Data modeling is an essential part of data engineering. In this story, I would like to talk about different data models, the role of SQL in data transformation, and the data enrichment process. SQL is a powerful tool that helps manipulate data. With data transformation pipelines we can transform and enrich the data loaded into our data platform. We will discuss various methods of data manipulation, scheduling, and incremental table updates. To make this process efficient, we first want to know some essential things about data modeling.
What is data modeling?
TO data model aims to organize elements of your data and standardize how data elements relate to each other.
Data models Ensure data quality, semantic configurations, and consistency in naming conventions. Help design the database. conceptually and create logical connections between data elements, i.e. primary and foreign keys, tables, etc.
Good and complete data model design is crucial if you need the most reliable and cost-effective data transformation for your data platform. Ensures that data is processed without delays and unnecessary steps.
Companies use a procedure known as dimensional data modeling to process data. Fountain — Production — Analytics Tiering between schemas (data sets) enables effective data governance and ensures our data is ready for business intelligence and machine learning.
Any measurable information is stored in fact tablesthat is to say transactions, sessions, requests, etc.
Foreign keys They are used in fact tables and are connected to dimension tables. Dimension tables have descriptive data that is linked to the fact table, i.e. brand, product type/code, country, etc.
Dimensions and facts based on business requirements are being linked to the Scheme.
The two most popular types of schemes are Star and snowflake. Not to mention that these are the most frequently asked questions during data engineering job interviews (1).
