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Using a data warehouse, you can aggregate data from many different sources into a single data repository to support advanced analysis and decision making in your business.
In this article you will learn how you can use this system in your company, what there is to consider and from which advantages you will benefit.
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Data Warehouse: Definition
A data warehouse, also known as an enterprise data warehouse (EDW), is a system that bundles information from diverse sources into a central, unified data archive in order to Data analysis, Data Mining, artificial intelligence, (AI) and machine learning to promote.
With the help of a data warehouse system, a business can perform effective analysis of large volumes (petabytes and beyond) of historical data that would not be possible with a traditional database.
For more than 30 years, data warehousing systems have been an integral part of Business intelligence-(BI) solutions, but recently they have evolved due to new data types and hosting methods.
It used to be hosted locally on a mainframe computer and focused on extracting, cleaning, and preparing data from other sources and loading and maintaining the data in a relational database.
Today, however, it can be hosted on a dedicated application or in the cloud and has analytics capabilities as well as tools for data visualization and presentation.
Architecture
The architecture of a data warehouse describes the basic design and structure of a system used to store, manage, and analyze large amounts of data. The architecture consists of several components that work together to enable effective data processing.
The main components are:
Data sources
This is the original data that is to be integrated into the data warehouse. These can come from various points, such as ERP systems, CRM systems or other databases.
Data extraction process
This is the process of extracting data from data sources and loading it into the data warehouse. This process includes data cleansing and transformation to ensure that the data is consistent and meaningful.
Data modeling
This is the design of the structure of the data. There are different approaches to data modeling, such as the dimensional model, the relational model or the object-oriented model.
Data processing
This is the process by which data is processed to generate meaningful information. This includes aggregation, filtering and summarization of data.
Data preparation
This is the process of preparing the data in the data warehouse for analysis. This includes the creation of reports, dashboards and other analysis tools.
Data storage
This is the physical storage of data in the data warehouse. There are different technologies for data storage, such as relational databases, NoSQL databases or data warehouse appliances.
Data analysis
This is the process of analyzing data in the data warehouse to generate meaningful information. This includes the creation of reports, dashboards, scorecards, and other analytical tools.
The architecture of a data warehouse is critical to effectively managing and analyzing large volumes of data. A well-designed architecture can help improve data quality, reduce data preparation time, and shorten analysis time.
OLAP and OLTP in data warehouses
OLAP, short for Online Analytical Processing, is an advanced database technology that focuses on efficient querying and reporting rather than transactional processing. The data origin for OLAP comes from OLTP (Online Transactional Processing) databases, which are usually stored in data warehouses.
With OLAP, large amounts of data can be analyzed quickly and easily to gain valuable insights and make informed decisions.
Unlike traditional databases, OLAP is designed to meet the needs of businesses and organizations that need to perform complex data analysis.
In contrast OLTP( online transaction processing), which is typically used for real-time transactions over the Internet.
The key difference between the two lies in their alignment:
While OLAP specializes in analytical evaluations, OLTP focuses on transaction-oriented processing. OLAP can thus be used to perform complex data analyses, while OLTP is primarily used for the fast and reliable processing of transactions.
OLAP tools are specifically designed for multidimensional analysis of data within a data warehouse, which includes both historical and transactional data.
The common application areas of OLAP include data mining and others Business intelligence applications, complex analytical calculations and Prediction scenarios as well as Business reporting functions like Financial Analysis, Budgeting and Forecasting.
With the help of OLAP, businesses can easily and effectively analyze their data and gain valuable insights to optimize their business processes and improve their decisions.
OLTP is designed to do just that, transaction-based applications by processing current transactions quickly and accurately.
Typical applications of OLTP include ATMs, E-commerce programs, the Processing of credit card statements, Online reservations, booking systems and Document management tools.
The 2 schemas in data warehouses
Schemas represent procedures that control the arrangement of information in a database or data warehouse.
Two key types of schema constructs exist, namely the star schema and the snowflake schema, which will significantly shape the design of your data model:
- Star Scheme: This schema contains a fact table that can be combined with various denormalized dimension tables. This is the most straightforward and common form of schema, with users benefiting from increased query speeds.
- Snowflake scheme: The snowflake scheme is less common, but it is an alternative form of organization. Here, the fact table is linked to various normalized dimension tables, which in turn have subordinate tables. Users of such a snowflake schema enjoy the advantage of reduced data redundancy, but must expect costly query performance.
Data Warehouse vs. Database, Data Lake and Datamart
The terms data warehouse, data lake and datamart are often used synonymously, but there are some differences between them, which are explained in more detail below.
Data Warehouse vs. Data Lake
A data warehouse acts as a central repository that collects raw data from diverse origins and structures it using predefined schemas for data analysis.
In contrast, the data lake a data warehouse without the predefined schemas, providing a higher level of analysis.
Data Warehouse vs. Datamart
A Datamart represents a selected set of data within a data warehouse that is specific to a particular business unit or department.
By focusing on a small portion of data, a datamart allows you to gain faster and more targeted insights for a department or business unit than would be possible with a comprehensive data warehouse dataset.
Data Warehouse vs. Data Lake
Primarily, a database aims to handle expeditious queries and process transactions efficiently, rather than perform extensive analysis.
