Data warehousing is the electronic storage of this data and therefore it is a crucial component of business intelligence. Optimizing performance is a vital goal. Therefore it is key for the success of businesses. Business intelligence is a term that covers several methods and processes. You can use them to collect, store, and analyze data. Data comes from business activities or operations and the ultimate goal is business success. The processes and activities used to improve performance include:

• Mining
• Reporting
• Performance metrics and benchmarking
• Analytics (visual or statistical)
• Querying
• Visualization
• Preparation

These processes and activities require large amounts of data.

What is Data Warehousing?

A data warehouse is a location where you can store information and is analyzed to make more informed decisions. Data you store in the warehouse comes from various sources. 

Various people in a company can use the data. They might include data scientists, data engineers, and business analysts. However, they access the data through SQL clients and analytics applications. Companies like us here at Devixo can help you make better use of your data.  

Competitive business needs data and analytical tools. To extract insights, you need reports, devices, and analytical tools. Also, to check the business’s performance and help you make decisions.

Data warehouses are essential. The power reports, analytical tools, and devices. They can do this because they store data efficiently and minimize the input and output of data hence quick results delivered at the same time. 

Data Warehouse vs. Database vs. Data Lake: What’s the Difference?

The difference is all in the design. A standard operational database is a store for structured data. Databases are also easily accessible. You can use them for:

• Auditing data entry
• The automation of business processes
• Analysis of relatively small data sets
• To create financial reports. 

Databases are a system that monitors and updates real-time data. Therefore, this means only the most recent data is available. A database might only store the latest address of a customer. On the other hand, a data warehouse might have a range of data. For example, several customer addresses covering ten years or more.

You can store much larger amounts of data in data warehouses and data can come from a range of sources. They are popular with medium and large companies therefore they allow for the sharing of data and content across a company. 

Data science research and testing companies generally use data lakes. Users include data engineers and scientists. A data lake is a vast storage location. In it, you can store vast amounts of data in their original form. When you need it, it is ready. 

Where Did the Concept Come From?

Believe it or not, data warehousing is not new. It’s been around for quite a few decades. It was necessary to handle increasing amounts of information.

The concept was first introduced in 1988 by a couple of IBM researchers. Barry Devlin and Paul Murphy developed the Business Data Warehouse. 

A must-read publication on data warehousing was first published in 1990. It was written by WH Inmon. This man is considered by many to be the father of data warehousing. He called his book “ Building the Data Warehouse.” Publishers have reprinted it several times since then.

Data Warehousing Jargon

Some of the most commonly used terms include:

• Metadata: Provides answers to any data-related questions. It is data about data. A simple example is the number of tables in a database. 
• Data Cube: A way of representing data in multiple dimensions.
• Metadata Repository: Plays an integral part in the system which includes business and working data as well as data for mapping and summarizing algorithms.
• Data Mart: Data marts are almost the same as a data warehouse. However, they tend to have a limited audience or data content. A marketing data mart contains data related to items, customers, and sales.
• Data Mining: Data mining is the process of discovering patterns. These patterns are in large data sets. It is a way of turning raw data into useful information. 
• Big Data: This is a term used for massive volumes of data. You can use it to address specific business problems.
• Data analysis: Data analysis is a process. It includes the collection, transformation, and modeling of data. The ultimate aim is to discover useful information for business decisions. Companies like Dexivo can help.   

How Data Warehousing Works

It is a central location for storing information. This information might come from a single or several data sources. The data flow can be organized differently, structure wise.

Once it arrives in the warehouse, it can be cleaned and processed. Then it is transformed from a database format to warehouse format. It is then sorted, amalgamated, and summarized. This makes it more organized and easy to use. After this process, it is easier to access. Users can use spreadsheets, SQL clients, or Business Intelligence Tools. 

The data warehouse merges the information. It allows a business to holistically analyze its customers as well as ensures all available information is considered.

Data warehousing provides better insight into a company’s performance. It allows you to compare data you have collected from many sources.  

