The progress of data science, extraction, and analysis allows business owners to study more about the companies, maximize workflow productivity, and define the benefits and drawbacks of the enterprise.
Yet, business data has to be mined, converted into the appropriate format, and stored for the following actions. Thus, companies apply data warehouses to process such vast loads of information.
In this article, we’ll cover the notion of an enterprise data warehouse, its elements, and the creation process.
What is an Enterprise Data Warehouse?
An enterprise data warehouse (EDW) is a relational data warehouse comprising the business data to prepare valuable statistics. There are diverse sources providing such data, like Customer Relationship Management (CRM) systems, Enterprise Resource Planning (ERP) systems, or physical recordings. Therefore, with a data warehouse, businesses can operate its immense values from single storage with no need for multiple databases control.
Enterprise Data Warehouse vs. Conventional Data Warehouse
Each data warehouse is a rich set of structured and clear business information for analysis to ease the process of making decisions in the company. But why do we consider enterprise data warehouses and the common ones different? They primarily differ by their tech architecture and functionality.
Conventional data warehouses (DWs) main aim is gathering and storing data for particular organization units (to illustrate, sales data, customer data, etc.) and cater to the requirements of certain departments. Whereas EDWs are complex structures split into fewer databases that store the info for all the units and answer queries at the enterprise level. As a result, an enterprise data warehouse is more beneficial due to the easier request processing and more expanded opportunities. It’s one more way to enhance the business performance among the other well-known ones, like enterprise app development or business intelligence implementation.
Best Enterprise Data Warehousing Solutions
Here are the most widely-applied EDW platforms able to satisfy the key requirements for data warehousing: instant scalability of storage, flawless performance, excellent protection, accessibility, etc.
Amazon Redshift
Amazon Redshift is a highly-demanded EDW vendor. It’s a full-fledged software with an expanded features kit. Its distinguishing capability is massively parallel data processing, columnar data storage, unlimited expandability, and query optimizer.
It enables customers to request vast, organized data loads and integrates AWS infrastructure to improve customer experience. Redshift is connected with a row of services, business analytics instruments, and solutions (like Amazon QuickSight, Periscope Data, Tableau, etc.).
Google BigQuery
Google BigQuery refers to a web service ensured by Dremel solution and created for simultaneous queries operation on huge datasets. Its main benefits are serverless architecture, multi-cloud opportunities, implemented machine learning (ML) and artificial intelligence (AI), and easy-to-use functionality.
Snowflake
Snowflake is an enterprise-level data warehouse developed as an Amazon Cloud service. It can cope with any workload, provide safe access to the company data, and offer precise and thorough statistics for various business goals.
Snowflake ensures separate storage and computation scalability, so customers are charged for those features individually. Its key functions are data and objects cloning, virtual calculation warehouse, recovery system object, change data extraction.
What Does EDW Serve For?
Specialists in different industries can profit from the EDW software analysis, as it simplifies the decision-making process. In addition, people in business can apply this solution to assemble all the existing info from the day the company was founded to develop efficient business strategies.
Moreover, EDW ensures statistics and analyzes the enterprise’s internal workflow. It’s an excellent way to maximize task fulfillment productivity by redistributing the workforce or defining which regular operations may be automated.
Last but not least, your business analysts can concentrate on the other assignments. There’s no need to define difficult SQL queries and comprehend the info structure, as this task is already accomplished by engineers. Thus, specialists only apply easy-to-use toolsets to accomplish their tasks effectively.
How Industries Use Data Warehouse
Healthcare
Healthcare is a domain applying an EDW to extract and store the medical-related data from various sources, like EHRs, supply chains, claims, EMRs, etc. It allows healthcare establishments to track patients’ healing process as well as the health conditions, resulting in improved clients and medical outcomes and cutting down operational investments.
Retail
EDW in Retail allows analyzing great amounts of data to forecast the demand for specific products, complete market exploration, and discover more distributional channels. Besides, such software provides an opportunity to observe the goods and services performance, clients’ behavior patterns and build an adjustable pricing policy. You can edit the price according to the current market situation.
Finance
In terms of this domain, financial institutions’ employees are able to track and observe interactions with clients. Banks commonly apply this solution to classify the user data and instantly access the info when needed. To illustrate, specialists can explore the customer’s credit history to decide whether to approve or decline their loan request.
Data Warehouse Types
Let’s now observe the enterprise data warehouse types to understand which one is suitable for your case:
Enterprise Data Warehouse
This model is the most applied one. It allows enterprises to store patchy data in one place. After mining, converting, and loading (ETL process), the information may be applied to conduct reports, create statistics, etc. This type is the only one keeping the vast data loads from diverse subject areas.
Operational Data Storage
Operational Data Storage’s peculiarity lies in the fact that it keeps the temporary information.
With ODS software, it is updated online at the current moment. That is why such a storage model is excellent for daily operations and employees’ performance tracking.
Data Mart
The main function of Data Mart is storing the information of the specific department but not the entire organization. This feature provides higher precision for performance observation as each department has a separate solution.
Data Lake
This type is aimed at gathering rough, unorganized, or well-organized data from diverse sources for further structuring, studying, and exploration. It is named data on demand as well. In case certain info is necessary, the data subgroup is selected according to the particular criteria and supplied to data analysts.
Data Warehouse vs. Data Lake vs. Data Mart
These three concepts are often mixed up. Yet, they are diverse due to their architecture, data types, usage aims, and data access levels. Let’s define each term to avoid such mistakes.
Data warehouse
Data warehouse is an advantageous option for companies processing huge amounts of data. It’s an immense repository of well-organized and optimized information. Such warehouses are mainly applied for business intelligence (BI), batch reporting, and visualizing statistics.
Data mart
This model is a data warehouse subtype that is used in a specific industry. Its main purpose is sharing the data by the industry and linking to each other. Yet, they are not suitable for scale organizations due to their small size.
Data lake
The data lake contains rough unstructured data in various formats (documents, videos, images, text, etc.) With it, the information is kept in the raw data type till required. This solution is commonly applied in data discovery, predictive analysis, and machine learning.
Enterprise Data Warehouse Architecture
EDW architecture comprises quite a complicated structure, as its main aim is data extraction, cleanse, and further analysis. Every operation needs an individual layer that completes its work. Commonly, the entire data pipeline is organized into the three main components:
- Single-tier architecture. The customer, database, and server are placed in one machine.
- Two-tier architecture. The client and the server interact straightforwardly.
Three-Tier Architecture
Let’s consider this architecture model separately as it comprises the three tires fulfilling various tasks:
- Bottom tier. This layer is a database. At this stage, the ETL process is completed.
- Middle tier. This level is an Online Analytical Processing (OLAP) system, which studies, explores, and analyzes the gathered data. During this phase, all forecasts, reports, and statistics are operated. There are two OLAP systems models: MOLAP (for multidimensional databases) and ROLAP (for relational databases).
- Top tier. This integral element contains the user interface.
Enterprise Data Warehouse Types
Let’s cover the two main methods of data warehouse deployment:
On-Premises
With this method, companies are responsible for purchasing, setting up, and maintaining the hardware for the data warehouse handling.
Benefits:
- High control
- Potent protection
- Increased speed
Drawbacks:
- Higher cost
Cloud
This environment allows data delivery as an operated Software as a System (SaaS) ensuring via several public cloud vendors. Thus, the organization doesn’t have to invest in hardware, only in the cloud environment.
Benefits:
- No need for system management
- Decreased spendings
Drawbacks:
- Private data break-in risk
How to Develop EDW?
A top-quality enterprise data warehouse is sophisticated software requiring a complex development process. Let’s observe the integral elements of EDW:
Database
The first question to handle is the database type. The relational or SQL ones are the most widely applied. They ensure great scalability and decrease the flaws’ possibilities. Yet, such types should be used with clear and organized data.
In case this doesn’t relate to your project, you may choose NoSQL with higher adaptiveness. This type allows for keeping divided files in folders. To illustrate, you can download the entire blog to the database as a plain document. Then you may open it for further actions.
Nonetheless, when processing private data, relational databases are essential. Keeping such unstructured information is forbidden by a data security protocol.
ETL Process
To organize the data, programmers apply data sourcing, converting, and migration instruments. Let’s see how this process happens in detail:
Extraction
During this step, the ERP solution gathers the data of different formats (SQL, XML, etc.) from diverse resources. Next, this data is placed in the waiting area. The data can’t be gathered straightforwardly into the database, as it should be converted into one main format.
Transformation
This phase converts the extracted data into the main format with the following steps:
- Filtering
- Merging
- Classifying
- Cleaning
Loading
The following stage is loading the data into the warehouse. According to the type of EDW, you are able to set the regularity of the storage refresh.
Metadata
It refers to the details concerning the data stored in your enterprise data warehouse. Looking like a senseless combination of letters and numbers, the information is impossible to comprehend without metadata which helps decode it by keeping the details.
Query Tool
Precisely configured instruments for queries help in fulfilling the main function of this solution- conducting analysis, completing reports, and preparing statistics, which is obligatory for every EDW.
Data mining algorithms are the essential step in building a top-quality EDW for software developers. They allow analysts to define new patterns, trends, find new market opportunities, and a lot of other useful information to progress the business and expand the brand.
Stages to Create an EDW
Such complex software as an ERP involves a row of essential specialists who complete certain steps to bring your project into reality. Still, you have certain responsibilities before proceeding directly to the EDW software creation:
Figuring Out Your Project Requirements
During this stage, you should clearly define which goals you would like to reach with an EDW solution. Study the data loads you keep and advantages it may bring after exploring and thoroughly analyzing it.
The next step is diving deeper into the details of the product development process and selecting the subject field, data warehouse model, and architecture. The essential features list should be created based on your needs from the EDW solution.
Find Credible Software Development Team
The digital product quality fully depends on the chosen specialists. When looking for trustworthy software development vendors, it is highly recommended to thoroughly explore their portfolios, applied technologies, domain experience (which desirably should match your business industry), and reviews from previous clients. Services like Clutch or Goodfirms are excellent tools to select the most suitable tech experts.
Summarize
Creating an enterprise warehouse database is an excellent way to expand the company, analyze its benefits and weak spots, conduct market research, track the interactions with customers, optimize the internal workflow, and thus, significantly enhance the performance. However, such a complex solution can’t be built without a competent team of developers who will create excellent ERP software useful for your specific case.
