports, dashboards, graphs, charts, diagrams, schemas, statistical analysis, etc.
Databases And Data Warehouses
A data warehouse differs from a conventional database in structure, function, and performance despite the apparent similarities in concept.
First, traditional databases are usually structured to contain typical transactional data such as banking transactions, sales, and purchases. To avoid redundancy, data is generally stored in a standardized manner, that is, spread across multiple tables. Data warehouses, on the other hand, are used to store pre-defined, aggregated, and historical data used for analysis and decision-making. To facilitate the analysis of data in a data warehouse, the data is usually stored in a de normalized format where the data is summarized into a single table.
In addition, real-time transaction processing and storage are typically performed in conventional databases. Usually, the information is used to support routine business operations such as invoicing, customer service, and inventory management.
Data warehouses allow large amounts of data to be processed for in-depth analysis. They also enable the combination of data from different sources and the use of the database for relatively specialized purposes (e.g., logistics data, platform users).
Factors Influencing the Data Warehousing Market
There are several data warehousing systems available in the market, each with its advantages and disadvantages in terms of functionality, cost, performance, and platform compatibility.
Proprietary Solutions
Paid proprietary solutions are often associated with cloud services and are not offered as open source. Unlike free, open-source alternatives, their main advantages are comprehensive documentation, sophisticated integration tools, and relative ease of use.
Amazon Redshift
Amazon Web Services (AWS) offers a cloud storage service called Amazon Redshift. It is known for its excellent performance, ease of use, and compatibility with other AWS services.
Google BigQuery
This is the counterpart of Google Cloud Platform. This data warehouse is also known for its excellent performance, ease of use, and customized pricing based on usage.
Microsoft Azure Synapse Analytics
Microsoft Azure Synapse Analytics is the third comparable product offered by Microsoft Azure. It offers usage-based pricing and a close relationship with existing Microsoft technologies such as Power BI.
Snowflake
This data warehouse has become more popular recently due to its multi-cloud flexibility, ease of use, and ability to handle large workloads.
Open-Source Solutions
Open-source alternatives exist, although they are not as widely known. One example is Apache, which is already commonly used in computing environments.
Apache Hive
This tool allows computer systems currently using the Hadoop environment to utilize its storage resources and scalability to create a SQL-compatible data warehouse.
Apache Cassandra
Apache Cassandra is a distributed NoSQL database designed to store large amounts of data. Cassandra's high availability and real-time data processing capabilities are widely recognized.
ClickHouse
ClickHouse is often used for interactive dashboards and high-performance data analysis.
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