Generally, a database acts as a concentrated data repository for a specific application, whereas a data warehouse stores information from one (or even all) applications within your business.
A database focuses on the continuous updating of real-time data.
In contrast, a data warehouse encompasses a much broader scope and collects both current and historical data to make it useful for predictive analytics, machine learning, and other intelligent analysis methods.
Types of data warehouses
You can implement different types of data warehouses into your business, depending on which variant fits best into your organization.
Cloud Data Warehouse
A cloud data warehouse is a specialized data warehouse offered as a managed service in the cloud.
In recent years, the popularity of cloud-based data warehouses has surged as more companies use cloud services to offload their on-premises data centers.
A cloud-based data warehouse allows the entire infrastructure for the data warehouse to be handled by the cloud provider.
This means that the customer does not have to make any initial investment in hardware or software and does not have to worry about managing or maintaining a data storage solution. This results in a compelling and straightforward solution for the customer.
Data warehouse software (local/license)
Businesses have the option of purchasing a data warehouse license and then installing a data warehouse on the company's own infrastructure.
Although this typically comes at a higher cost than a cloud data warehouse service, it may be a better choice for government agencies, financial institutions or other organizations.
Especially when a higher degree of control over the data is desired or strict regulations regarding security, data protection or other regulations must be adhered to.
Data warehouse appliance
A data warehouse appliance represents a fully integrated solution of hardware and software that a business can plug directly into its network and use.
It combines CPUs, memory, operating system and data warehouse software in a single solution.
Compared to cloud and on-premises deployments, a data warehouse appliance offers a Balance investment costs, speed of deployment, scalability, and management control.
Advantages of a data warehouse
If you use a data warehouse in your company, you will benefit from the following advantages in the short and long term:
- Better data quality: Data from disparate sources limits the ability of decision makers to reliably set business strategies. It enables data integration so you can use all enterprise data in every business decision.
- Faster business insights: The multitude of data sources poses a great challenge to decision makers when it comes to developing resilient corporate strategies. With a data warehouse, the integration of data becomes possible, making corporate data usable at any time and for any decision. This makes decision-making much more efficient and well-founded.
- Better decision making: A data warehouse promotes diverse business intelligence capabilities, such as data mining (discovering hidden structures and connections in data), artificial intelligence, and machine learning. These tools can be used by data professionals and executives to gain sound evidence for smarter decision-making in nearly every area of the business, from business operations to financial management to inventory management.
- Achieving and expanding competitive goals: The factors listed are largely responsible for a business being able to analyze a variety of data faster and more effectively than by using different data stores. This leads to an increased chance of gaining valuable insights and thus increasing the potential of the business.
Optimize Document Workflows with Konfuzio and Data Warehouses
Konfuzio is an AI-powered document automation platform that extracts structured data from unstructured documents such as invoices, contracts, and receipts.
Data warehouses allow you to store these large volumes of structured and semi-structured data from various origins.
The connection between Konfuzio and data warehouses is profound, as Konfuzio can seamlessly feed the extracted data from unstructured documents into data warehouses.
This enables further Analyses, reports and visualizations, providing deep insight into the data. Konfuzio's automation of data extraction results in a significant reduction in the time and effort required to collect and prepare data for analysis.
As a result, businesses can access data faster and more efficiently and gain valuable insights.
Once the data is extracted, it can be transformed and loaded into a data warehouse where it can be combined with other data sources, analyzed and visualized with business intelligence tools.
In this way, companies can gain insights into their operations, identify trends, and make data-driven decisions.
Overall, the integration of Konfuzio and data warehouses can help businesses streamline their data management processes, improve data quality, and gain a competitive advantage in their respective industries.
Advantages of the combination Konfuzio and Data Warehouse
The combination can improve your business in more ways than one:
- Increased efficiency: By automating the extraction of data from unstructured documents, Konfuzio can significantly reduce the time and effort required to capture and prepare data for analysis. This can make your business more efficient as your employees spend less time on manual data entry and more time on value-adding tasks.
- Improved data quality: Konfuzio's AI-powered platform can help ensure data accuracy by automatically extracting structured data from unstructured documents. This can improve the quality of your data by reducing the risk of errors that can occur during manual data entry.
- Better Insights: By combining data from multiple origins into one data warehouse, you can gain better insights into your operations and make data-driven decisions. The Data extraction functions from Konfuzio help you gather data from unstructured sources that give you additional insights into your business that you may not have had access to before.
- Competitive Advantage: With the help of Konfuzio and a data warehouse, you can gain a competitive advantage in your industry by making faster and more informed decisions. This allows you to identify trends, optimize processes and react faster to changes in the market.
Overall, the combination of Konfuzio and a data warehouse can help make your business more efficient, improve data quality, gain better insights, and give you a competitive advantage.
Conclusion: Data warehouse for optimized data storage and analysis
If you use a data warehouse, you will benefit from the many advantages, such as better data quality and the ability to make the right competitive decisions quickly and flexibly.
Depending on what industry you are in and how the infrastructure is set up, there are different ways to integrate it into your business.
If you then use other tools, such as Konfuzio in combination with data warehouses, you can optimize an entire area in your company.
