**4. Design Aspects**

The design aspects and practical implementation constraints are to be studied in detail to develop a suitable data managemen<sup>t</sup> solution. This section presents the design aspects to be considered in data warehouse- and data lake-based enterprise data management.

#### *4.1. Data Warehouse Design Considerations for Business Needs*

To design a successful data warehouse, one should also realize the requirements of an organization and develop a framework for them. Some of the key criteria to keep in mind when choosing a data warehouse are as follows:


DW projects can be compartmentalized to smaller pieces capable of delivering faster. This design trick helps to prioritize the DW as a business's needs change.


#### *4.2. Data Lake Design Aspects for Enterprise Data Management*

At a high level, the concept of a data lake seems to be simple. Irrespective of the format, it stores data from multiple sources in one place, leverages big data technologies, and deploys on a commodity infrastructure. However, many a time, reality may fail due to various practical constraints. Hence, it is quite important to consider several key criteria while designing an enterprise data lake:

• **Focus on business objectives rather than technology**: By anchoring the business objectives, a data lake can prioritize the efforts and outcomes accordingly. For instance, for a particular business objective, there may be some data that are more valuable than others. This kind of comprehension and analysis is the key to an enterprise's data lake success. With such an oriented goal, data lakes can start small and then accordingly learn, adapt, and produce accelerated outcomes for a business. In particular, some

key factors in this regard are (1) whether it solves an actual business problem, (2) if it imparts new capabilities, and (3) the access or ownership of data, among others.


#### **5. Tools and Utilities**

In this section, we categorize and detail the popular data warehouse and data lake tools and services in Sections 5.1 and 5.2, respectively.

#### *5.1. Popular Data Warehouse Tools and Services*

An enterprise data warehouse is one of the primary components of business intelligence [14,16]. It stores data from one or more heterogeneous sources and then analyzes and extracts insights from them to support decision making. Some of the popular top data warehousing tools are explained below:

• **Amazon Web Services (AWS) data warehouse tools**: AWS is one of the major leaders in data warehousing solutions [78] (https://aws.amazon.com/training/classroom/ data-warehousing-on-aws/, accessed on 25 September 2022). AWS has many services, such as AWS Redshift, AWS S3, and Amazon RDS, making it a very cost-effective and highly scalable platform. **AWS Redshift** is a suitable platform for businesses that require very advanced capabilities that exploit high-end tools [79]. It consists of an in-house team that organizes AWS's extensive menu of services. **Amazon Simple Storage Service (AWS S3)** is a low-cost storage solution with industry-leading scalability, performance, and security features. **Amazon Relational Database Service (Amazon RDS)** is an AWS cloud data storage service that runs and scales a relational database. It has resizable and cost-effective technology that facilitates an industrystandard relational database and manages all database managemen<sup>t</sup> activities.


#### *5.2. Popular Data Lake Tools and Services*

A data lake stores structured data from relational databases, where semi-structured data, unstructured data, and binary data and can be set up "on the premises" or in the "cloud" [83,84]. Some of the most popular data lake tools and services are analyzed below:

