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Data Warehousing: Concepts, Methods, and Structures

Introduction

How data warehousing providers provide their services is an important factor to consider in businesses. Commonly available alternatives to data warehousing include vendors that let you operate the license on your server or those who host software tools on their servers.

When it comes to the design of the data warehousing system, there are many different alternatives available. The hub-and-spoke design, which consists of a centralized data warehouse with dependent data marts, is the one that is used the most often.

It is like a factory, which produces information for businesses. Let’s check the definition, concepts, methods, and structures of data warehouses. At last, we will discuss why there is a need for data warehousing solutions.

What is a Data Warehousing?

Your company is likely now gathering a lot of data. There will be several systems where that data will be kept.

As a result, your sales team’s databases may contain information about past customer transactions, while social media interactions are captured by your marketing systems, and comments, reviews, and complaints are monitored by your CX team.

The combined potency of all of the information would be enormous. Unfortunately, many companies are unable to fully realize that potential at the moment because:

  • Data is compartmentalized into distinct, non-integrated systems;
  • There is simply too much data to process manually.

You can store all of your data in a large, virtual data warehouse. It converts current and historical data from various sources across your company into useful, educational analytics for BI.

Data Warehouse Architecture

The design or framework for managing and arranging data for corporate analytics and insight is called a data warehouse architecture. This organizational architecture is where effective data integration, storage, and retrieval takes place.

A well-designed data warehouse architecture has advantages.

  • Solid centralized and consistent data is maintained as the input in this mechanism.
  • Extraction, Transformation, and Loading take place from Data Staging to Data storage in the form of Metadata or a Multi-dimensional Database.
  • This data is then utilized in Information delivery, data mining, OLAP, and report/query.

Therefore, the establishment of a data-driven culture inside businesses, the conversion of raw data into actionable insights, and the driving force behind informed decision-making all depend on a strong data warehouse architecture.

Before we delve into more details, it is necessary to learn about what a generic data warehouse architecture looks like.

Components of Data Warehouse Architecture

What types of source systems can input data into a data warehouse?

  • We can have OLTP systems ODS, and even a data warehouse. (Yes, a data warehouse can send data to another data warehouse in addition.)
  • There can be raw files directly inputted (raw data) as well as data coming from outside the organization, which is the external source (External data).

Staging Area

Now, typically a staging area, also known as a stage is a landing zone for a data warehouse. It should ideally maintain the same copy as a source system.

The major advantage of doing this is if there is a data issue one can refer to the stage to understand what data was sent by the source without having to engage multiple stakeholders again and again. Data in this stage is extracted and loaded by ETL (Extract, Transform, and Load) processes. Please note that the transformation usually doesn’t happen at this layer.

Data mart

Now ETL (Extraction, Transformation, Loading) is used again this time with transformations to load data from Stage into either a normalized data store, a dimensional data store, or a data mart. Most widely it’s a dimensional data store. A dimensional data store is a result of dimensional modeling done using facts and dimensions, which is a gimbal methodology.

Multi-Dimensional Database

It is essentially a data warehouse, and finally, we have a consumption layer in the data warehouse, which is what a data warehouse outputs, it can create an OLAP system for analytical or reporting purposes and send data to another data warehouse using extracts or DB  connections it can provide a materialized view to business users for quick querying of data for a day to day use and finally it can create a multidimensional database (MDB)  also known as a cube for analytical or business intelligence purposes.

All these layers are a result of data warehousing

Actions Performed at Every Layer During Data Warehousing

The first layer is the source to stage the action here also known as Data Sourcing. Data transformation is usually performed in the second layer it can also be performed before loading to the consumption layer if there is a need for it. This is a common architecture, where most data warehouse stores are represented by NDS, and there may be many more layers within DDS and data marts. Next, if we combine the first two layers, the action performed here is data consolidation and data mapping.

