What is Data Warehouse?

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With the advancement in technology each day and its reach, we have to face the challenge of Information Overload. Organized and processed data results in valuable information. When the data is unorganized, random, and floating like air bubbles without a specific goal, it becomes an Information Overload. Also, a data warehouse is a solution that aids in achieving the desired outcome with the data. Altogether, it enables Business Enterprises to achieve their business goals. Let us discuss what is Data Warehouse in detail.

Meaning of a Data Warehouse

A warehouse in general is a place used for storing goods. Items stored here can be Inventories, equipment, and other items required. Similarly, a data warehouse is a central repository. It collates large amounts of data from multiple sources in a highly unified, and structured manner. A Data Science Course in India can help you in knowing about what is Data Warehouse in detail.

Different Purposes of a Data Warehouse

Enterprises use the data stored in a data warehouse for various purposes.

A few examples can be:

• Business Intelligence
• Artificial Intelligence
• Data mining
• Data Analysis
• Machine Learning

Consequently, it serves for important business strategies, decisions, expansions, and improved organizational performance.

Types of Data Warehouses:

1. Traditional Warehouse

Traditional data warehouses comprise multi-tiered structures, servers, data stores and applications primarily on-premises. Additionally, they can handle large volumes of data for reporting and analytical purposes. Also, they have limitations in terms of interoperability.

Here Extract, Transform, and Load approach is used to load data from the source to the data warehouse. Learn Data Science to become an expert in this field.

2. Modern Data Warehouse-MDW

MDW is a cloud-based system that collects data from multiple sources. It also helps enterprises store and analyze data to make better business decisions. It uses analytical tools like machine learning and data mining. As a result, they are usually faster than the traditional on-premises data warehouses. They use the Extract, Load, and Transform(ELT) approach. Subsequently, the ELT approach allows data tools to load large volumes of data at once and then transform as per the requirement. MDW automation also enables users to add new sources. Also, it can create new data models and new data marts without writing SQL codes.

Advantages of a Data Warehouse

A few common benefits of a Modern Data Warehouse are mentioned below:

1. Better quality data with more trust

The data in a DW is collated from various sources. Additionally, it can be in the form of operational databases, transactional systems, and flat files. After that, it undergoes de-duplication and certain operational cleansing. Further, it is undergoing standardization to create a “single source of truth”, before being stored in a data warehouse.

2. Faster business insights and decision-making

The data in a DW is assembled from different sources. They can be operational databases, transactional systems, and flat files. Because of this organizations can undertake various BI activities. For example Data mining, machine learning, and artificial intelligence. They assist in finding business patterns much faster.

Data Warehouse Architecture

A data warehouse consists of a 3-tier architecture. It can also be customized as per the company’s specific needs:

Bottom Tier:

The bottom tier consists of database servers that collect, cleanse, and transform data from different sources. The process is entirely batch-driven. Also, it relies on automation.

Middle Tier:

The middle tier represents the analytics engine tier. It consists of an OLAP or Online Analytical Processing server. Subsequently, it provides fast query speed to access and analyze data. The type of OLAP server to be used is determined by the database system used in this tier.

Top Tier:

This represents the interface of the front-end user. This interface allows the end user to perform ad-hoc analysis of their business data. It can also help in viewing reports.

Key components in a data warehouse

Below are the few key components of a data warehouse:

ETL

ETL format moves the data from a source database to a data warehouse. As a result, data can be converted into an organized and more user-friendly format. This, in turn, can be used for queuing, processing, and analysis.

Metadata

Metadata is a short description of the data. It is stored in a data warehouse to make it searchable. Also, it makes the data more usable for analysis, and creating dashboards and reports. 

SQL Query Processing

SQL(Structured Query Language), is the default language for querying the data stored in a data warehouse by the analysts. This allows for high performance in data analytics. However, the data warehouse becomes expensive due to more data and SQL computing resources.

Data Layer

Data Layer is the layer provided to the users to access the data. It creates a partition segment in an organization. Additionally, it helps to decide who can be given the data access. As a result, it helps in better control at a granular level. 

Governance and Security 

Governance is the set of practices, protocols, data compliances, and technologies. Subsequently, they aid in effective control and data management in a data warehouse. Also, good governance and security systems in a data warehouse are essential for the effective use of data in an enterprise.

A Comparative Analysis of Data Warehouse with Data Mart, Database, and Data Lake

Data Warehouse vs Data Mart

A data mart is a subdivision of warehouse data. It holds data specific to a particular subject. It’s example can be various departments in an organization like the finance and HR department. Additionally, a data mart enables business analysts to gain meaningful insights faster since they deal with a smaller subset of relevant data. 

Data Warehouse vs Database

A database is a primary data source built for specific applications. Whereas, a warehouse holds a vast volume of data for a large number of applications. Also a database aids in running rapid queries and processing transactions. At the same time, a warehouse focuses majorly on Business intelligence (BI) tools.

A database focuses on updating data on a real-time basis while a warehouse captures historical as well as current data for predictive analysis.

Data Warehouse vs Data Lake

A data lake is like a Data Warehouse that stores the organization’s data without a predefined framework. It is like a reservoir that aggregates both structured and unstructured forms of data. This allows a data lake to perform more analytics than a warehouse.

Tools used for cloud-based data warehouse

A few of the best data warehousing tools are:

1. Amazon Redshift
2. Teradata
3. Bigquery
4. Snowflake
5. PostgreSQL
6. Amazon RDS
7. Microsoft Azure
8. IBM Db2
9. Cloudera
10. MarkLogic
11. MariaDB
12. Firebolt

Challenges With Cloud-Based Modern Data Warehouse

With the genesis of advanced business analysis tools companies are using huge volumes of data from different sources. This makes data warehouses very expensive and not so flexible.

It also faces major security challenges in the form of malware attacks, data theft, and data breaches. In short cloud-based data warehouses face issues related to privacy and regulatory compliances.

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Conclusion

An increased availability of different types of data and modern technology has been noted. This is why data warehouses are undoubtedly a big help to businesses. It helps them in enhancing their productivity and output. Additionally, the current cloud-based architecture data warehouses have enhanced their analytical capabilities. Also, data visualization tools have been added for the organizations. However, there is a challenge to work on the cost-effectiveness and security features. Consequently, it would make data warehouses more affordable and sustainable in the future.

Recommended Reads

1. What Is a Data Warehouse And How It Works?
2. What is Data Science and its Career Path?
3. Scope of Data Science in India: Career, Eligibility, Jobs (2024)
4. What is a Data Warehouse? Definition, Concepts, Types

FAQs

Q1- What is the purpose of developing a data warehouse?

Ans-Firms can now utilize the information that they have collected in warehouses to expand, make tactical decisions, and also improve operational efficiency.

Q 2. What is SQL in a data warehouse?

Ans-SQL or Structured Query Language is a programming language used for querying and accessing databases in a data warehouse.

Q 3. What is the main use of a data warehouse?

Ans -A data warehouse’s few most important advantages are faster business insights and decision-making.

Q 4. What is data warehouse architecture?

Ans- In general, a data warehouse consists of a 3-tier architecture.

Q 5. What difficulties are faced by data warehouses?

Ans-Data warehouses are facing difficulties in terms of their high cost and security threats. Furthermore, they can be in the form of data theft, malware attacks, etc.

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