Data Warehouses are a central repository for data that has been merged from one or more diverse sources. Data warehouses are used to support BI activities like data mining, online analytical processing (OLAP), and business reporting.

Prescriptive Analytics helps explicitly link analysis to decision making by making recommendations on what we should do or what choice we should make to achieve a certain outcome. This analytical method involves integration of numerical optimisation techniques with business rules and even financial models.

Data Marts are a subset of a data warehouse that is tailored to the needs of a particular business unit or department. Data marts often contain data important to a specific business area, such as sales, marketing, or finance.

Descriptive Analytics helps us describe what things look like or what happened in the past. It can take forms of simple aggregations or cross tabulations data.

OLAP systems typically store data in a multidimensional format, which allows users to slice and dice the data to see different relationships and trends.

Data Lakes are a centralised repository of raw data & has the ability to store structured, semi-structured and unstructured data from various sources including databases, social media and sensors. Data lakes are frequently used to store data for future study, such as machine learning and AI.

Online transaction processing (OLTP) is a type of database system used in transaction-oriented applications, such as many operational systems. “Online” refers to that such systems are expected to respond to user requests and process them in real-time.

Exploratory Data Analysis (EDA) is a statistical method that helps you understand your data by summarizing its most important features.

Structured data and unstructured data are two types of data commonly used in the field of data science. The type of data that is used depends on the specific application.