Data scientists heavily rely on machine learning models and visualization packages, but their work is ultimately built upon the foundation of data and data infrastructures. These infrastructures allow them to perform analytics and modeling, making their work valuable for businesses. Raw data is sourced from various channels, but for larger organizations, corporate data warehouses are the primary source of information. These warehouses are designed to structure and organize a company's available data. By utilizing these data warehouses, data scientists can analyze business performance and provide answers to crucial business questions. Check out the rigorous Data Science course in Delhi to become a skilled data scientist in leading companies.

Data scientists typically do not need to worry about the technicalities of data warehouses, as their focus is on utilizing these resources to perform analytics. Data engineers are responsible for the provisioning, creation, and maintenance of data warehouses. However, having a fundamental understanding of data warehousing can facilitate effective communication between data scientists and business decision-makers with data engineers.

There are two primary categories of information systems:
Transactional/Operational Systems (OLTP) and Analytical Systems (OLAP).

Transactional/operational systems, also known as OLTP systems, are information systems designed to support the execution of business processes. Their primary function is to manage database interactions such as inserts, updates, and deletes. For example, in a social media website, the user database must handle user registration, login, and posting tasks. The transactional/operational system must continuously read and write to the database to keep the business running. It must add new users, update their passwords upon request, record user posts in the database, track user interactions with posts, and perform similar transactions efficiently.

Analytics systems, also known as OLAP systems or Data Warehouses, are designed to support the analysis of business processes. These systems primarily interact with databases through queries and retrieve aggregated data.

For example, it is important to understand and measure business performance by analyzing data in a social media business. This may involve tracking the monthly sign-ups or the frequency of recent posts. Analyzing such data requires a different type of information system and database architecture than transactional systems. Analytics systems must be designed with an architecture that allows for easy, accurate, and efficient data querying.
What is Dimensional Design in Data Warehousing?
Dimensional Design is a methodology used in data warehousing to Design and organizes data in a way optimized for querying and analysis. It involves creating a data model focused on the end users' needs, with a particular emphasis on facilitating ad-hoc queries.
The basic idea behind dimensional Design is to organize data into two types of tables: fact tables and dimension tables. Data tables contain the quantitative measurements or metrics of the data, while dimension tables contain the descriptive information that provides context for the data in the fact tables.

Dimensional Design focuses on creating a flexible structure that can accommodate future changes while ensuring that the Design supports high-performance queries and reporting.
This is achieved by following a set of best practices and guidelines, such as:

Designing a data model that is understandable and usable by business users
Ensuring that the data is stored in a denormalized format that facilitates fast querying
Creating well-defined dimensions and hierarchies that provide context for the data
Designing fact tables that are additive and that contain only the measures that are needed for analysis
Creating appropriate aggregation levels to balance query performance and storage requirements
There are three important types of data warehouse architectures:
Single-Tier Architecture: This is the simplest type of data warehouse architecture where all the components, including the database server, data warehouse server, and client, are installed on a single machine. This architecture is easy to set up and maintain but may suffer performance issues.

Two-Tier Architecture: [/b]In this architecture, the data warehouse server and the client are installed on separate machines, and the client communicates directly with the data warehouse server. This architecture is more scalable than the single-tier architecture and can handle more data.

[b]Three-Tier Architecture: This is the most common type of data warehouse architecture. This architect divides the data warehouse into three layers: the database server, the application server, and the client. The client interacts with the application server, which communicates with the database server to retrieve data. This architecture is highly scalable, provides better performance, and allows for easier maintenance and upgrades.
Final Lines
In summary, analytics and transaction/operational systems have distinct design and architecture requirements. We reviewed the three primary types of data warehouse architectures and discussed how Dimensional Design is applied to each. By understanding these concepts, data scientists and business decision-makers can better communicate with data engineers and effectively utilize data warehouses to analyze business performance.