Businesses commonly use warehouse data for exploration and data mining. An efficient system identifies patterns of information. This will help them improve performance. It also makes the contact between departments more effective. This is because different departments can access the data. 

Historical data can be gathered from within a data warehouse. You can then run analytics on it. Information found provides an insight into consumer preferences. Times of high sales and customer spending are also highlighted. 

Different Types of Data Warehouse

There are three types of data warehouses.

1. Enterprise Data Warehouse (EDW).

EDW is a centralized warehouse. It offers support for decision-making across an organization. Mainly, it is a cohesive approach for organized data representation. The sorting of data by subject is possible. Access is provided under those divisions. 

2. Operational Data Store (ODS).

ODS is a data store with a broad enterprise scope. The difference with this is that the data is refreshed in near real-time. It can then be used for everyday business activities. 

3. Data Mart.

Data Mart is a subsection of the data warehouse. It supports a specific region, business function, or unit. 

Attributes of Data Warehouse Design

The main characteristics of a warehouse’s design are:

• Theme-focused: The design of a data warehouse uses a specific theme. It provides information relating to a subject instead of operations. The topics might include marketing, sales, or advertising. A data warehouse focuses on the display and analysis of data. This helps with the decision-making process. 
• Unified: It helps to unify and integrate data. The data comes from various databases. These are acceptable for data modeling. 
• Time Variance: Data can be stored for a long period. 
• Non-volatility: When data loads, data in the warehouse stays where it is.  

Data Warehouse Architecture

A data warehouse arranges and stores data in a certain way. For the data to be valuable, it has to be organized and cleansed. The most up-to-date, effective techniques to extract raw data are used. It also converts it into an easily digestible structure. There are three types of architecture to consider.

Single-tier Architecture

Its focus is producing a dense set of data. A reduction in deposited data volume is also a focus.  There are situations when this architecture isn’t suitable. If, for example, your business has complex data requirements. Also, if it has large numbers of data streams. 

Two-tier Architecture

The two-tier architecture is a more efficient way to store data. However, it only supports a nominal number of users. It is not scalable either. With this architecture, tangible data sources are split from the warehouse.

Three-tier Architecture

Businesses commonly use three-tier architecture. It produces an organized data flow using raw information hence provides helpful insights. 

It is usual for the bottom tier to be a data bank server. This server collects data from many sources. The middle level is an Online Analytical Processing (OLAP) server which changes the data.  And it makes it more acceptable. It can then be used for analysis and multilayered scrutiny. The third layer includes client tools. Application Programming Interface. Organizations use it for several reasons. Advanced data analysis, inquiries, and reports, for example.

Data Warehouse Components

Five key components make up a data warehouse design. 

1. Data Warehouse Database.

The database is the heart of a data warehouse. It is for the storage of all enterprise data. The data is stored in a manageable condition for reporting. However, there are different types of databases to choose from or storing data. 

• Typical relational databases: These are row-entered. You are likely to use them every day. 
• Analytics databases: These are specially for storage. They are also for the management of analytics. 
• Data warehouse applications: These are not storage options. They are more a kind of software that you can use for data management.  
• Cloud-based databases: These are databases hosted on a cloud. This is where you retrieve them. The benefit of this is that you don’t have to purchase any special hardware. 

2. Extraction, Conversion, and Loading Tools

These help with data extraction. They convert data into a more suitable form. Then it is loaded into the warehouse. There are several ETL tools to choose from. Your choice can affect the time it takes for the data extraction. How the tools extract the data is also affected. It can also impact the changes that take place. How easy it is to do it is another consideration.  

3. Metadata

Metadata allows businesses and technical teams to understand data. They can also convert it into information. There are two types:

• Business Metadata: Information that is given meaning in the context of an organization.  
• Technical Metadata: Information about physical attributes. The physical attributes help to load data from the original sources. 

4. Data Warehouse Access Tools.

Business users usually can’t work with a data warehouse directly. You’ll find many tools that can help. For example:

• Reporting and query tools: Help with the production of corporate reports. They can take the form of spreadsheets, interactive visuals, or calculations. 
• Application development tools: These tools help create tailored reports. These reports are used for specific reporting purposes. 
• Data mining tools: These help identify trends, links, and arrays. The tools look at vast quantities of data.  
• OLAP tools: Help with constructing a multi-dimensional warehouse. They also enable the analysis of data from several viewpoints. 

Here at Dexivo, we can also help. 

5. Data Warehouse Bus.

A warehouse bus is an essential tool. Use it for designing and conveying data warehouses. It contains a data mart. Data is partitioned for a specific user groups’ needs.  

Why You Need Data Warehouse: The Pros and Cons

Data warehouses are a useful tool for many businesses. However, they’re not something that suits everyone. Let’s break down the pros and cons to help you make the right choice when choosing yours.

Pros

• Speedy data retrieval: Data in your data warehouse is never lost. A quick search will quickly retrieve it so you can analyze it further. Or you can get in touch with companies such as Dexivo, who will do it for you. 
• Identify errors and correct them: Data warehouses help remove user error. 
• Deviations are highlighted. They can be corrected before the data is loaded. 
• Easy integration: A data warehouse translates information. It translates it into a simple, digestible format. When this happens, team members can understand it.  

Cons

• Preparation is time-consuming: Inputting the data takes a long time. This allows the data warehouse to perform many other functions. 
• Compatibility difficulties: You might need an independent Business Intelligence team. This is key if you can’t figure out how to use your warehouse correctly. 
• Cost of maintenance is high: A data warehouse can consistently update. However, upgrades can be expensive. Regular maintenance is also required and can be costly. 
• Confidential information can limit its use: Sensitive data might not be available for everyone. This can restrict the use of your data warehouse. 

Find Out Why Big Data Companies Like it Wet

Which Data Warehouse is the Best?

There are many data warehousing solutions, but which one is the best? Here at Devixo, we like to help change the way companies do business. We know how important it is to have all the information to hand. Here is a list of our top five data warehouses. 

Top 5 Data Warehouses

Snowflake

Snowflake is a data platform that has opened the door for many business types. Previously, they would be unable to benefit from their data. Snowflake has thousands of customers. These customers can now advance their businesses further than ever before. What it provides is a single, integrated platform built for the cloud.  

Microsoft Azure Systems

Azure is the updated version of Microsofts SQL Data warehousing. It uses the latest technology. It provides a state-of-the-art analytics solution.  Also included is enterprise data warehousing. Access is available to server less on-demand resources. Microsoft uses the most advanced privacy in its warehousing. Top-quality security is an additional benefit.  

Amazon Redshift

Amazon Redshift has tens of thousands of customers. What is on offer is a simple and cost-effective data warehouse service. The service is fast, fully managed, and on a petabyte-scale. 

Google BigQuery

BigQuery is Google’s fully managed, low cost, petabyte-scale data warehouse, and built specially for analytics. It is server-less. There is no framework you have to worry about working. There is also no need for a database controller. This Big data analytics platform is very powerful.  Companies of all shapes and sizes can use it. 

Vertica

Thousands of leading, data-driven enterprises trust Vertica. They use it for the storage of their data. Top names include Twitter, Bank of America, Etsy, Uber, and Intuit. Total cost is much lower than legacy systems. An added benefit is its speed, reliability, and scale.  

What Does the Future Hold For Data Warehousing? 

The future for data warehousing is in the cloud. An increasing number of data warehousing tools are now available. These tools take advantage of the cloud’s numerous advantages. They include accessibility from anywhere and collaboration. Flexibility is another advantage. 

The cloud also removes one significant barrier. The cost has limited the adoption of data warehouse solutions. That is up until now. However, the cloud model is low cost. It’s also fast and easy to get started if you choose a cloud data warehouse. The deployment process is not time-consuming. 

Take Advantage of Data Warehousing and Take Your Business to the Next Level

Data warehousing collects and manages data from various sources. Store it all in a central location. Then use it for meaningful business insights. You can use it to help make your business decisions. It also provides fast access to vast amounts of data. Simple technology allows you to access it better. There’s no time like the present to take advantage of the data you collect. Help take your business to the next level. 

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