AtliQ Grands is a company that has a hotel chain in India and running their business for the last 20 years. Hotels are situated at namely Delhi, Mumbai, Hyderabad, and Bangalore and offer four types of Property (AtliQ Seasons, AtliQ Exotica, AtliQ Bay, and AtliQ Palace).These Properties offer four types of rooms (Standard, Elite, Premium, and Presidential) for their visitors. There are four channels through which they can do their bookings AtliQ Grands(own website), make your trip, Log Trip, and Tripster etc. Key Learnings :
- OLTP (Online Transactional Processing) : Real-time processing for transactions such as hotel bookings, cancellations, and payments. It’s the operational database used for day-to-day activities.
- OLAP (Online Analytical Processing) : Analyzing historical data for decision-making, involving complex queries and aggregations to gain insights.
- ETL (Extract, Transform, Load) : Processes involving extracting data from various sources (like CSV files), transforming it (cleaning, structuring), and loading it into a data warehouse.
- Data Warehouse : A centralized repository that stores data from different sources, optimized for querying and reporting.
- Data Understanding CSV Files : Understanding the data stored in CSV (Comma-Separated Values) files, which contain tabular data. Fact vs. Dimension Tables, Star vs. Snowflake Schema: Fact Table: Contains transactional data (e.g., bookings, revenue) and connects to dimension tables. Dimension Table: Holds descriptive information (e.g., hotel details, customer profiles). Star Schema: A data modeling technique where the fact table is at the center, connected to dimension tables. Snowflake Schema: An extension of the star schema, where dimension tables are further normalized.
- Data Exploration : Analyzing the data to discover patterns, trends, and anomalies. This step helps identify insights.
- Data Cleaning : Preparing the data by handling missing values, duplicates, and inconsistencies. Clean data ensures accurate analysis.
- Data Transformation : Converting data into a suitable format for analysis. This may involve aggregating, joining, or reshaping data.
- Insights Generation : Deriving actionable insights from the cleaned and transformed data. These insights guide decision-making.
AIM : This project demonstrates the power of data analytics in the hospitality industry, showcasing how insights from data can drive better decision-making and business optimization.