A data mapping is done between source fields and target data warehouse fields using business rules and the transformations required to meet them are also documented in the mapping document. In the final layer of data, provision is made for efficient use of information and finally, data integration, extraction, loading, data quality checking, data auditing, and metadata capture or management are also done in almost all layers of the general data warehouse.

Advantages of Data Warehousing

A data warehouse is usually a single stop for business to manage their data.

  • Quick Access to Information: Users have quicker access to data and information as opposed to reaching out to multiple sources to get the required information.
  • Higher ROI: This aids in faster and more informed decision-making. Hence, improved return on investment.
  • Improved Data Usability: Undergoes various data cleaning exercises as well as data quality checks. It makes data more usable.
  • History data: Storage of history data in a data warehouse, enables analytics for data over some time and hence effective insights.

What is a Cloud Data Warehouse?

Every business has started stepping into a cloud data warehouse, where your data is safe and secure on cloud computing platforms like Amazon Web Services(AWS), Google Cloud Platform (GCP), and Microsoft Azure.

A cloud data warehouse allows organizations to store massive amounts of data than ever. Their Pay-as-you-go pricing models help organizations save money investing in hardware as cloud data warehouse manages data without the need for hardware infrastructure. Speeds up the performance with high-speed data processing, and querying adds to its benefits.

  • Cost Saving
  • Convienence and Speed
  • Advanced Analytical Features
  • Faster Performance
  • Support AI and ML initiatives
  • Rigid Secure Structure

What is a Modern Data Warehouse?

A modern data warehouse is cloud native and meets the challenges faced by a typical data environment with advanced data storage and management frameworks.

It works on cloud computing platforms as well as provide instant data processing, big data analysis and processing, and integration with hybrid and multi-cloud architectures. By individualize compute and storage, it offers the best of both in terms of speed and price, whereas, security measures that are strong provide protection, and conformity.

Usability and performance are two powerful aspects of this computing model:

  • interfaces make data available and
  • ETL facilitates the generation of effective information to support decision-making.

Examples of such data warehouses are Snowflake, Google BigQuery, and Amazon Redshift, and they depict the possibilities and hyped versatility of contemporary data warehousing systems.

A Major Difference between Data Lakes and Data Warehouses

A data lake is an enormous virtual collection of raw data. They can store any type of data without any pre-processing and have a larger storage capacity.

The main difference is that data lakes contain raw data, and data warehouses store processed data prepared for reporting. This makes them useful in a variety of circumstances.

If you’re really into data science or have extensive machine learning requirements, a data lake can be a better option because raw data is more malleable.

If you want a data store that is better suited to provide enhanced reports for strategic decision-making, then a data warehouse is your best bet.

Key features of Data Warehousing

1. Subject-Oriented

In contrast to providing details on the day-to-day operations of businesses, the information included in a data warehouse is organized by topics.

These topics may include things like sales, marketing, distributors, and so on. A data warehouse will never concentrate its attention on the processes that are now being performed. In its place, it emphasized the importance of data modeling and analysis in the decision-making process.

In addition, it provides a clear and concise perspective on a particular topic by excluding data that is not useful to support the decision-making process.

2 . Incorporated

Data Warehouse is a rapidly expanding firm with a primary emphasis on delivering services that are of the highest quality in terms of data storage.

Data warehousing emphasizes business intelligence as opposed to a company’s day-to-day activities or transactions.

In addition to this, a data warehouse must keep its categorization, structure, and coding consistent to make data analysis as easy as possible.

3. Time-Variant

When compared to the time horizon of operational systems, the time horizon for a data warehouse is relatively expensive.

Information that is related to the past can be obtained from data collected in a data warehouse.

This data is associated with a certain period. Either overtly or implicitly, it has a component of the passage of time.

4. Non-Volatility

Another essential quality of data warehousing solutions is their non-volatility, which refers to the fact that their basic data is not deleted whenever the facility is updated with new information.

In addition, the data is only readable, and it is possible to refresh it occasionally to provide the user with an accurate and up-to-date image.

Data Warehouse vs. a Database

While there are some parallels between a data warehouse and a standard database, they are not necessarily the same thing. With a database, data is gathered for various transactional purposes, which is the fundamental difference.

However, a vast amount of data is collected for analytics purposes in data warehouses. While warehouses hold data, which can be used for larger analytical queries, databases provide instantly available data.

A data warehouse is an example of an OLAP system, also referred to as an online database query response system. An OLTP system is an online database editing system, similar to an ATM.

Tools for Data Warehousing

Are you curious about data warehouse tools? These are software components, which are used to perform various operations on large data sets. These tools make it easy to compile, read, write, and transmit data from many sources.

They are designed to make operations like data merging, filtering, and sorting easier to do. Some of the widely used data warehouse tools include Xplenty, Amazon Redshift, Teradata, Oracle 12c, Informatica, IBM Infosphere, Cloudera, and Panoply.

The Most Effective Methods for the Design of Data Warehouses

  1. Develop models for the data warehouse that are optimal for information retrieval using dimension, de-normalized, or hybrid methods for data organization.
  2. Decide between using an ETL Data Warehousing strategy or an ELT approach when integrating data.
  3. Choose a single method for designing the data warehouse, such as the top-down or the bottom-up method, and stay with it throughout the design process.
  4. Before putting data into the data warehouse, you should always utilize an ETL tool to clean and convert the data if you are going to be employing an ETL strategy.
  5. Develop an automated data cleaning procedure that would clean all of the data in a standardized manner before loading it.
  6. To ensure that the extraction process runs well, the data warehouse’s various components should be able to share metadata.
  7. When it comes to developing your data warehouse, you should adopt an agile strategy rather than a set method.
  1. When transporting the data from the data stores to the data warehouse, it is imperative that effective integration, as opposed to simple consolidation, of the data, take place at all times. It would need the normalization of data models using the 3NF notation.

When Should We Consider Data Warehouse Consultancy?

Designing, developing, and implementing data warehouses are incredibly difficult, time-consuming tasks that demand in-depth expertise to do right. A data warehouse should be used in such a way that you can generate high roi.

It is wonderful if you already have staff members with this knowledge. An internal team with the right resources, including funding, time, and data engineering skills, can be very helpful, provided they start with a thorough understanding of your current data architecture.

Most businesses, however, lack the internal capabilities required to oversee a project of this size, and all but the largest IT teams will find it challenging to juggle their current duties with a data warehouse installation.

At that point, hiring outside data warehouse consultants as an outsourcing option may make sense.

What Exactly Does Data Warehouse Consultancy Mean?

Using external data experts to design, develop, and maintain your data warehouse is known as data warehouse consultancy.

You might choose to start from scratch and create a unique data warehouse, or you might only assist with the deployment of pre-made data warehouse software. There are consulting choices to fit you, regardless of the choice you make.

Consultants for Data Warehouses Assist With

  • Creating ETL tools for a more seamless transfer
  • Data warehouse modeling and database design
  • Data warehouse building and management
  • Data integration and migration
  • Designing data pipelines

In some circumstances, data warehouse consulting will need more specialized assistance, such as a data engineer’s skills to construct data pipelines. Depending on what you need, you can outsource to a group of specialists rather than a single generalist consultant.

Bottom Line

A data warehouse is a strong tool that may assist you in gaining a deeper understanding of both your company and your consumers. It may assist you in finding patterns and making more informed judgments.

If you want to establish a successful data warehouse, it is essential to follow the best practices for the design of a data warehouse. Data Warehousing Solutions can conduct an in-depth analysis of your company’s needs and gather requirements for potential cloud data warehouse solutions.

Aegis Infoways

Aegis Infoways is a leading software development company that provides a wide range of business solutions like software development, data warehouse, or web development for specific business needs.

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