How do you compare data modeling and data engineering?

There are three primary data model types: relational, dimensional, and entity-relationship (E-R). Additionally, some less commonly used models include hierarchical, network, object-oriented, and multi-value. Each model type dictates the logical structure, determining how the data is stored, organized, and retrieved.

Data modeling is all about defining and structuring how data is stored, organized, and managed. It involves creating visual representations of the data and its relationships, which helps in designing databases that are efficient and easy to understand.

Data Engineers on the other hand, takes the artifacts produced by Data architects/data modelers as inputs and implements the model/solution that enables data to business users. Data Engineers are responsible for producing low level design docs, building ETL/ELT frameworks, efficient pipelines/workflows following the architecture  principles, Data models/standards/principles, business rules etc. They usually build ingestion pipelines (batch/streaming), enriches/curates/transform & model the data & enables it to the downstream systems. They will also be involved in building API layers/pipelines that enables data to downstream applications or build real time streaming/analytics solution.

Imagine you have an app on your phone that shows you the weather for your city. Behind the scenes, this app uses an API (think of it as a magical tool) to collect weather data from different sensors placed around the city. The API works like a weather data collector, getting information such as temperature, humidity, wind speed, and rainfall from those sensors. But, just like any magic, sometimes things can be a little messy, and the collected data might not be perfect. That's where the data engineers come in! The data engineers use their expertise to build the data pipeline which cleans up the data, making sure it's accurate and complete.

Data engineering, on the other hand, is focused on the practical application of data collection, transformation, and integration. It involves building the infrastructure and pipelines that allow data to flow from various sources to end-users and systems, ensuring it is accessible, reliable, and usable.

A data modeler needs to have a holistic view of Organisation data landscape, need to understand business requirement, data strategy, business goals and builds a blueprint which consists of ER diagrams, data standards, principles & definitions that protects data quality, makes data easy to understand and consume by the business. They are also responsible for maintaining the models as it gets incremental updates based on future requirements. Data modelers usually delivers Conceptual, Logical and Physical data models, Data definitions, standards/principles, business rules, data mapping/transformations.

Data modelers are responsible for designing and maintaining the data models, ensuring that data is logically structured and follows business rules. They create schemas, diagrams, and documentation to guide how data is stored and accessed.

Data engineers, however, are the builders and maintainers of the data infrastructure. They develop data pipelines, set up databases, manage ETL (Extract, Transform, Load) processes, and ensure data is available and performant for analysis and business use.

Data modelers need strong analytical skills, an understanding of database design principles, and proficiency with modeling tools and languages like SQL. They should also have a good grasp of the business domain to create models that meet organizational needs. Data engineers require programming skills, particularly in languages like Python, Java, or Scala. They need experience with data storage solutions, ETL tools, and big data technologies like Hadoop or Spark. Problem-solving skills and the ability to work with large datasets are also crucial.

Data modeling can be challenging due to constantly changing business requirements and the need to balance normalization for efficiency and denormalization for performance. Ensuring data integrity and consistency is also a key challenge. Data engineering faces challenges related to the scalability and reliability of data pipelines, handling diverse data sources, and maintaining data quality. Ensuring that data is processed and available in a timely manner can also be a significant hurdle.